Future Transportation doi: 10.3390/futuretransp6040168

Authors: Haemi Shin Sungkuk Kim

This study evaluates the cost competitiveness of the Northern Sea Route (NSR) relative to the Suez Canal Route (SCR) for a representative Asia–Europe voyage, using the Shanghai–Rotterdam route as the reference case. A scenario-conditioned stochastic Monte Carlo framework is developed to estimate the probability that the NSR incremental voyage cost is lower than that of the SCR, PNSR. The analysis identifies the probabilistic threshold conditions under which the NSR can become cost-competitive under seasonal and geopolitical uncertainty. The model decomposes insurance-related costs into route-specific risk components and jointly incorporates seasonal conditions, Arctic geopolitical risk, and Red Sea war-risk exposure under five scenarios. The results show that PNSR remains below 50% in all scenarios, indicating that the NSR does not achieve cost competitiveness in the majority of simulations under the assumed cost structure. The summer-current base case yields PNSR = 16.6%, while geopolitical easing increases it to 23.5%. In contrast, the winter-current scenario reduces PNSR to 1.4%, confirming a strong seasonal constraint on NSR viability. Single-variable sensitivity analysis identifies icebreaker fees and Arctic navigation insurance premiums as the most influential NSR-side cost variables, whereas SCR war-risk exposure has the largest overall effect by increasing SCR-side costs. Further threshold analysis shows that no single variable within a plausible operating range is sufficient to raise PNSR to 50%. A combined two-variable analysis shows that simultaneous reductions in icebreaker fees and Arctic navigation premiums can bring PNSR close to 50% under less extreme individual parameter values than those required in the single-variable analysis. These findings suggest that NSR competitiveness is structurally constrained and is more likely to depend on coordinated changes in multiple cost components than on a favourable change in any single variable.

Future Transportation doi: 10.3390/futuretransp6040167

Authors: Nikola Knežević Mladenka Blagojević Aleksandar Čupić Dragan Lazarević Momčilo Dobrodolac

The rapid expansion of the express delivery sector, driven by the growth of e-commerce, has increased the importance of effective complaint management as an integral component of service quality and consumer protection. Although operational service performance and complaint handling have been widely investigated, existing studies have generally assessed these dimensions separately, leaving a gap in integrated performance evaluation. This study proposes a comprehensive decision-support framework for evaluating complaint resolution performance of express delivery operators by integrating Service Failure Indicators (SFI), Complaint Resolution Indicators (CRI), an Entropy-based Composite Quality Index (CQI), and the MARCOS multi-criteria decision-making method. The proposed framework combines objective operational and complaint-related performance indicators within a unified evaluation model. The CQI is constructed by aggregating three complaint resolution indicators using objectively determined Entropy weights, while the MARCOS method is applied to rank operators according to both operational performance and complaint resolution effectiveness. The applicability of the framework is demonstrated through a case study involving five major express delivery operators in the Republic of Serbia using regulatory operational data. The results reveal meaningful differences in operator performance and show that integrating operational service failures with complaint resolution effectiveness provides a more comprehensive evaluation than evaluating either dimension independently. The proposed framework contributes to the literature by integrating operational service quality evaluation, complaint resolution performance, composite quality measurement, and multi-criteria decision-making into a unified evaluation framework. Furthermore, it provides an objective benchmarking tool that can support regulatory oversight, managerial decision-making, and continuous quality improvement in the express delivery sector.

Future Transportation doi: 10.3390/futuretransp6040166

Authors: Lennart Luttkus Lars Mikelsons

Autonomous micromobility vehicles (AMVs) need local planning that balances task progress, safety, and pedestrian interaction quality in pedestrian-rich shared spaces. Evaluating such planners is difficult because studies vary scenarios, seeds, metrics, outputs, and aggregation rules, while single-score leaderboards hide which behaviors produce a ranking. This paper proposes a repeatable, auditable, multi-objective benchmark protocol for AMV local planning. Before comparison, it fixes the scenario set, repeated seeds, measured metrics, stored outputs, planner-interface records, and aggregation procedure. We demonstrate it with a frozen robot_sf_ll7 campaign: 47 shared-space scenarios, three evaluation seeds, and 141 scenario-seed episodes per planner in one differential-drive AMV configuration. The stress test surfaces a descriptive safety–performance separation: a Proximal Policy Optimization (PPO)-family profile reaches higher observed mean task success than a classical reciprocal-avoidance baseline, while that baseline keeps lower collision exposure. Absolute success stays low for both, with most scenarios unsolved by either. Because this learned policy was trained on a superset of the evaluation scenarios, its higher success reflects behavior on the benchmark set, not held-out generalization—an overlap the protocol records per planner rather than hiding in one score. The finding is bounded to these configured pipelines, not a universal planner-family ranking. The contribution is an auditable comparison framework tracing results from the scenario matrix and fixed seeds to episode records, aggregate reports, and manifests.

Future Transportation doi: 10.3390/futuretransp6040165

Authors: Ahmed N. Elbelacy

This study presents a field-informed simulation-based decision-support framework for improving transportation operations within the Port Said–Port Fouad bridge–ferry crossing corridor in Egypt. The investigated corridor represents an interruption-sensitive multimodal transportation system where traffic performance is strongly influenced by maritime navigation activity, bridge-closure events, ferry batch-service operations, fluctuating travel demand, and adaptive traveler behavior. The proposed framework integrates AIS-assisted operational characterization, SUMO-based microscopic traffic simulation, adaptive traveler redistribution, congestion-spillback analysis, XGBoost surrogate modeling, and multi-objective optimization within a unified analytical environment. AIS data were used to identify representative vessel-passage events and bridge-closure periods that supported field calibration of the simulation framework. The methodology explicitly represents bridge-capacity interruptions, ferry operational constraints, multimodal demand redistribution, and corridor-wide congestion dynamics. To reduce the computational burden associated with repeated simulation evaluations, an XGBoost surrogate model was developed to estimate key performance indicators, including transportation delay, vehicle accumulation, ferry waiting time, emissions, and spillback severity. The surrogate model achieved strong predictive performance with a coefficient of determination of R2 = 0.965. Model calibration and within-sample validation were conducted using operational observations collected during a six-day field campaign. The within-sample validation results demonstrated satisfactory agreement between observed and simulated conditions, with an average relative error of approximately 4.8% across major performance indicators. Comparative analyses were performed under existing-operation, rule-based, optimization-based, and adaptive-control scenarios. The results indicate that the proposed framework reduced total transportation delay by 43.8%, peak corridor-wide vehicle accumulation by 65.9%, and estimated CO2 emissions by 17.4% relative to existing operating conditions. In addition, the framework maintained stable performance under increased demand levels and prolonged bridge-interruption scenarios. Overall, the findings demonstrate the potential of simulation-informed decision support and surrogate-assisted optimization for improving operational efficiency, congestion resilience, and environmental sustainability within interruption-sensitive bridge–ferry transportation systems.

Future Transportation doi: 10.3390/futuretransp6040164

Authors: Saeed Reza Ramezanpour Nargesi Somayeh Ramezanpour Nargesi Stephen Mattingly Vivian Miller Sina Shokoohyar

This paper identifies and categorizes factors influencing discretionary lane-changing behavior. A mixed-methods approach combining qualitative and quantitative analysis is employed to structure lane-changing stimuli into clusters and determine their relative importance. Three focus groups of drivers with experience in the United States generated forty discretionary lane-changing reasons. These stimuli were subsequently evaluated through a stated-preference survey of 220 respondents. Exploratory Factor Analysis (EFA) revealed three latent categories: Hazard & Evasive, Situational & Environmental, and Operational & Efficiency factors. Quantitative assessment indicated that safety-critical stimuli, such as encountering a stopped emergency vehicle or road debris, were the highest-ranked individual factors triggering a lane change. Furthermore, preliminary cluster comparisons suggest that the “Hazard & Evasive” cluster is the most influential group of motives. This study offers a foundational step toward developing a “lane-changing propensity” scale. Rather than providing ready-to-integrate mathematical parameters, it contributes a foundational behavioral basis for future model development and validation, highlighting non-traditional driver motivations that future microscopic simulations may eventually explore.

Future Transportation doi: 10.3390/futuretransp6040163

Authors: Stavros Skarlis Andreas Nikiforiadis George Barboutidis Josep Maria Salanova Grau Georgia Ayfantopoulou

Hydrogen-powered cargo bikes are gaining increasing attention in the framework of urban logistics, thanks to their enhanced agility to travel and park in congested areas, low carbon footprint, and extended traveling range. Nevertheless, deploying a fleet of hydrogen-powered cargo bikes can be challenging, necessitating the parallel assessment of the operation of the fleet, the H2 refueling strategy, and eventually the design of the respective refueling infrastructure. The objective of this article is to demonstrate a systematic methodological framework for deriving strategic insights into the deployment of a fleet of cargo bikes equipped with a hydrogen fuel cell unit. Employing advanced longitudinal-based vehicle mathematical modeling, coupled with statistical techniques, the energy consumption of the vehicle was analyzed under a broad spectrum of operating conditions. Moreover, the refueling strategy was analyzed for different ranges of vehicle fleets, and enhanced options for designing the respective hydrogen stations were charted. Ultimately, this work provides strategic insights that could be used by cargo bike fleet operators and H2 refueling infrastructure designers.

Future Transportation doi: 10.3390/futuretransp6040162

Authors: Souksamai Thoumboulom Fumitaka Kurauchi Toshiyuki Nakamura

Dynamic freight transportation systems are critical for improving logistics performance in developing economies where multimodal integration remains limited. Despite Lao People’s Democratic Republic (Lao PDR)’s substantial investments to transition from a landlocked to a land-linked hub, quantitative assessments capturing heterogeneous cargo behavior across its multimodal networks are scarce. This study develops a cargo-specific, multi-source, multi-sink minimum-cost flow (MCF) framework to evaluate freight transport performance in Lao PDR. The framework integrates road, railway, inland waterway, and air transport into a unified directed network, capturing heterogeneous behaviors across six cargo categories. Testing under three demand scenarios (Q500, Q1000, and Q2000 tons/day) across both export and import systems, the results from 99 feasible solutions indicate that railway transport consistently dominates long-distance corridors within the assumptions of the proposed framework, reflecting its structural cost advantages. Conversely, road transport primarily serves first- and last-mile connectivity, while inland waterways and air transport serve as supplementary modes. Ultimately, these findings provide scenario-based insights to support policy decision-making for cross-border infrastructure investments, regional dry port integration, and synchronized rail-road connectivity across the Greater Mekong Subregion (GMS).

Future Transportation doi: 10.3390/futuretransp6040161

Authors: Diana Šateikienė Evelina Jurkutė

Digitalization and sustainability are increasingly important in container terminal development, yet their operational-level integration remains insufficiently explained in medium-sized terminals. This study examines how digital systems support sustainability-related outcomes in a medium-sized container terminal in the Port of Klaipėda, Lithuania. An exploratory qualitative case study was conducted using document analysis and semi-structured interviews with three key informants representing operational, planning/administrative, and technical or sustainability-related functions. Qualitative content analysis was applied to identify digital technologies, operational mechanisms, sustainability outcomes, implementation barriers, and managerial responses. The findings show that the terminal operating system, vehicle booking system, automated gate solutions, and port community system support sustainability mainly indirectly, through improved planning, information exchange, gate coordination, reduced congestion, and more efficient resource use. However, these effects are constrained by system fragmentation, limited interoperability, manual data transfer, uneven access to real-time information, and insufficiently targeted staff training. The strongest documented sustainability effects relate to LED lighting and solar energy infrastructure, while other digital-green links remain mainly qualitative or potential. The study provides exploratory, case-based evidence on how digitalization and sustainability are operationally connected in the analyzed medium-sized container terminal. Rather than proposing a new general theory, the study offers an analytical classification structure that helps distinguish documented, indirect and potential digitalization–sustainability links. The findings provide practical recommendations for system integration, staff training and sustainability performance monitoring.

Future Transportation doi: 10.3390/futuretransp6040160

Authors: Massimo Di Gangi Orlando Marco Belcore Antonio Polimeni

This paper proposes an integrated routing framework for liner shipping networks in which the routing decision concerns the movement of one or more containers from an origin to a destination, jointly addressing topological feasibility, temporal consistency, and cost–time trade-offs. The methodology combines a label-setting routing algorithm with a post-processing phase that enables multi-criteria analysis and clustering of origin–destination pairs. Within this framework, each container route explicitly accounts for service schedules, frequencies, dwell time, transshipment constraints, and port-specific handling costs, thereby ensuring the generation of temporally feasible routes over large-scale liner shipping networks. Two optimality criteria are considered for the container routing problem: time and cost. Computational experiments on a real-inspired network demonstrate the scalability of the proposed approach and highlight the difference between optimal time and cost-routing choices for containers. Further insights are obtained through clustering analyses, which reveal heterogeneous routing profiles and distinct trade-off patterns across origin–destination pairs, providing additional management insights beyond aggregate performance indicators. Overall, the proposed procedure offers a flexible and extensible tool for analyzing container movements within liner shipping services and supports advanced decision-making in maritime network design and service planning.

Future Transportation doi: 10.3390/futuretransp6040159

Authors: Jay T. Cabuñas Vince Jebryl G. Montero Alexis M. Fillone

A Micro-Analysis Zone (MAZ) for localized transport planning creates challenges in cost and logistics from large sample sizes, delaying household travel surveys. Thus, this study developed a stratified sampling framework with a lower sampling rate for MAZ based on household size as a demographic stratum. Using census data from a barangay in Davao City, various sampling rates from 20% to 1% were examined using Monte Carlo simulation with one million iterations across two stratification approaches: proportional and Neyman’s allocation. The simulations revealed that a sampling rate as low as 8% can estimate trip frequencies within 10% error (MAPE ≤ 10%) at around 95% probability. Nevertheless, there is a trade-off between these metrics, with the probability dropping to 68% to maintain an error within 5% (MAPE ≤ 5%) under both stratification approaches. Furthermore, trip generation modeling revealed that R-squared values for estimating the trip frequency have a 95% probability of being 0.7 or higher. Despite this, caution is warranted, as some secondary predictors lost statistical significance at lower sampling rates. Ultimately, this study recognizes the performance of lower sampling for a small urban barangay, relieving transport planners from challenges in conducting household travel surveys.

Future Transportation doi: 10.3390/futuretransp6040158

Authors: Ankit R. Patel Mukti Advani Areen Alsaid Katarzyna Turon

Transportation systems are fundamental to economic development, social participation, environmental sustainability, and human well-being [...]

Future Transportation doi: 10.3390/futuretransp6040157

Authors: David Álvarez-Antelo Luis Javier Miguel

The decarbonization of passenger transport remains one of the most challenging sectors for climate policy, as supply-side technological solutions alone are insufficient to meet stringent climate targets. This paper presents an extended system dynamics model of the Spanish passenger transport sector, built on the WILIAM integrated assessment framework, that for the first time endogenizes modal shift, allowing transport mode shares to emerge from the interaction of policy instruments, price signals, and infrastructure provision. Four scenarios are evaluated to 2050: a Baseline, a scenario calibrated to the Spanish National Integrated Energy and Climate Plan (GG-PNIEC), a more ambitious electrification and modal-shift scenario (GG+), and a Decent Living Standards scenario (DLS) combining technological change with deep demand reduction. Relative to 2023, direct CO2 emissions in 2050 fall by 64% under GG-PNIEC, 75% under GG+, and 84% under DLS. While GG-PNIEC and GG+ deliver substantial reductions through electrification, only DLS approaches deep decarbonization, combining rapid electrification with a reduction of over 55% in light-duty vehicle travel demand relative to the Baseline. These results indicate that technological substitution alone is insufficient, and that sufficiency-oriented demand reduction is decisive for reaching the deepest emission cuts in passenger transport.

Future Transportation doi: 10.3390/futuretransp6040156

Authors: Muhammed Yasin Codur Emre Kuskapan Emrah Yurtbas Merve Kayaci Codur

Speed bumps are commonly used as traffic calming devices to reduce vehicle speeds on urban road sections. However, their geometric design and implementation quality are critical not only for traffic safety but also for vehicle occupants’ comfort. If speed bumps are not designed in accordance with relevant standards, they may generate excessive vibration and negatively affect driver and passenger comfort. This study investigates the compliance of 10 different speed bumps with design standards and evaluates their effects on driver and passenger comfort. Field experiments were conducted using triaxial accelerometers at different vehicle speeds under two- and four-passenger loading conditions. The measured vibration data were evaluated using vertical acceleration and Overall Vibration Total Value (OVTV)-based comfort criteria. The results showed that only one of the examined speed bumps fully complied with the relevant design standards. The statistical results indicated that vehicle speed, speed bump type, passenger loading condition, and passenger position significantly influenced OVTVs. Overall, the study demonstrates that improperly designed or constructed speed bumps can substantially reduce vehicle occupant comfort. The proposed field-based evaluation approach can be used to assess existing speed bumps and support the design of safer and more ergonomic traffic calming devices.

Future Transportation doi: 10.3390/futuretransp6040155

Authors: Ajik Sulistiyo Wirawan Dony Dahana Doni Wihartika

This study examines the determinants of Generation Z’s purchase intention for environmentally friendly public transportation in Indonesia, a mode of transport that continues to experience suboptimal ridership. We investigate the effect of consumers’ awareness of green product attributes and operational processes, as well as their exposure to green promotions, on green brand sustainability and, subsequently, on green purchase intention. The study involved 400 Generation Z respondents aged 19 years or older residing in Jakarta, Indonesia. Data were analyzed using Structural Equation Modeling (SEM) with LISREL11.0. The results show that green product awareness, green process awareness, and green promotion exposure significantly influence green brand sustainability, explaining 69 percent of its variance, with green product awareness being the most dominant predictor. Furthermore, green purchase intention is significantly shaped by these antecedents and green brand sustainability, collectively explaining 84 percent of its variance. The findings demonstrate that strengthening environmental awareness across product attributes, operational processes, and promotional communication can substantially increase Generation Z’s intention to use sustainable public transportation. This study extends the research on green consumer behavior and provides strategic insights to strengthen sustainability in the public transportation sector.

Future Transportation doi: 10.3390/futuretransp6040154

Authors: Nadiia Aleksandrovska Oleksiy Melnyk Mykhailo Kosoy Oleksandr Demidiuk Oleksandr Shumylo Václav Píštěk Pavel Kučera

This paper proposes a norm-based admissibility criterion formulated in the frequency domain for evaluating whether translational and rotational motion amplitudes of a ship moored at a quay remain within operational limits prescribed by port authorities. The approach is built on a linear six-degree-of-freedom model that includes hydrodynamic added-mass and radiation-damping effects, wave excitation forces, and aerodynamic wind loads, as well as linearized reactions of mooring lines and quay fenders, including an equivalent viscous representation of hull–fender friction. Instead of explicitly inverting the full system matrix to compute the complete response, the admissibility assessment is derived from row-wise norm bounds of the frequency-domain system, yielding a computationally efficient admissibility criterion for compliance with motion limits. The criterion naturally enables a port-oriented decision index and an operational safety margin that can be evaluated for each degree of freedom and used to compare alternative mooring arrangements. Numerical verification is performed for a bulk carrier under storm wave excitation and different loading conditions, demonstrating the sensitivity of admissibility to mooring geometry and pretension. The results confirm that the proposed criterion provides a practical engineering tool for rapid go/no-go decisions regarding cargo operations and supports the selection of mooring arrangements that improve operational robustness under adverse environmental loading conditions. In addition, a Monte Carlo-based uncertainty analysis is performed to evaluate the robustness of the proposed admissibility criterion with variable mooring stiffness and damping parameters. The proposed criterion is intended as a rapid engineering screening tool to complement conventional frequency-domain response analysis.

Future Transportation doi: 10.3390/futuretransp6040153

Authors: Antonio Hurtado-Beltran Agustín Guerra Carlos Chávez-Negrete José Eleazar Arreygue-Rocha Nelio Pastor-Gómez

Most truck stops in rural areas are located along high-demand highway corridors, consistent with current methods based on capacity and demand models. However, these models underestimate the safety aspect, failing to detect highway corridors with low traffic demand and low truck stop accessibility. This study proposes a Geographic Information System (GIS)-based planning methodology to identify highway corridors affected by atypical crash rates involving commercial trucks and truck stop shortages. The proposed methodology defines two indexes to measure commercial truck safety and truck stop supply, serving as a basis for identifying four levels of need for new truck stops using reclassification and map algebra techniques. Results reveal that the identified highway corridors exhibit atypical crash rates and largely coincide with areas of low truck stop accessibility and low to moderate truck demand. Thus, the methodology introduces an important component, truck safety, in identifying new parking locations, usually dismissed by existing models.

Future Transportation doi: 10.3390/futuretransp6040152

Authors: Kittisak Makkawan Thanyaphat Muangpan

This research proposes and empirically examines a sustainability management model for Thai ports using environmental, social, and governance (ESG) indicators as the main analytical dimensions. Although ESG-based port sustainability has gained attention, limited evidence explains how ESG-based port management contributes to sustainable port management through smart port technology in the Thai port context. A quantitative survey was conducted with 115 practitioners of container terminal operators in Thailand. The questionnaire rated ESG practices, smart port technology, and sustainable port management on a seven-point scale. The measurement model was evaluated, and the structural relationships among constructs were tested using Partial Least Squares Structural Equation Modeling. The results indicate that the model has moderate to high explanatory power, with an average R2 of 0.602, and six of the seven hypotheses were supported. The environmental port management dimension significantly influenced the social port management dimension, the governance port management dimension, and smart port technology, while the governance port management dimension significantly influenced smart port technology. The path from the social port management dimension to smart port technology was not supported. Smart port technology had a strong positive effect on sustainable port management. The indirect effect results indicate that smart port technology functions as an enabling mechanism linking environmental and governance port management dimensions to sustainable port management. These findings provide an evidence-based framework to support SDG achievement and long-term competitiveness of Thai ports.

Future Transportation doi: 10.3390/futuretransp6040151

Authors: Konstantinos Kolonas Stavros T. Ponis Michalis Fragkoulakis Athanasios Vourdanos

The emergence of unmanned aerial vehicles (UAVs) introduces new opportunities for the design of intelligent and flexible transportation systems beyond traditional road-based logistics. This study investigates the integration of UAVs as an alternative transportation mode within industrial environments, focusing on the rapid delivery of critical spare parts in large-scale production facilities. A two-stage optimization framework is developed, combining demand pre-processing with a routing algorithm that determines fleet utilization and delivery schedules under operational constraints. The proposed framework utilizes a data pre-processing stage, which converts enterprise resource planning order records into delivery-ready item data, with a mixed-integer linear programming (MILP) routing model that assigns eligible spare parts to UAV trips and determines the use of a fixed fleet under payload, dimensional, service-time, and battery-related constraints. The approach is evaluated using real annual order data from a metal-industry plant, combined with simulated intra-day arrival profiles due to the absence of exact order-placement timestamps in the ERP records. The results indicate that UAV-based transportation can serve a substantial share of internal demand while achieving shorter delivery-response times for the modeled UAV layer under the simulated dispatch instances and significantly lower direct energy-related transportation costs compared with the existing pickup-based process. The results highlight the role of UAVs as a complementary transportation layer in controlled industrial networks, supporting the transition toward more responsive and intelligent future transportation systems.

Future Transportation doi: 10.3390/futuretransp6040150

Authors: Andrii Plugin Serhii Musyenko Kyrylo Kutsin Ján Dižo Serhii Panchenko Oleksii Lobiak Danylo Hadaychuk Dmytro Plugin Oleksii Dudin Nadiya Murygina Maksym Murugin

The objective of this study is to develop a physical model of a prestressed reinforced concrete railway sleeper, to construct a corresponding finite-element model, and to compare the experimental and numerical results. During operation, many of them sustain damage due to cracks. The article deals with the crack resistance of sleepers, specifically the impact of anchoring on reinforcement. Physical models of concrete sleepers were designed and fabricated; they included specimens with non-anchored or anchored reinforcement. Their finite-element models were constructed, and both computational and full-scale experiments were carried out; they also included their loading to failure. It is found that the anchoring of reinforcement does not improve the crack resistance or strength of the models, and even leads to a slight reduction in their properties. It has therefore been found that the anchoring of reinforcement does not improve the resistance of sleepers up to the formation of transverse cracks in the under-rail and mid-section areas; however, it can be considered as a technological measure, as well as a measure for protecting the ends of rebars from contact with the external environment to prevent leakage currents from flowing through them. The results obtained make it possible to design sleepers with anchored reinforcement to achieve the required crack resistance.

Future Transportation doi: 10.3390/futuretransp6040149

Authors: Alexey Fadyushin Dmitrii Zakharov Anatoly Pistsov

This study investigates the influence of spatial heterogeneity on urban transport demand forecasting. A two-stage framework combining cluster analysis and machine learning was applied to origin-destination trip data generated by a calibrated transport model of a large city. Origin-destination pairs were grouped according to travel conditions using K-Means clustering based on private transport trip length and public transport travel time. The identified clusters were subsequently analyzed with respect to travel costs, modal split, spatial distribution, and transport demand characteristics. Machine learning models were then developed to predict transport demand for public transport, private transport, and walking. Two forecasting strategies were compared: a unified model trained on the entire dataset and cluster-specific models trained separately for each identified cluster. The results revealed significant spatial heterogeneity in travel conditions and transport demand structure. Cluster-specific machine-learning models reduced prediction errors for public and private transport demand, while the magnitude of improvement varied across travel modes and evaluation metrics. The findings demonstrate that accounting for spatial heterogeneity influences transport demand prediction performance, with the greatest improvements observed for public and private transport demand, whereas the effect was less pronounced for walking demand.

Future Transportation doi: 10.3390/futuretransp6040148

Authors: Petr Nachtigall Petr Kučera Martin Šturma Tomáš Starý Jaroslav Matuška

Railway transport management has changed dramatically over the past 50 years. The advent of computer technology and the capacity for information transmission brought greater safety and the ability to remotely control interlocking devices. These enable the centralisation of railway transport management, leading to higher operational efficiency and reduced staffing costs. At the same time, this technological progress has enabled the development of additional automation functions, which we can abbreviate as ARS (Automated Route Setting). The international designation Automatic Route Setting (ARS) includes actions that enable the automation tool to execute instructions to the signal box without the intervention of operating personnel (the dispatcher). Their importance increases with line speed and the size of the remotely controlled area. Thanks to them, the dispatcher gains time because the ARS can automatically resolve some operational situations or allow the dispatcher to address them in advance, thereby distributing the workload over a wider time window. However, the interlocking system itself remains the primary safety mechanism and will prevent ARS if any element of the infrastructure is occupied. At the same time, it is not possible to automate safety-critical functions that require direct assistance from the operating personnel. In the article, the authors analysed functions in which ARS is currently widely used. In the next part, they focused on the possible expansion of the palette of these functions that could be included in the ARS regime using multi-criteria analysis. The WSA method was applied using data obtained from routine users of the system. This approach enabled the incorporation of practical operational experience into the evaluation process and provided an empirical basis for assessing and prioritising the analysed functions. The next step was a safety-critical analysis and determination of the conditions under which they could be included in the ARS regime. The safety-critical functions are left aside. It is assumed that these will still have to be performed by the operator, not by the ARS. Detailed implementations and quantification of their impacts on the dispatcher’s activities are then carried out for selected ARS functions. The analysis therefore yields a prioritised ranking of ARS functions, indicating the order in which their implementation would be most appropriate from an operational perspective. This ranking provides a systematic basis for the phased deployment of ARS functionalities, considering their expected operational benefits and practical applicability in railway traffic management. The last part of the article is a look into the future, because the development in the field of safe communication between the train and the infrastructure (V2I) and the transmission of valid information provides many new challenges not only in the field of ARS itself, but also in the optimisation of the entire process of managing and organising rail transport. If we can use the ARS functions today, it is only a matter of technical development to be able, for example, to guide trains to the exact time when a train route will be built for this train. This will also enable optimising the train’s energy consumption and tracking capacity use. The ideal state is when the infrastructure fully communicates with the train in GoA4 mode and optimises both the train’s ride and the use of the infrastructure.

Future Transportation doi: 10.3390/futuretransp6040147

Authors: Jie Shang Tun Ahmad Adlan Asma’an Jamaluddin Muhamad Nazri Borhan Fazilatulaili Ali Jianqiu Chen Ahmad Nazrul Hakimi Ibrahim

Private vehicle overreliance in Klang Valley, Malaysia contributes to severe traffic congestion, air pollution, and carbon emissions, yet public transport modal shift remains far below national policy targets. This study extends the Theory of Planned Behaviour (TPB) by incorporating two external constructs, environmental concern (EC) and technology adoption (TA), to investigate the behavioural intention of 483 private vehicle users to switch to rail transit. Hypotheses were tested using partial least squares structural equation modelling (PLS-SEM). The results show that, with the exception of subjective norm (β=0.064, p=0.180), all constructs exert significant positive effects on behavioural intention. Technology adoption emerged as the strongest direct predictor (β=0.417), followed by perceived behaviour control (β=0.340). Environmental concern operated exclusively through indirect pathways, mediated by attitude and perceived behaviour control, and exhibited the largest effect size on the TPB components (f2 = 0.380–0.449, large effect).

Future Transportation doi: 10.3390/futuretransp6040146

Authors: Erika María López-López Osnamir Elias Bru-Cordero Cristian David Correa-Álvarez

Urban road-safety agencies increasingly rely on administrative incident registries that contain many events but few fatalities. This study develops a reproducible machine-learning benchmark for rare-event road-traffic fatality prediction using an urban incident registry from Medellín, Colombia. The analysis is framed as a risk-ranking problem rather than as high-certainty binary classification, because fatal outcomes account for less than 1% of the records. The benchmark compares a prevalence-only reference, logistic regression, CART, Random Forest, and XGBoost under a shared preprocessing and time-aware validation design. Historical records are used for training and later observations are held out for testing, reducing temporal leakage and approximating prospective use. Model performance is evaluated with metrics suited to severe class imbalance, including ROC-AUC, PR-AUC, Youden-based threshold summaries, Precision@1%, bootstrap uncertainty intervals, calibration diagnostics, feature-importance analysis, and sensitivity checks. The results show that the available registry variables support risk enrichment but not high-precision fatality classification. The study contributes a transparent baseline for computational road-safety research and clarifies the limits of registry-based prediction when exposure, traffic-flow, roadway, infrastructure, weather, and post-crash response variables are not available.

Future Transportation doi: 10.3390/futuretransp6040145

Authors: Fabiha Rahman Robert Oliver Thomas Crawford Ralph Buehler Jinhyung Lee Junghwan Kim

In contemporary urban planning, the concept of the 15-min city (15-MC) is gaining significant attention. Prior studies on 15-MC often assume a constant walking speed, regardless of variations in urban topography and individual demographic characteristics. Since 15-MC access is frequently evaluated through quantified accessibility metrics in applied planning practices, such assumptions can influence the resulting estimates. This study investigated how 15-MC accessibility scores change when walking-speed assumptions are adjusted to account for topography, age, and gender in a unified framework. We selected Roanoke, a medium-sized hilly city in Virginia’s Appalachian region in the United States, as a case study to compare the conventional constant-speed approach with an alternative specification that incorporates topographic and demographic variation in walking speed. Instead of developing a new model, the study was framed as a case-based sensitivity analysis showing how accessibility estimates shift under different walking-speed assumptions across different groups in a hilly urban context. Findings indicate that while the topography-sensitive comparison reduced the number of census blocks meeting the 15-MC threshold by 1.88% across the entire region, the selected route examples demonstrate how even small aggregate differences may alter threshold-based interpretations in specific cases. This contributes to ongoing discussions of “15-MC for whom?” and emphasizes the importance of more cautious and inclusive interpretation of accessibility metrics in planning practices.

Future Transportation doi: 10.3390/futuretransp6040144

Authors: Yasmany García-Ramírez Fabián Díaz-Muñoz Xavier Merino-Vivanco

Speed management remains a central challenge in road safety, particularly in road segments where geometric design, crash concentration, or downstream stopping conditions require drivers to reduce speed. Although conventional traffic signs provide regulatory guidance, recent behavioral approaches suggest that message framing may influence driver compliance by activating different cognitive and social associated psychological constructs. However, limited evidence exists on how traditional speed-reduction signs compare with urgency-based, risk-based, and social-norm messages in shaping drivers’ behavioral intention. This study examined the perceived effectiveness of five speed-reduction messages: a standard regulatory sign, an urgency-based version, a crash-risk warning, and two social-norm variants. A within-subject survey design was applied to 326 active drivers, using seven-point Likert scales to measure behavioral intention, perceived risk, social influence, credibility, and clarity. Descriptive comparisons showed that the urgency message obtained the highest behavioral intention score, followed by the standard regulatory and risk-warning messages, whereas both social-norm messages showed lower means and greater dispersion. A latent structural equation model showed good fit and indicated that stated behavioral intention was primarily associated with perceived risk and message credibility, whereas social influence and clarity did not add significant explanatory value once these appraisal constructs were considered. This pattern suggests that drivers’ stated intention to reduce speed is shaped less by social conformity or basic message comprehension and more by whether the sign is perceived as risk-relevant and credible. Field and simulator studies are still needed to determine whether these stated-intention patterns translate into observable speed reduction.

Future Transportation doi: 10.3390/futuretransp6040143

Authors: Simao Alice Chen

Past Citi Bike rebalancing research has relied on optimization and geospatial models but has treated spatial and temporal structures separately, leaving a gap in understanding stations as long-term behavioral entities. This study exploits the frequent spatiotemporal structure in Citi Bike daily trip data and treats the station’s bike net flow rate (NFR) time-series as the study object. Stations are grouped into regimes using time-series clustering, cluster stability, and the spatial context surrounding each station. Stations were assigned operational roles based on their hourly NFRs and potential contribution to the rebalancing truck. A priority-queue-based heuristic routing (PQHR) algorithm is introduced to design a single-vehicle route that accounts for stations’ regimes, roles, rebalancing urgency, and priority during rush hours. Therefore, this study formally introduces the Station Behavior Regime Model (SBRM) that defines station regimes, rebalancing roles, and routing. The result achieved a >90% reduction in the number of stations with extreme bike accumulation or unavailability and reduced the NFR of affected stations by >30% in busy areas. The spatial context derived from station behavior modes suggests new ways to define neighborhood boundaries. The methodologies provide new avenues for rebalancing operations and routing plans across a broad range of station-centric transportation network studies.

Future Transportation doi: 10.3390/futuretransp6040142

Authors: Ehitayhu Hagos Tom Brijs Kris Brijs Geert Wets Bikila Teklu Teferi Abegaz

Road traffic crashes remain a major global public health and economic challenge, with heavy vehicle drivers disproportionately involved in severe incidents, particularly in low- and middle-income countries. In Ethiopia, limited access to continuous professional training constrains efforts to improve drivers’ safety-related knowledge and awareness. This study explored the impact potential and user acceptance of gamified e-learning modules designed to enhance heavy vehicle drivers’ knowledge and awareness of fatigue management, speed-related behavior, and eco-driving practices. A randomized pretest–post-test control-group design was employed, in which professional drivers were assigned to either an intervention group that completed three gamified e-learning modules or a control group that received no training. Data were analyzed using mixed repeated-measures analysis of variance. The results revealed significant time × group interaction effects across all domains (p < 0.001), with substantially greater improvements in the intervention group and large effect sizes. Participants also reported high perceived usefulness, behavioral intention, and trust in the system. These findings provide preliminary evidence that gamified e-learning may be a feasible and promising approach for improving short-term safety-related knowledge among professional heavy vehicle drivers. Further research is needed to determine whether these improvements are sustained over time and translate into behavioral change and measurable road safety outcomes before broader implementation can be recommended.

Future Transportation doi: 10.3390/futuretransp6040141

Authors: Hongbin Zhang Haoyu Zhou Cheng Fan Wutao Li Lishun Ma

Severe environmental pollution and complex weather changes significantly hinder the effectiveness of vehicle traffic flow statistics and traffic monitoring technologies. Therefore, achieving fast and accurate vehicle detection in complex environments has become one of the key tasks in the new era. This paper proposes a vehicle detection method for complex environments based on YOLOv11, named YOLO-FTG. First, the neck network of the YOLOv11 baseline model is improved by adding a P2 detection layer and the corresponding detection head. Second, a Spatial-Frequency Hybrid Convolution (SFHC) module is designed. Third, a Global-Local Adaptive Module (GLAM) is proposed. To verify the effectiveness of the proposed model, experiments were conducted on three self-constructed datasets and the public BDD100K dataset. The experimental results demonstrate that compared with existing methods, the YOLO-FTG model achieves higher accuracy, with mAP50 scores of 75.3%, 98.71%, 51.00%, and 64.59% on the four datasets, respectively. These scores represent improvements of 3.24%, 0.34%, 5.64%, and 3.51% over the baseline model, respectively. While maintaining real-time inference speed, these results indicate the effectiveness and robustness of the proposed model in complex environments.

Future Transportation doi: 10.3390/futuretransp6040140

Authors: Amjad Ali Larry Liu Blessing Ojeme Chidozie Anadozie Fremah Agyemang Satish Ukkusuri Eunhan Ka Shagun Mittal

The rapid digitalization of transportation systems has expanded the cyberattack surface of critical infrastructure while exposing significant shortages in transportation-specific cybersecurity workforce capacity. Existing workforce studies rarely integrate transportation-sector workforce development, organizational preparedness, operational technology (OT) challenges, and interdisciplinary collaboration within a unified analytical framework. In response to this gap, this study employs a mixed-methods approach that integrates Term Frequency–Inverse Document Frequency (TF-IDF) text analysis of publicly available workforce development documents, qualitative interviews with academics, policymakers, and transportation cybersecurity professionals, and organizational survey analysis to examine workforce development challenges across transportation subsectors. The findings reveal persistent information technology (IT) and operational technology (OT) competency gaps, curriculum–industry misalignment, organizational staffing shortages, disparities in cybersecurity preparedness, and the critical importance of experiential learning and dedicated cybersecurity staffing. The study further emphasizes the need for interdisciplinary education, organizational capacity building, and sustained collaboration among academia, government, and industry to strengthen the transportation cybersecurity workforce’s readiness and resilience. Collectively, the findings position transportation cybersecurity workforce development as a systems-coordination challenge that requires long-term strategic investment, cross-sector collaboration, and workforce innovation to enhance the security, resilience, and operational continuity of transportation systems.

Future Transportation doi: 10.3390/futuretransp6040139

Authors: John Omwamba Chiara Ricchetti Lucia Rotaris Giovanni Longo

Grocery shopping remains a heavily car-dependent activity in urban areas, even for short-distance trips within residential neighbourhoods. A primary barrier to shifting toward active travel (walking or cycling) is the physical burden of carrying heavy or bulky goods. This study investigates whether a retailer-managed home delivery service could encourage consumers who currently rely on motorised modes for grocery shopping to shift towards active travel while preserving the in-store shopping experience. The analysis focuses on urban Italian consumers who currently use motorised modes for grocery shopping. Using a Stated Preference (SP) experiment and a Mixed Logit (MMNL) model (n = 88), we analyse the conditions under which such a service may encourage the adoption of active travel modes and support proximity-based shopping patterns. Given the exploratory nature of the study and the small, non-representative sample, the findings should be interpreted as preliminary evidence for urban motorised grocery shoppers rather than as representative of the Italian population. The results indicate a substantial willingness among respondents to adopt the proposed service configuration. Delivery time, service cost, and the availability of delivery time-window selection emerge as critical factors influencing consumers’ choices. Acceptance of the service is also influenced by perceptions of walking and cycling infrastructure quality, trust in the integrity of delivered groceries, preferences for local products, and concerns regarding the working conditions of delivery personnel. Additionally, the model reveals significant heterogeneity in preferences regarding delivery by drone/autonomous vehicle and a 100% reduction in greenhouse gas emissions relative to conventional motorised transport. Younger respondents exhibit a more favourable attitude towards automated delivery technologies, while differences in the valuation of environmental benefits emerge between male and female respondents. The findings suggest that retailer-managed home delivery may represent a promising mechanism for encouraging active travel among current motorised grocery shoppers, while maintaining consumers’ relationship with neighbourhood retail services. These results provide retailers and urban policymakers with valuable insights, suggesting that appropriately designed delivery services may support more sustainable and proximity-oriented shopping behaviours. Such services could potentially contribute to maintaining the accessibility and vitality of neighbourhood retail activities, particularly in ageing urban contexts.

Future Transportation doi: 10.3390/futuretransp6040138

Authors: Bulut Ozan Ceylan Oğuzhan Der Arif Savaş

The safe and uninterrupted operation of the ship’s main engine is critical for maritime transportation. The shutdown mechanism, part of the main engine protection systems, prevents serious damage by automatically stopping the engine in critical situations such as low lubrication oil pressure, overspeed, high bearing temperature, and cooling system failures. However, identifying the faults that trigger the shutdown system and evaluating their risk levels is crucial for improving system reliability. In this study, shutdown events that may occur in a two-stroke low-speed marine diesel main engine were investigated using Fuzzy Fault Tree Analysis (FFTA). The shutdown event was defined as the peak event, and a total of 34 baseline events were modelled under five main branches: low lubrication oil pressure, overspeed, high thrust bearing temperature, abnormal jacket coolant inlet condition, and crankcase/cylinder oil mist formation. Fuzzy assessments based on expert opinions were defuzzified and converted into probability values and used in fault tree calculations. The results showed that the shutdown risk is largely affected by failures originating from the jacket coolant system and the lubrication oil system. Specifically, lubrication oil filter clogging and contamination/blockage in the coolant line were identified as the most critical risk factors. The findings significantly contribute to prioritizing maintenance and condition-monitoring activities aimed at improving the ship’s main engine reliability through a risk-based approach.

Future Transportation doi: 10.3390/futuretransp6040137

Authors: Elvira Maeso-González María Isabel Olmo-Sánchez Jesús González-Feliu

Digitalization is transforming urban mobility and logistics, changing behaviors and the way demand is anticipated and managed. This paper frames both research and practice in this area. Through a systematic review of reviews in Scopus and Web of Science, following PRISMA 2020, a corpus of 21 documents was compiled. The analysis organizes findings into five interrelated dimensions: technology; operation and service design; society, user, and equity; institution, regulation, and governance; and economics and scalability. These are interpreted through two cross-cutting axes: behavioral change among users and operators, and the anticipation and management of demand supported by big data and predictive models. By integrating urban mobility and logistics, usually analyzed separately, this study compares barriers and enablers. The two axes help identify gaps: limited coverage of medium-sized cities, low-density environments, and developing countries; fragmented treatment of user diversity; weak integration between mobility and logistics data; and the lag between the sophistication of predictive models and the institutional capacity to incorporate them into planning. This paper proposes an integrative framework for analyzing the digitalization of urban mobility and logistics as part of a single urban transition.

Future Transportation doi: 10.3390/futuretransp6040136

Authors: Riccardo Ceriani Leonardo Cameli Margherita Pazzini Valeria Vignali Claudio Lantieri

The forecasting of road surface conditions is a pivotal component for intelligent transportation systems, in terms of supporting maintenance planning, safety and mobility management. The increasing availability of large-scale monitoring data, collected from passenger vehicle fleets, enables the development of data-driven forecasting approaches. However, systematic comparisons between classical time-series models and machine-learning methods in this context remain limited. The proposed benchmarking framework evaluates direct road surface roughness forecasts at 1-, 7-, 14-, 30-, and 90-day horizons using multi-year vehicle-derived data collected across heterogeneous road segments. Daily roughness indicators are derived from raw measurements and modeled following a consistent, segment-wise experimental protocol. The proposed analysis involves the evaluation of multiple machine-learning regressors including Ridge, Random Forest and Gradient Boosting which are trained on lagged observations and rolling statistics. Performance of the models is assessed using two error metrics: unweighted and uncertainty-aware weighted. Findings indicate significant variations in predictive accuracy and robustness across models and segments, emphasizing the influence of feature-based learning strategies and data-quality weighting. The research provides a scalable and transparent methodology for evaluating forecasting models on vehicle-based road monitoring data, contributing practical guidance for the deployment of artificial intelligence in Intelligent Transport Systems (ITSs).

Future Transportation doi: 10.3390/futuretransp6040135

Authors: Tingquan He Yibo Dai Zhongbin Luo Shanfeng Lu Sen Luan

To mitigate driving risks from brake failure on long and steep downhill sections, this study designs three deployment schemes for radar–video fusion devices: a baseline scenario with no coverage, a scenario with partial coverage in high-risk areas, and a scenario with full coverage. Corresponding information service strategies are delivered via Human–Machine Interfaces (HMIs), forming an integrated active prevention and control framework from risk perception to preventive action. Driving simulation experiments focusing on the car-following process were conducted to collect vehicle operational data and extract characteristic indicators based on the Wiedemann model. A Generalized Linear Mixed Model was employed to comprehensively examine the effects of HMIs on car-following behavior to identify the optimal active prevention strategy. Results show that drivers exhibit greater caution under the partial coverage scheme, with time headway increasing by 47.63% compared to the scheme with no radar–video fusion devices to ensure safety. Under full coverage conditions, drivers can obtain real-time information about the leading vehicle’s status and the distance between the two vehicles in key risk sections. Drivers choose to follow the leading vehicle, balancing both safety in car-following and efficiency on long and steep downhill sections. As the level of accompanying services improves, drivers engage in self-regulation to avoid rear-end collisions. Particularly under the scheme with full coverage of radar–video fusion devices, the standing distance significantly increases by 219.37% compared to the partial coverage condition. Drivers demonstrate optimal vehicle control capabilities. Furthermore, there is an interaction effect between the accompanying service strategy and drivers’ attributes on car-following behaviors. Under different schemes, more experienced drivers exhibit a certain degree of aggressiveness, providing a basis for the targeted design of information services for different types of drivers. The findings support the deployment and application of risk perception and prevention devices on long and steep downhill sections, which can effectively enhance the comprehensive safety of such special roads in the connected vehicle environment.

Future Transportation doi: 10.3390/futuretransp6040134

Authors: Muhammad Farhan Henning Koehler

Modern route planners such as Google Maps and Apple Maps serve millions of users worldwide, optimizing routes in large-scale road networks where fast responses are required for diverse cost metrics including travel time, fuel consumption, and toll costs. Classical algorithms like Dijkstra or A* are too slow at this scale, and while index-based techniques achieve fast queries, they are often tied to fixed metrics, making them unsuitable for dynamic conditions or user-specific metrics. Customizable approaches address this limitation by separating metric-independent preprocessing and metric-dependent customization, but they remain limited by slower query performance. We recently introduced Customizable Tree Labeling (CTL) as a framework that combines tree labelings with shortcut graphs. The shortcut graph enables efficient customization to different cost metrics, while tree labeling, supported by path arrays, provides fast query answering. Although CTL enables optimizing routes with different cost metrics, it still faces challenges in storing and reconstructing path information efficiently, which hinders its scalability for answering millions of queries. In this article, we build on the CTL framework by developing several algorithmic variants that differ in the information retained within shortcut graphs and path arrays, offering a spectrum of trade-offs between memory usage and query performance. To further enhance scalability, we propose a batch processing strategy that shares path information across queries to eliminate redundant computation. We empirically evaluated the performance of our algorithms on 13 real-world road networks. The results show that they significantly outperform state-of-the-art methods, achieving speedups of up to factor 15 for route computation while maintaining practical memory requirements.

Future Transportation doi: 10.3390/futuretransp6040133

Authors: Francesco Cuccaro Rodrigo Tapia Valerio Gatta Edoardo Marcucci

Mobility hubs promise to reduce car dependence and make multimodal travel work in practice, yet behavioural evidence remains limited when hub improvements coexist with easier car access. This article examines the tension at Rome Trastevere, an urban rail node that gradually acquires mobility-hub functions while facing improved parking access near Piazza della Radio. The empirical analysis combines a pilot survey of 83 users with an on-site stated preference survey of 204 valid respondents. The stated preference instrument uses a route-based feasible-choice design with nine choice sets per experiment: respondents evaluate alternatives among bikes, walking, e-scooters, e-mopeds, public transport, private cars, and shared cars under variations in travel time, travel cost, and search time. The paper estimates a multinomial logit model in Apollo and uses sample enumeration, supported by Monte Carlo simulation, to assess four parking and shared-mobility scenarios and produce confidence intervals around predicted probabilities. Results show that users respond to time, monetary cost, and search friction in coherent and policy-relevant ways. Setting the car parking search time to zero increases predicted car probability only marginally, by about 0.9% relative to the baseline. By contrast, a EUR 1/h increase in parking cost reduces predicted car probability by about 14.7%, while a EUR 1.5/h increase reduces it by about 22.4%. A coordinated scenario combining higher parking cost and lower shared-mode search time produces the lowest predicted car probability and strengthens e-scooter and e-moped alternatives, while public transport remains the dominant option. Findings indicate that parking pricing steers behaviour more clearly than parking convenience destabilizes it in the tested range. The paper shows that mobility-hub performance depends on coordinated access management, including parking regulation, shared-service reliability, and legible multimodal transfer.

Future Transportation doi: 10.3390/futuretransp6030132

Authors: Luís M. B. C. Campos Joaquim M. G. Marques Pedro A. Serrão

The aim of this paper is to give an overview of emerging technologies for the greening of aviation, how they can be applied to different classes of aircraft, and the challenges to be overcome in achieving efficiency and environmental objectives. The following steps are part of the journey towards the greening of aviation: (i) developing and maturing new technologies, including electrification and sustainable fuels; (ii) where possible, using new technologies in the current fleet to maximize short-term benefits—i.e., EU Fit for 55; (iii) when it is not possible to retrofit new technologies to current aircraft, incorporating them into new next-generation aircraft designs from 2035; and (iv) replacing existing fleets with new, cleaner aircraft to meet the ICAO Net Zero 2050 goal. These technologies of prime importance will have to be supplemented by operational, regulatory, and economic enablers to support wide deployment. There will not be one solution that meets the requirements of all aircraft classes or mission profiles, but rather a combination of electrification, hydrogen propulsion, and sustainable aviation fuels will be required. Achievement of aviation’s environmental goals will hence not solely be a function of technological progress but also certification pathways, investment in infrastructure, and integrated policy strategies.

Future Transportation doi: 10.3390/futuretransp6030131

Authors: Kamil Faisal Wai Yeung Yan Wenzheng Fan Man Ho Kwan Mohammed Alamoudi Alaa Sindi Yasser Qaffas

As the Kingdom of Saudi Arabia (KSA) accelerates its transition toward smart mobility under Vision 2030, establishing a robust digital infrastructure is paramount for the safe deployment of autonomous vehicles (AVs). High-definition (HD) maps serve as a critical foundation for this infrastructure, yet their deployment is severely bottlenecked by extreme operational costs, massive data processing payloads, and rapid environmental variations across vast highway networks. To address these challenges, this paper proposes a comprehensive, localized national strategy structured around three key tasks. First, it establishes a unified national HD map standard to guarantee seamless interoperability and data sharing among competing AV manufacturers and government transport authorities. Second, it implements an AI-powered baseline workflow using Mobile Mapping Systems (MMS) for high-fidelity static map construction, anchored and validated within designated pilot zones, including the King Abdulaziz University campus and key sectors in the Kingdom. Third, it deploys a decentralized, vision-based crowdsourcing system that leverages active public and commercial vehicle fleets for real-time map maintenance. By integrating a sovereign edge-cloud AI infrastructure that respects local Personal Data Protection Law (PDPL), this framework bridges the gap between high-accuracy baseline mapping and long-term economic sustainability, offering an actionable technical roadmap for scaling a resilient digital transport layer across the Kingdom.

Future Transportation doi: 10.3390/futuretransp6030130

Authors: Nicoletta Matera Ludovica Grasso Michela Longo Wahiba Yaïci

Decarbonizing heavy-duty road transport requires comparing zero-emission options to guide infrastructure investments along strategic corridors. This study develops a scenario-based techno-economic model to evaluate hydrogen fuel cell trucks (HFCTs) and battery electric trucks supported by dynamic wireless power transfer (DWPT) on a 100 km segment of Italy’s A4 motorway in 2030 and 2050 scenarios. The framework integrates traffic flows, vehicle archetypes, infrastructure sizing, and end-to-end energy chains (power-to-hydrogen-to-wheel for hydrogen and grid-to-wheel for WPT) to estimate capital and operating costs, efficiencies, and energy demand. Results show that hydrogen refueling infrastructure requires lower initial investment (approximately €60 million CAPEX and €20 million annual OPEX) than wireless charging systems (€80 million CAPEX and €15 million OPEX). However, WPT achieves significantly higher grid-to-wheel efficiency (96% vs. 62%) and lower per-vehicle energy demand (18 MWh/year vs. 25 MWh/year). These findings highlight a fundamental trade-off: hydrogen solutions offer operational flexibility and are better suited to long-haul or low-density contexts, while WPT systems are more efficient and become increasingly competitive in high-traffic corridors with high infrastructure utilization. Overall, the results suggest that no single technology universally dominates and that optimal deployment depends on traffic density, infrastructure usage, and system integration. A combined implementation of hydrogen and wireless charging technologies may provide the most effective pathway to balance efficiency, flexibility, and cost in future heavy-duty transport systems.

Future Transportation doi: 10.3390/futuretransp6030129

Authors: Nicharuch Panjaphothiwat Diane Gyi Andrew Morris

Advanced Driver Assistance Systems (ADAS) have significant potential to improve road safety. However, drivers’ perceptions and acceptance of these systems in Thailand have not been explored. This study investigated Thai drivers’ perceptions towards ADAS and examined factors associated with trust and intention to use. A cross-sectional survey was conducted with 849 licenced drivers. The questionnaire measured perceived usefulness, perceived ease of use, trust, barriers and concerns, expectations and preferences, and intention to use ADAS. Data were analyzed using Mann–Whitney U tests, Spearman’s rank correlations, and multiple linear regression. Results indicated that Thai drivers reported positive perceptions of ADAS regarding perceived usefulness, expectations, preferences, and intention to use. Trust was most strongly associated with constructs such as perceived usefulness, perceived ease of use, and intention to use. Multiple regression identified perceived usefulness, trust, and expectations and preferences as significant positive predictors of intention to use ADAS, whereas barriers and concerns were negatively associated with intention to use. Perceived ease of use was not a significant predictor. These findings highlight the importance of perceived usefulness, trust, and user expectations in shaping intention to use ADAS and support the need for new policies regarding driver education and awareness initiatives in Thailand.

Future Transportation doi: 10.3390/futuretransp6030128

Authors: Sara Avila Forcada Isaac Medina Martinez

Medium-sized cities in the Global South are at the center of future urban growth, yet their transportation systems remain dominated by car-dependent trajectories. This paper examines how urban infrastructure shapes inclusive access to active commuting using a latent class model across three Mexican cities. We identify two distinct commuter environments defined by infrastructure quality. In low-infrastructure settings, active commuting is concentrated among younger men, consistent with existing literature. In contrast, in high-infrastructure environments, the baseline probability of active commuting is nearly three times higher, so that women and older individuals commute actively at substantially higher absolute rates even though demographic penalties remain present in both environments. Attitudinal variables, often emphasized in policy discourse, are not significant predictors of mode choice. These findings suggest that infrastructure investment is not only a tool for increasing active commuting rates but also a mechanism for expanding mobility access across demographic groups. For rapidly growing medium-sized cities, prioritizing non-motorized infrastructure can play a central role in building inclusive, low-carbon transportation systems.

Future Transportation doi: 10.3390/futuretransp6030127

Authors: Liang Ma Yuanli Bao

In heavy-haul railway systems, effective empty railcar distribution (ERD) can optimize composition planning and meet empty railcar requirements (ERRs) at all loading ends, thereby improving the efficiency of train operations. To solve practical challenges such as the imbalanced supply–demand of empty trains, redundant loading and unloading cycles, and prolonged waiting times, this study establishes a multi-objective and 0–1 integer programming model for ERD at the loading end of a heavy-haul railway. The model can simultaneously maximize the fulfilment of all ERRs, minimize the ERD delay time, and reduce the waiting time in the heavy-train combination problem under complex constraints, including the passing capacity of sections, combination capacity of stations, and ERR at the loading end. While traditional optimization methods such as mathematical programming or heuristic algorithms partially address these issues, they are ineffective under dynamic constraints and state-space explosion. Furthermore, traditional reinforcement learning-based methods, such as Q-learning, exhibit limitations in railway scheduling due to the state-space explosion problem and inadequate model generalization. To overcome these limitations, this study proposes an innovative framework; the ERD at the loading end of the heavy-haul railway is formalized as a Markov decision process and optimized using deep Q-network (DQN) reinforcement learning. In addition, this study proposes an experience data fusion mechanism that integrates the empirical rules of the dispatchers through a modular architecture, achieving real-time constraint compliance while maintaining scalability for practical implementation. The NSGA-II genetic algorithm for multi-objective problems is used in this study to evaluate the performance of the DQN algorithm. The experimental results demonstrate that the DQN algorithm can fully meet ERRs with zero delay and produce optimal schemes for train combinations. Meanwhile, NSGA-II presents superior performance in minimizing the combination waiting time and same-destination train combinations. Meanwhile, the DQN algorithm can identify superior ERD strategies in the expanded-action and state spaces, enabling the effective handling of complex constraint-based ERD.

Future Transportation doi: 10.3390/futuretransp6030126

Authors: Saida Fellah Charif Mabrouki

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Future Transportation doi: 10.3390/futuretransp6030125

Authors: Tomoo Noguchi

Empty-vehicle repositioning is a persistent challenge in long-haul road freight because carriers must reduce empty mileage without sacrificing service reliability under lead times, appointment windows, and uncertain load realization. This paper formulates empty-vehicle repositioning on freight corridors as a stochastic control problem with explicit space–time feasibility and a stated within-epoch event order. Lead times couple current dispatch decisions to future capacity, pickup windows impose reachability constraints, and stochastic match feasibility captures information and market frictions. We develop dynamic lower bounds from time-expanded relaxations, showing that dual prices of inventory-balance constraints can be interpreted as space–time scarcity values. We further introduce an order-dependent nested friction decomposition that separates excess empty movement into spatial imbalance, temporal mismatch induced by lead times and time windows, and information frictions. Guided by this structure, we propose price-guided rolling-horizon and generalized-cost policies and evaluate them on synthetic corridor experiments organized around the three friction families. The results reveal service–empty-mileage trade-offs, a pronounced knee in the Pareto frontier, lower service loss under widened tight pickup windows, and strong sensitivity to match feasibility. The PG-RH policy reduces empty-distance exposure and total cost relative to static balancing in the main scenarios while maintaining comparable, but not uniformly dominant, service performance. The framework provides a diagnostic basis for identifying the sources of deadhead and for designing operational interventions that reduce empty mileage without undermining reliability.

Future Transportation doi: 10.3390/futuretransp6030124

Authors: Viktoria Petkani Dimitris Tzanis Evangelos Mitsakis Evangelos Mintsis Eleni I. Vlahogianni

Effective traffic management relies on the availability of high-quality traffic data to support real-time decision-making for optimizing traffic flow, enhancing safety, and reducing environmental impacts. This study aims to address the lack of integrated and operational approaches for traffic data quality management by proposing a scalable and adaptable framework for the systematic assessment and enhancement of traffic data. The framework consists of four interconnected layers, including data ingestion, data quality assessment, data imputation and correction, and a real-time alerting mechanism. Its applicability is demonstrated through a real-world case study on traffic signal control plan selection, using sensitivity and simulation-based analyses in SUMO. The results indicate that degraded data quality, particularly due to missing or invalid records, can significantly affect system behavior, leading to suboptimal decisions and reduced traffic performance. These findings highlight the importance of continuous and systematic data quality monitoring as a critical component for reliable and efficient traffic management systems.

Future Transportation doi: 10.3390/futuretransp6030123

Authors: Mia Poledica Nataša Moreti

Railway transport is increasingly recognized as a key pillar of sustainable mobility, offering a low-carbon and energy-efficient alternative to road and air transport and playing a critical role in achieving climate objectives, regional connectivity, and sustainable tourism development. Despite extensive research on service quality, sustainability, and tourism, their interrelationship within the railway sector remains insufficiently explored. This study aims to systematically analyze the intersection of quality management systems (QMS), sustainability, and tourism in passenger railway transport and to identify structural gaps that hinder their integration. A systematic literature review was conducted following the PRISMA methodology, resulting in a final sample of 37 studies. The findings reveal a significant research gap, particularly the absence of integrated and empirically supported QMS frameworks linking passenger satisfaction with sustainability and tourism objectives. Quality-management-oriented constructs appear in 48.6% of the analyzed studies, sustainability in 32.4%, and tourism in 24.3%, while none demonstrate full integration of all three dimensions. The study contributes by providing a conceptual basis for future research on the integration of operational quality management, environmental performance, and passenger-oriented service quality in railway systems.

Future Transportation doi: 10.3390/futuretransp6030122

Authors: İrşad Bayırhan

Carbon-neutral ship technologies not only protect the environment but also ensure the maritime sector’s future competitiveness and compliance with international regulations. Therefore, while the transition to carbon-neutral solutions in both port investments and ship technologies is an indispensable part of sustainable maritime transport, some safety risks remain uncertain. This study examines the safety aspects of carbon-neutral ship technologies (hydrogen, ammonia, methanol, battery systems, and other alternative fuels) and demonstrates how risks can be managed within the ALARP (As Low As Reasonably Practicable) framework. For this purpose, a risk matrix was created in the study using probability and severity values, an ALARP classification was made, and FMECA/HAZOP (Failure Mode, Effects, and Criticality Analysis/Hazard and Operability Study) summaries were prepared for critical risks. Subsequently, reasonable and practicable mitigation options were presented for each risk, covering technical, operational, and human factor dimensions. Analyses show that hydrogen poses an explosion risk, ammonia has toxicity and environmental impacts, methanol poses an invisible flame risk, and thermal runaway levels in battery systems are unacceptable. Other fuels (biofuels, LNG derivatives (blue fuels, bio-LNG), synthetic gases, and electro-fuels) offer opportunities in terms of sustainability and infrastructure compatibility but also carry some fundamental risks along with limitations in production capacity. Engineering solutions, operational measures, and human factor practices play a critical role in mitigating all these risks. The widespread adoption of carbon-neutral ship technologies is a process that requires a systematic approach not only to environmental sustainability but also to safety.

Future Transportation doi: 10.3390/futuretransp6030121

Authors: Mahmudul Haque Jamil Mostafa A. Elseifi Md Afif Rahman Chowdhury

Electric scooters (e-scooters) have rapidly integrated into university transportation networks; however, there is limited empirical understanding of users’ and non-users’ perceptions, which is essential for developing effective and inclusive policies. This study addresses this gap by analyzing the differential perceptions of e-scooter adoption, safety, and policy preferences at Louisiana State University (LSU). A quantitative, cross-sectional survey was administered to 1036 respondents (592 users and 444 non-users). Statistical analyses, including Chi-square tests and Binary Logistic Regression, were used to identify key perceptual differences and behavioral predictors of e-scooter usage. Results show that users were predominantly male undergraduates, with speed (90%) and convenience (61%) as the primary motivators. Users were over 12 times more likely to perceive e-scooters as safer than walking. In contrast, non-users cited frequent scooter misplacement (84%) as their top barrier to adoption. Logistic regression confirmed that concern about misplacement (Odds Ratio = 0.076) and support for restrictive policies were strong negative predictors of use, while belief in safety and low cost were positive predictors. These findings may help inform campus micromobility policy discussions. The strong negative perceptions associated with scooter misplacement suggest that designated parking hubs and geofencing strategies could help improve campus operations and pedestrian accessibility. In addition, because safety perception was identified as an important predictor of e-scooter use, targeted safety awareness and educational initiatives may help improve rider behavior and address perceived operational safety concerns. This strategy ensures a balance between user adoption incentives and the safety/accessibility needs of the entire university community.

Future Transportation doi: 10.3390/futuretransp6030120

Authors: Sujin Jung Yongjohn Shin

This study examines which onboard human functions remain essential for crewed ships operating under hybrid autonomous operations with Remote Operation Center (ROC) supervision. As maritime operations transition toward higher levels of autonomy, a critical challenge lies in determining the functional boundary between onboard crews and shore-based control systems. A three-round Delphi method was conducted with 20 maritime experts from five stakeholder domains to identify and validate essential onboard functions. The analysis adopts a function-based perspective, distinguishing core functional responsibilities rather than traditional occupational roles. The Delphi analysis resulted in the validation of four primary onboard function groups: Management, Operation and Control, Maintenance and Recovery, and Automation/ICT/Network. All four groups satisfied the predefined importance and stability criteria in Round 2, with mean importance scores ranging from 4.35 to 4.70 and coefficients of variation ranging from 0.09 to 0.12. In Round 3, all function groups also exceeded the minimum CVR threshold of 0.42, with CVR values ranging from 0.70 to 1.00. Operation and Control showed the highest mean importance score (4.70) and CVR value (1.00), indicating the strongest expert agreement regarding its essentiality under hybrid autonomous operations. These results demonstrate that onboard decision-making authority, manual or override capability, technical recovery, and automation-related system supervision remain non-substitutable despite ROC support. The findings provide quantitative evidence for defining minimum onboard functional requirements and offer a structured basis for future discussions on manning, training, onboard–ROC role allocation, and regulatory frameworks for Maritime Autonomous Surface Ships (MASS). This study contributes to clarifying the functional architecture of hybrid autonomous ship operations and supports safer and more accountable human–automation integration strategies.

Future Transportation doi: 10.3390/futuretransp6030119

Authors: Gianpaolo Ghiani Emanuele Manni Valentino Moretto Sandra De Iaco Monica Palma Gianluca Romano

Real-time navigation systems are increasingly used to provide optimal driving routes together with accurate travel time predictions that reflect dynamic urban traffic conditions. Recent advances have focused on integrating structured traffic data from traditional APIs with unstructured, context-rich information extracted via semantic crawling of news websites and social media platforms. This survey reviews state-of-the-art approaches that combine these heterogeneous data sources to improve route planning and travel time estimation, with special attention to the challenges posed by incident detection, event extraction, and multimodal data fusion. We discuss core methodologies including natural language processing techniques for event recognition, machine learning models for traffic prediction, and graph-based routing algorithms, highlighting their advantages and limitations. Finally, we outline open research directions for building context-aware navigation systems able to adapt to real urban mobility conditions.

Future Transportation doi: 10.3390/futuretransp6030118

Authors: Amit Kinjawadekar Nandineni Rama Devi Shantharam Patil

Transit accessibility is a critical determinant of urban equity (SDG-11) in the Global South, a term referring to emerging economies characterised by rapid urbanisation and significant infrastructure deficits. A significant ‘compliance–resilience gap’ persists in intermediate Indian cities. This study evaluates a 10.2 km primary transit corridor in Udupi, auditing 42 transit interfaces across 21 nodes using a unified 14-parameter framework. Analytical reliability was confirmed via inter-rater reliability testing (Krippendorff’s alpha = 0.822). Using a joint display synthesis, technical compliance failures were mapped to qualitative user narratives. Results supported Hypothesis 1 (H1) via chi-square testing, revealing systemic failures (p < 0.05) in ramps (2%) and information systems (0%). Hypothesis 2 (H2) was validated through a one-sample t-test, showing that stakeholder perception (mean = 1.55) was statistically significantly lower than the neutral threshold (t(99) = −21.10, p < 0.001). These deficits triggered restrictive user adaptation strategies, including temporal displacement and forced social dependency. The study establishes a replicable ‘justice-centred’ audit framework to prioritise interventions in resource-constrained urban contexts.

Future Transportation doi: 10.3390/futuretransp6030117

Authors: Pavlo Nosov Oleksiy Melnyk Tomáš Kalina Martin Jurkovič Oleg Onishchenko Mykola Malaksiano Alona Sokol Petro Nykytyuk

The rapid development of Maritime Autonomous Surface Ships (MASS) requires advanced data-driven approaches for navigation safety monitoring and human-factor-aware risk analysis. This research proposes a fractal–episodic framework for assessing ship-control microvariability from normalized AIS/ECDIS trajectories in risk-oriented navigation monitoring, with particular relevance to MASS. The framework converts local micro-motion irregularities into passage-level indicators through sliding-window analysis of XTE-derived signals; computation of Higuchi, DFA, and Katz fractal measures; formation of a nine-component track signature; min–max normalization; and weighted aggregation into a chaos score complemented by a confidence index. The proposed framework can support intelligent monitoring and decision-support systems in autonomous maritime operations by providing interpretable behavioral indicators derived from AIS/ECDIS data.

Future Transportation doi: 10.3390/futuretransp6030116

Authors: Seyed Behbood Issa-Zadeh Claudia Lizette Garay-Rondero

Maritime pilotage is increasingly shaped by decarbonisation, digitalisation, and wider sustainability pressures, yet the integration of sustainability thinking into pilotage training remains insufficiently understood. This study addresses that gap by examining sustainability thinking not simply as an issue of awareness, but as a problem of training integration in a safety-critical professional context. Using an exploratory, theory-informed quantitative design, the study reinterprets primary survey data from 39 active maritime pilots through a deductive analytical framework combining sustainability pillars, integration domains, sustainability thinking sub-competencies, composite analytical conditions, and structured interpretive synthesis. The findings show that sustainability-oriented thinking is already present in pilotage practice, but that its integration into training remains uneven. It appears strongest where it is embedded in operational judgement and socially established professional norms, and weaker where it depends on pedagogical reinforcement, institutional consistency, and digital credibility. The main challenge is therefore not the absence of sustainability awareness, but the inconsistent translation of that awareness into trainable, repeatable, and professionally reinforced competence. By deriving barriers, enablers, drivers, and risks, the study offers a more applied framework for understanding sustainability thinking as a training and professional development issue in maritime pilotage.

Future Transportation doi: 10.3390/futuretransp6030115

Authors: Luciana Ferreira Leite Leirião Vinicius Pazini Leite Ronan Adler Tavella Daniela Debone Simone Georges El Khouri Miraglia

This study investigates associations between socioeconomic and travel variables among users of the São Paulo metro, focusing on travels made for work and study purposes, which are expected to reflect regular commuting patterns, and identifies the main variables associated with mobility characteristics within this group. Using data from the 2017 Origin–Destination Survey conducted by the São Paulo Metro Company, a set of 10,522 respondents was analyzed. The statistical analysis employed Pearson correlation, factor analysis of mixed data (FAMD), and multiple linear regression. The findings indicate that both socioeconomic and travel variables were significantly associated with mobility characteristics among metro system users in the Metropolitan Region of São Paulo (RMSP). The main variables associated with these mobility characteristics were the distance between origin and destination, the distances to the respective stations, travel duration, age, study status, employment status, education level, Brazilian Criteria score, and number of vehicles. Based on the FAMD, these variables were organized into multiple dimensions that could be descriptively grouped into three main groups of information: travel burden and spatial accessibility; life-stage and educational/occupational profile; and life-stage and socioeconomic position. The socioeconomic composition of consistent metro users predominantly includes middle and middle-lower economic classes, with lower economic class, lower household income, and lower education levels being associated with longer travel distances and durations. The study also revealed that most metro travels are within 20 km, with an average travel time of 74 min. These findings suggest that improved infrastructure and better-distributed metro networks throughout the RMSP may contribute to enhancing accessibility, promoting social inclusion, and improving transportation equity.

Future Transportation doi: 10.3390/futuretransp6030114

Authors: Ziyad N. Aldoski Csaba Koren Dilshad Mohammed

Driving in snow presents major safety challenges due to reduced visibility and slippery road conditions. Simulation-based tools may help improve hazard awareness; however, their effectiveness depends on how realistically they represent real-world driving experiences. This study examines the perceived realism and learning outcomes of a browser-based snow-driving simulation. A total of 87 licensed drivers with prior snow-driving experience interacted with a first-person browser-based simulation and subsequently completed a structured questionnaire. Composite indices were developed to measure Real-World Risk Perception (RWRP), Simulation Realism (SRI), Learning and Reflection (LEARN), and Awareness and Behavioral Reconsideration (AWARE). Quantitative analyses included reliability testing, descriptive statistics, correlation analysis, and multiple regression, complemented by qualitative thematic analysis. Results showed that perceived simulation realism was significantly associated with self-reported learning and awareness outcomes, whereas prior real-world risk perception was only weakly associated with post-simulation responses. Behavioral consistency between reported real-world and simulated driving behaviors was limited, suggesting that increased cognitive awareness does not necessarily correspond to behavioral equivalence. Qualitative findings identified limitations in vehicle dynamics, environmental complexity, traffic interactions, and emotional realism. Overall, the findings suggest that perceived realism plays a central role in shaping learning and awareness outcomes in browser-based driving simulations. The study highlights the educational potential of accessible web-based simulation environments while also emphasizing limitations in behavioral realism and transfer.

Future Transportation doi: 10.3390/futuretransp6030113

Authors: Jorge Becerra-Moreno Antonio Hurtado-Beltran Francisco J. Domínguez-Mota Agustín Guerra

Rapid urbanization has intensified congestion, environmental pressures, and transport inequities, thereby increasing interest in Smart Urban Mobility (SUM) as an approach that combines digital technologies, sustainable transport strategies, and data-informed decision-making to respond to these challenges. However, the evaluation of SUM remains fragmented due to the absence of harmonized assessment frameworks and the diversity of methodologies applied across smart city contexts. This study presents a systematic literature review of evaluation approaches and indicator architectures for SUM in smart city contexts. Using a PRISMA-guided screening process, 33 eligible studies were selected from 412 retrieved records. Three main methodological groups were identified: quantitative approaches, multi-criteria decision-making methods, and qualitative or participatory frameworks. A total of 273 indicators were organized into eight factor categories, confirming the multidimensional nature of smart mobility assessment while also revealing limited consistency in indicator selection and application across studies. Across the selected studies, current evaluation practices are increasingly linked to project prioritization, planning, and decision support; however, their effectiveness remains constrained by data inconsistencies, governance fragmentation, and insufficient user inclusion. These findings highlight the need for assessment frameworks that are sufficiently comparable to enable cross-city learning, yet flexible enough to reflect local contexts and institutional realities.

Future Transportation doi: 10.3390/futuretransp6030112

Authors: Issa Mahamied Andrés Rodríguez Silvia Sipone Luigi Dell’Olio

Autonomous mobility is reshaping how travel time is perceived, experienced, and monetised. Most existing studies have examined the value of time (VOT), willingness to pay (WTP), comfort and safety perception, digital services, and user perception as isolated phenomena, with limited efforts to integrate these dimensions into unified analytical frameworks. This study aims to address the fragmented nature of existing research by developing an integrated understanding of user behaviour in autonomous mobility, linking VOT, WTP, psychological constructs, and service-related factors within a unified analytical perspective. A systematic review methodology following PRISMA 2020 guidelines was applied. A total of 81 peer-reviewed studies published between 2015 and 2026 were included and analysed, focusing on Private Autonomous Vehicles (PAVs) and Shared Autonomous Vehicles (SAVs). The results reveal three main trends. First, autonomous travel introduces greater flexibility in time use and enables productive or leisure activities during travel. Second, behavioural aspects of VOT and WTP are strongly influenced by psychological constructs such as trust, safety, and risk perception. Third, notable differences emerge between PAV and SAV contexts, particularly in terms of comfort, control, and safety perception. The literature predominantly employs stated preference surveys, discrete choice models, and hybrid models incorporating psychological factors. However, fragmentation persists in modelling behavioural aspects of time perception and shared mobility services. This study provides a structured synthesis of existing evidence and highlights key research gaps by integrating economic, psychological, and service-related dimensions. The findings emphasise the importance of context-specific and psychologically informed modelling approaches to better understand user acceptance and behavioural adaptation in autonomous mobility systems.

Future Transportation doi: 10.3390/futuretransp6030111

Authors: Panagiotis Karetsos Viktoria Petkani Dimitris Tzanis Evangelos Mintsis Evangelos Mitsakis

Accurate short-term traffic prediction is a key requirement for modern traffic management systems, yet many existing approaches remain focused on offline evaluation and do not address the challenges of continuous real-time deployment. In this work, we present a context-aware spatiotemporal graph convolutional network (STGCN) framework designed for low-latency, scalable traffic forecasting under operational conditions. The proposed approach integrates structural information from the road network, temporal regularities derived from historical data, and a residual correction mechanism trained on systematic prediction errors observed during real-time operation. The framework is designed to remain lightweight, enabling continuous minute-level inference without computational overhead that would hinder long-term deployment. The methodology is evaluated in two real-world case studies of different scale and complexity. In Thessaloniki, Greece, multiple forecasting models are evaluated across different temporal resolutions using one-minute speed data, with the proposed STGCN selected for real-time deployment. A residual correction module trained on historical prediction errors further improves real-time forecasting accuracy compared to the baseline STGCN deployment. Scalability is further demonstrated in the South Holland region of the Netherlands, where the same architecture is applied to a larger network and extended to multi-horizon forecasting. Results show that the proposed framework achieves competitive predictive performance while maintaining low computational cost, and that incorporating residual error learning provides a robust and practical solution for improving forecasting accuracy in real-world deployments. These findings highlight the importance of combining domain-specific modeling with operational considerations in traffic prediction systems.

Future Transportation doi: 10.3390/futuretransp6030110

Authors: Maria Osipenko Fanqi Meng

Predicting origin–destination flows in high-density bike-sharing systems remains challenging due to the lack of models that jointly capture temporal dynamics and behavioral variability in urban mobility. In this study, we introduce a spatio-temporal optimal transport framework with dynamically calibrated behavioral regularization that integrates physical network costs with historical mobility priors to infer latent behavioral structure in trip patterns. Unlike static or purely predictive approaches, the proposed framework captures temporal spillovers across hourly intervals, allowing for the continuous evolution of mobility flows. We reinterpret the regularization parameter as a behavioral persistence indicator governing the trade-off between cost minimization and prior adherence. This parameter is dynamically calibrated over a 12-month period using Kullback–Leibler divergence from historical priors, enabling a behavioral diagnostic perspective on mobility regimes. Empirically, we uncover statistically significant regime shifts: weekday mobility is dominated by cost-efficient flows, whereas weekend behavior exhibits stronger adherence to historical mobility patterns and greater variability. We further identify systematic weather-related modulation, with adverse conditions associated with reduced behavioral persistence and patterns consistent with a contraction of discretionary mobility. These findings demonstrate that the proposed framework yields an interpretable behavioral metric for urban mobility systems. This has implications for adaptive mobility management, enabling data-driven rebalancing strategies that respond to temporal variation in behavioral regimes.

Future Transportation doi: 10.3390/futuretransp6030109

Authors: Danut Dragos Damian Felicia Michis Luminita Moraru

This study proposes a transparent, data-driven framework for behavior recognition based exclusively on IMU measurements, hypothesizing that vehicular jerk-based features can help in differentiating driving behavior. Unlike studies relying on direct jerk values, our approach derives novel findings from jerk-based features. For rolling windows of 300 samples, a comprehensive set of statistical and dynamic descriptors is extracted, including amplitude, variance, standard deviation, coefficient of variation, standard error, skewness, and kurtosis, as well as jerk-based features such as jerk_std, jerk_variance, jerk_amplitude, and jerk_spikes. Statistical analysis is used to identify features with strong discriminative power. The selected features are used to compute the Driving Score (DS) and, along with the Kernel Density Estimation (KDE) and associated statistics, provide a driver’s profile. Low DS values are consistently associated with increased jerk variability, whereas high DS values correspond to smoother and more controlled motion profiles. The robustness of the proposed framework is evaluated using several machine learning classifiers as baselines, with the jerk-based features as inputs. For the aggressive driver class, the Driving Behavior Score (DBS) model reports a Recall of 0.952 and an F1 of 0.925. For the normal driver class, the DBS model reports a Recall of 0.839 and an F1 of 0.879. The model has a total accuracy of 0.907. Also, Logistic Regression and ensemble models like Extreme Gradient Boosting (XGB) and Random Forest (RF) perform well. The proposed framework offers an explainable, computationally efficient alternative to conventional machine-learning classifiers for identifying aggressive drivers. It relies on lightweight statistical computations being suitable for real-time implementation.

Future Transportation doi: 10.3390/futuretransp6030108

Authors: Zoltán Rózsás István Lakatos

Pedestrian safety at urban intersections requires risk-aware mechanisms that extend beyond binary collision detection toward comparative prioritization among multiple agents. This study introduces the Intelligent Pedestrian Model (IPM), a reference-normalized scalar framework that represents pedestrian risk as a function of trajectory, contextual, infrastructural, and behavioral factors, decomposed into Exposure and Severity components. Building on IPM, the Safety-Prioritized Trajectory Model (SPTM) operationalizes the Exposure component using an observation-only, leakage-free kinematic proxy embedded into a cost-aware negative log-likelihood objective. Evaluation on the ETH/UCY benchmark under a strictly inductive protocol shows that moderate prioritization (β ≈ 1.0) improves best-of-K multimodal performance (ALL FDE@K: 0.979 → 0.970 m) while maintaining mean displacement accuracy within seed-level variability. The results indicate that Exposure-based weighting does not act as a global accuracy enhancer but redistributes predictive capacity toward safety-relevant motion regimes. Validation currently covers two ETH/UCY folds under a controlled inductive protocol, while broader cross-fold evaluation remains for future work.

Future Transportation doi: 10.3390/futuretransp6030107

Authors: Isaac Ndumbe Jackai II Steffel Ludivin Tezong Feudjio Tevoh Lordswill Ndingwan Olive Dubila Dindze Davide Shingo Usami Brayan Gonzalez-Hernandez Luca Persia

Real-time crash risk assessment is a key component of proactive road safety management, enabling the identification of hazardous conditions within short temporal intervals before crashes occur. Traditional crash-based models are unsuitable for such applications due to the rarity, reporting delay, and stochastic nature of crash data. Traffic conflicts, capturing near-miss interactions between road users, provide a practical alternative for real-time safety analysis. Over the past decade, numerous modelling approaches have been developed to translate conflict information into crash risk estimates; however, the literature remains fragmented and lacks a unified analytical synthesis. This review presents a state-of-the-art, model-centric analysis of conflict-based approaches, classifying them into five paradigms: statistical/regression-based, Bayesian, extreme value theory (EVT), machine learning (ML), and hybrid models. Beyond classification, the study conducts a structured cross-paradigm comparison across key dimensions, including conflict representation, data characteristics, temporal modelling, uncertainty treatment, validation strategies, computational complexity, and operational readiness. The paradigms are further interpreted through the complementary lenses of conflict frequency and severity. The review identifies key research gaps, including fragmented conflict definitions, challenges in modelling rare and extreme events, incomplete treatment of uncertainty and spatiotemporal dynamics, and limitations in validation, transferability, and deployment. Emerging research directions include standardized and adaptive conflict indicators, EVT–machine learning integration, integrated uncertainty-aware frameworks, advanced spatiotemporal modelling, transferable models, and scalable real-time implementation. By combining structured evidence mapping and cross-paradigm synthesis, this study supports model selection, development, and deployment for dynamic crash risk assessment.

Future Transportation doi: 10.3390/futuretransp6030105

Authors: Joydeep Banik Md Emon Miah Arman Hossain Md Sifat Bin Siraj Armana Sabiha Huq Tiziana Campisi

Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are becoming convenient modes of transportation; however, their safety remains a critical concern as crashes continue to occur. To reveal crash trends and temporal variations, this study develops time-series forecasting models to predict future crash counts of such vehicles. The crash dataset released by the National Highway Traffic Safety Administration (NHTSA) has been used here. Two univariate forecasting models—the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Facebook Prophet model—have been used here for different datasets. The models were trained on 30 months of data (July 2021 to December 2023) and validated on 6 months of data (January–June 2024). Validation metrics include Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Theil’s U1 statistic. Results showed that Facebook Prophet significantly outperformed SARIMA for both datasets, achieving an RMSE of 2.71 and an MAPE of 6.9% for ADAS, and an RMSE of 2.24 and an MAPE of 8.85% for ADS. For both systems, the model revealed empirically observed cyclical patterns and consistent rising trends. ADAS crashes exhibit a bimodal temporal pattern, with recurring peaks in January and May–June, alongside notable troughs in February–March and August–September. ADS displays a trimodal pattern, with recurring peaks in April–May, August and October, alongside notable troughs in December and the early winter months. These patterns represent empirically identified temporal regularities rather than causally attributed seasonality. From the future forecasts for July to December 2024, the model showed that ADAS crashes are expected to range between 40 and 80 per month, while ADS crashes are projected to remain between 20 and 40 per month. These findings underscore the need for proactive safety measures and enhanced regulatory oversight during identified high-risk periods to mitigate the growing trend in AV crashes.

Future Transportation doi: 10.3390/futuretransp6030106

Authors: Cüneyt Çatuk

This study examines the determinants of bilateral trade asymmetries between Türkiye and its three main EU partners—Germany, Bulgaria, and Romania—over 2002–2024. Within the gravity framework, bilateral symmetry in trade data implies that reported exports should equal partner imports (Xᵢⱼ = Mⱼᵢ). Deviations from this condition reflect systematic distortions caused by valuation practices, institutional gaps, and crisis-induced disruptions. This study employs a fixed-effects panel framework to identify the structural and contextual determinants of mirror−data asymmetries in Türkiye–EU trade. Using HS2−level mirror statistics from TÜİK and Eurostat, three asymmetry measures—the Bilateral Asymmetry Index (BAI), Absolute Logarithmic Difference (ALD), and Relative Symmetry Index (RSI)—are estimated through a fixed-effects panel model. Results show that a one−unit improvement in logistics performance (LPI) reduces asymmetry by approximately 0.17 points (p < 0.01). Maritime connectivity (LSCI) shows a small but statistically significant positive coefficient, while exchange rate volatility remains insignificant. The effects of global crises are heterogeneous: the 2008 financial crisis significantly increases asymmetry (+0.07, p < 0.01), whereas COVID−19 is associated with a reduction in asymmetry (−0.04, p < 0.01). The interaction between LPI and crisis periods is negative and significant (−0.03, p < 0.05), confirming that a stronger logistics capacity buffers crisis-induced reporting gaps. Country-specific results reveal that Romania drives much of the variation (within−R2 = 0.26), while Germany remains largely insulated from crisis effects. The findings highlight that deviations from bilateral symmetry are driven by structural and institutional factors rather than random error. Policy recommendations stress harmonized customs valuation, digital logistics integration, and enhanced Türkiye–EU statistical coordination to strengthen trade data reliability and crisis resilience.

Future Transportation doi: 10.3390/futuretransp6030104

Authors: Ivana Čermáková

The transformation of transport is necessary not only for climate change mitigation, but also for increasing competitiveness, developing modern technologies in transport, and improving the well-being and quality of life of the population. This article discusses the current state of the transformation of transport and infrastructure to low/zero emission within EU member states and, in particular, their smart cities. This article discusses the challenges, modern technologies, disadvantaged groups and overall concept of transformation with the aim of designing the most effective strategy for transport transformation in the EU, potentially at the smart cities level. The potential relationship between the position of EU member states in the Climate Change Performance Index (CCPI) ranking and different environmental and non-environmental indicators in the EU is identified and analyzed. Regression and ordered logit models are calculated. The results show that only minimum indicators are not correlated, and greenhouse gas emission (GHG), urbanization rate in the EU member state and the ratio of private car ownership to public transport usage have a significant impact on the potential transformation of transportation and a country’s ranking in the CCPI. The odds ratio for urbanization rate is 3.18 (+1 means better ranking 24 times greater) and 4.68 for the ratio of private car ownership to public transport usage (+1 means better ranking 108 times greater). The discussion of the article defines research trends aimed at improving the level of transport transformation and challenges related to successful transformation.

Future Transportation doi: 10.3390/futuretransp6030103

Authors: Ang Ji Xilu Ju Nieyangzi Liu Junxian Chen Zhe Dai

Cooperative Connected and Automated Mobility (CCAM) is a critical paradigm for overcoming the limitations of single-vehicle intelligence and enabling coordinated intelligent transportation. To address the lack of systematic reviews towards recent CCAM advances, this paper presents a comprehensive review of relevant publications from the past five years. First, we establish a unified framework spanning communication, perception, decision-making, and control, and clarify the associated core components and technologies. Then, we identify three major bottlenecks that constrain large-scale CCAM deployment: uncertainty propagation along the perception-decision-control (PDC) chain, misalignment between functional safety and SOTIF standards, and inadequate end-to-end cybersecurity protection. In the context of 5G-A/6G, edge computing, and large-language-model-driven intelligence, we further propose targeted research directions. This survey aims to provide a systematic reference for theoretical investigation and engineering implementation.

Future Transportation doi: 10.3390/futuretransp6030102

Authors: Jorge Gonçalves Fernando Nunes da Silva Robert de Almeida Marques

This article conducts a thorough comparative analysis of public transport systems in Curitiba and Lisbon, focusing on cost-efficiency and structural performance from the user’s viewpoint. Curitiba is noted for pioneering the BRT model in the 1970s, while Lisbon is evolving towards a multimodal system with substantial investments in integration and user-centric policies. Employing a case study methodology and mixed analytical approaches, the analysis examines governance structures, network architecture, financing mechanisms, and service quality indicators. The findings indicate that although Curitiba imposes a similar or higher fare burden relative to user incomes, it offers significantly lower service value across various dimensions, including modal diversity and infrastructure quality. In contrast, Lisbon’s integrated governance model for bus and tram networks proves effective in enhancing accessibility and sustainability, despite some coordination issues with centrally governed transport networks. This study contributes to the international discourse on the limitations of single-modal transport systems and highlights the necessity of institutional integration, long-term investment, and adaptive governance frameworks for urban mobility transformation in the 21st century.

Future Transportation doi: 10.3390/futuretransp6030101

Authors: Yanxuan Song Pengyan Lei Yanyang Yin Shuangqi Xu

Unsignalized intersections involve complex interactions among heterogeneous road users and are associated with elevated safety risks. Although surrogate safety measures derived from high-resolution trajectories enable proactive safety assessment, such data are not widely available in routine monitoring systems, which often provide only coarse-grained traffic observations. This study examines how the inferability of surrogate safety information changes as the available traffic data become progressively coarser. Using the high-resolution inD dataset, we implement a controlled feature degradation framework across three nested levels of data granularity and develop intersection-specific models for three tasks: critical conflict detection, dominant direction classification, and vulnerable road user (VRU) involvement identification. Model performance and changes in variable importance are evaluated using PR-AUC and SHAP analysis. The results show clear task-dependent degradation. Models based on high-granularity data achieve strong overall performance, with an average PR-AUC above 0.88. Dominant direction classification remains relatively robust as data granularity decreases, with PR-AUC declining from 0.970 to 0.893, whereas VRU involvement identification deteriorates substantially, from 0.991 to 0.697. The results further indicate that vehicle-based traffic variables retain meaningful predictive value for conflict detection and direction classification but are insufficient for reliable inference of VRU-related risk. Interpretability analysis shows a progressive shift in model reliance from kinematic interaction variables to coarser exposure-related and structural descriptors as observability decreases. These findings clarify the relationship between data granularity and task-dependent surrogate safety inference at unsignalized intersections.

Future Transportation doi: 10.3390/futuretransp6030100

Authors: Bratislav Lukić Goran Petrović Žarko Ćojbašić Dragan Marinković Srđan Dimić

Real-time traffic signal control represents a key challenge in modern intelligent transportation systems, particularly under highly variable traffic flows and the presence of priority vehicles. This study proposes a hybrid framework for adaptive signal plan control at a signalized intersection. The framework integrates deep learning-based traffic prediction, surrogate-based performance evaluation, and reinforcement learning-based adaptive control. Short-term traffic flow is predicted using recurrent neural networks, providing anticipatory information for traffic control decisions. Based on predicted flows and generated candidate signal plans, a machine learning surrogate model enables fast estimation of key performance indicators, including average vehicle delay and queue length. Adaptive control is implemented using the Proximal Policy Optimization algorithm within the SUMO environment via TraCI, which enables real-time fine-tuning of signal phases. A dedicated priority and stability module ensures effective emergency vehicle preemption and adaptive public transport priority while preserving intersection stability. Simulation results show that the proposed framework reduces average vehicle delay by up to 35% compared with FT and by up to 15% compared with standalone RL, while also improving traffic flow efficiency and priority vehicle performance.

Future Transportation doi: 10.3390/futuretransp6030099

Authors: Armin Stein Bjarne Käberich Souhaiel Ben Salem Raffael Bausch Thomas Vietor

Growing mobility demand and declining vehicle utilization motivate dual-use vehicles that can alternately transport passengers and freight. This work presents an early-stage utility value analysis to select a baseline concept and integrates it into model-based systems-engineering architecture development of an autonomous dual-use shuttle. Existing dual-use-capable shuttle concepts were screened and comparatively assessed using a utility value analysis with exclusion criteria and weighted evaluation criteria, including operational versatility, module exchange flexibility, infrastructure effort, battery positioning, and technology readiness. Criterion weights were derived by pairwise preference analysis, emphasizing the versatility of use scenarios. The highest-ranking concept, 101 Modular Mobility, was selected as the reference architecture. Subsequently, a SysML system model was developed in a MagicGrid-structured model-based systems-engineering (MBSE) process, covering stakeholder needs, key use cases such as transport service usage, module exchange, and automated charging, and the resulting system context and interfaces. The system model is augmented by a tailored Grey Box structural viewpoint within the MagicGrid workflow to make module boundaries and inter-module interfaces explicit for the modular dual-use shuttle architecture. The resulting model provides a traceable early architectural baseline for further refinement and subsequent verification activities.

Future Transportation doi: 10.3390/futuretransp6030098

Authors: Treerapot Siripirote Apivat Jotisankasa

This study proposes a rigorous optimization framework for the design of traffic counting station locations in large-scale highway networks, with specific application to Thailand’s national highway system. A mixed-integer linear programming (MILP) model is developed to determine the optimal sensor placement under budget-constrained scenarios while explicitly incorporating existing infrastructure. The model aims to maximize origin–destination (OD) flow observability and minimize estimation error, measured by the percentage of OD flows intercepted and root mean square error (RMSE). The proposed framework is validated using a real-world network. The results demonstrate that the optimized design significantly outperforms conventional approaches, including random and high-flow-based selection methods, achieving over 70% reduction in estimation error and 93% of OD flows intercepted with a feasible number of stations. Furthermore, the statistical representativeness of the selected locations is validated across spatial, functional, and traffic characteristics and traffic measurement errors. The findings provide a scalable and cost-effective decision-support tool for transport authorities in developing countries seeking to modernize transportation planning, traffic management, and infrastructure development under limited resources.

Future Transportation doi: 10.3390/futuretransp6030097

Authors: Saki Rezwana Haimanti Bala

The transition to autonomous driving is creating mixed traffic environments in which human-driven vehicles, partially automated vehicles, and autonomous vehicles must continuously interact, adapt, and respond to one another. This paper presents a comprehensive review of driver behavior in mixed traffic with autonomous vehicles, with emphasis on the sociotechnical nature of human–machine coexistence. The review synthesizes recent evidence on behavioral adaptation in car-following and tactical decision-making, trust calibration, situational awareness, takeover performance, internal and external human–machine interface design, surrogate safety metrics, vehicle-to-vehicle communication, operational design domains, and data-driven scenario generation. The literature shows that drivers do not respond to autonomous vehicles uniformly. Instead, behavior varies by driving style, perceived predictability of the automated vehicle, interface transparency, and traffic context. The review also emphasizes that these interaction patterns are context-dependent and may differ substantially across regions, particularly in dense mixed traffic environments. While some adaptations can improve stability and safety, others can encourage opportunistic maneuvers, overtrust, confusion, or degraded takeover quality. The review also highlights that crash data alone are insufficient to assess safety in mixed traffic, and that near-miss analysis, surrogate conflict metrics, and scenario-based evaluation are essential for understanding safety-critical interactions. Across the literature, a central inference emerges: adaptation to autonomous vehicles is real, but it is not automatically stabilizing. Safe deployment therefore depends not only on technical vehicle performance but also on behavioral legibility, transparent communication, calibrated trust, and robust evaluation under diverse real-world conditions. The paper concludes by identifying major research gaps, including the lack of longitudinal studies, incomplete standardization of surrogate metrics, limited understanding of vehicle conspicuity effects, and the need for integrated frameworks that jointly assess driver behavior, system design, and scenario-based safety.

Future Transportation doi: 10.3390/futuretransp6030096

Authors: Saida Fellah Charif Mabrouki

Dry ports are becoming increasingly important elements of port–hinterland transport systems, particularly as maritime gateways face rising congestion, infrastructure pressure, and coordination challenges within global supply chains. As international trade expands and logistics networks grow more complex, inland terminals are progressively evolving into integrated intermodal platforms that support more efficient freight distribution between seaports and their hinterlands. This study presents a PRISMA-based systematic review of research on dry port–seaport systems covering the period 1980–2025. Following a structured screening and selection procedure, peer-reviewed publications were identified and analyzed to examine conceptual developments, thematic orientations, geographical scope, and decision-making perspectives within the field. Particular attention is given to the growing relevance of digital transformation, including artificial intelligence and machine learning, in shaping future dry port operations and network design. By synthesizing existing contributions and identifying research gaps, this review provides a consolidated understanding of the evolution of dry port research and outlines key directions for advancing sustainable, resilient, and data-driven port–hinterland systems.

Future Transportation doi: 10.3390/futuretransp6030095

Authors: Hamed Nozari Zornitsa Yordanova

The rapid increase in the number of cars in large cities has made efficient parking management one of the major challenges of urban transportation systems. The present study aims to develop a smart framework for sustainable allocation of parking spaces in urban environments, and presents an integrated approach based on digital twin and multi-objective optimization. In this framework, a digital model of the urban parking system is created that is able to analyze real and simulated data related to parking demand, space occupancy status, and traffic flow and support optimal allocation decisions. The results of the analysis show that using the proposed framework can reduce parking search time by an average of 28%, make the distribution of parking use more balanced, and consequently reduce the amount of pollutant emissions from vehicle movement by about 17%. Also, sensitivity and scalability analyses show that the proposed model also has stable and reliable performance in large urban networks. These results indicate that the proposed framework can be used as an effective tool for developing sustainable parking management systems in smart cities.

Future Transportation doi: 10.3390/futuretransp6030094

Authors: Yuhui Liu Xiongguan Bao Shuangming Li Chenhui Gu Qihua Fang

To improve ship AIS trajectory prediction under pronounced spatiotemporal coupling and dynamic maneuvering conditions, this study proposes a Spatio-Temporal-Hybrid-Multi-head Attention model (STHA) integrating multiscale convolution, bidirectional long short-term memory, and multi-head attention. Historical AIS data from the Zhoushan waters in 2024 were preprocessed through screening, cleaning, outlier removal, resampling, and cubic spline interpolation to construct trajectory samples. Comparative experiments were conducted against BP, BiLSTM, and BiGRU using MAPE, RMSE, and R2 as evaluation metrics. The results show that STHA achieves the best overall predictive performance, more accurately follows trajectory variations across different vessel types, and exhibits better robustness in scenarios involving turning and speed changes. These findings indicate that the proposed model is effective for high-precision ship trajectory prediction and can provide useful support for subsequent collision risk assessment and navigation safety assistance.

Future Transportation doi: 10.3390/futuretransp6030093

Authors: Artem Holovan Vytautas Dubra Andrii Holovan

This study addresses the question of how augmented reality (AR) technologies can be designed and integrated into maritime navigation systems to meet the needs of young navigators within contemporary socio-technical bridge environments. The article is based on a qualitative, literature-based research methodology involving a structured analysis and synthesis of peer-reviewed journal articles and conference proceedings related to AR interfaces, human performance, decision support, and maritime training. The reviewed studies indicate that AR can enhance perceptual and situational awareness by overlaying navigational information directly into the navigator’s field of view, thereby reducing head-down time, improving spatial alignment of information, and supporting performance in low-visibility and high-traffic conditions. The literature also shows that AR-enabled visualizations and shared displays can support individual and team-based decision-making by facilitating real-time, context-aware information exchange on the ship’s bridge. Safety-related benefits are identified as indirect outcomes of improved perception and cognitive support rather than as isolated technological effects. Simultaneously, the findings highlight that these benefits depend strongly on human-centered interface design and appropriate training. The study concludes that AR has significant potential to enhance maritime navigation for young navigators when integrated as part of a balanced socio-technical system combining technology, human factors, and structured education.

Future Transportation doi: 10.3390/futuretransp6020092

Authors: Mustafa Albdairi Ali Almusawi

Traffic signal control at signalized intersections plays a key role in mitigating urban congestion, reducing vehicle emissions, and improving road safety. This study examines three signal control strategies at a four-approach isolated intersection simulated using the Simulation of Urban Mobility (SUMO) microscopic traffic simulator: a baseline fixed-time plan, a Webster-optimized fixed-time plan, and a queue-responsive adaptive controller implemented through the Traffic Control Interface (TraCI). The strategies were evaluated under balanced traffic demand of 600 vehicles per hour per approach over a 3600 s simulation period. Performance was assessed using eight indicators related to mobility, environmental impact, and safety, including average delay, travel time, queue length, network speed, throughput, CO2 emissions, fuel consumption, and time-to-collision events. The results indicate that the adaptive controller produced the greatest improvements, reducing delay by 14.3%, travel time by 13.6%, CO2 emissions by 9.3%, fuel consumption by 9.4%, and TTC conflicts by 11.2%, while increasing network speed by 47.9%. The Webster-optimized plan achieved moderate improvements, lowering delay by 4.8% and fuel consumption by 5.0% without additional infrastructure requirements. Overall, the findings suggest that both signal re-timing and queue-responsive adaptive control can enhance intersection performance, with the preferred approach depending on available infrastructure and implementation costs.

Future Transportation doi: 10.3390/futuretransp6020091

Authors: Francisco Isaías Rivera-Meza Jaime Wenceslao Parra-Moroyoqui José Leonardo Jiménez-Ortiz Omar Arodi Flores-Laguna Guillermo Cano-Verdugo Gener José Avilés-Rodríguez

Urban mobility is increasingly conceptualized as a multidimensional, user-centered domain of transport system evaluation with potential implications for population health. This study examined the association between user-reported urban mobility experiences and perceived stress among adults using a high-intensity corridor in Nogales, Sonora, Mexico. A quantitative cross-sectional analytical study was conducted with 423 participants using the Urban Mobility Experiences Scale (UMES) and the Perceived Stress Scale (PSS-14). Spearman’s correlation analyses showed inverse associations between perceived stress and several mobility dimensions, although only Sustainability and Urban Environment remained statistically significant after Bonferroni correction (ρ = −0.266; p < 0.001). In multivariate analysis, Sustainability and Urban Environment, Accessibility and Connectivity, and Travel Time and Efficiency were retained as significant predictors, jointly explaining 14.1% of the variance in perceived stress (R2 = 0.141; f2 = 0.152). These findings suggest that multidimensional urban mobility experiences, particularly environmental and accessibility conditions, are associated with perceived stress beyond traditional operational indicators in high-intensity urban corridors.

Future Transportation doi: 10.3390/futuretransp6020090

Authors: Julián Sánchez Corredor Marta Luz Arango Uribe Cristian David Correa Álvarez

Urban infrastructure works conducted under live traffic conditions often face a persistent gap between approved traffic management plans and their actual field implementation. This gap remains underexplored in longitudinal studies, particularly in utility projects from low- and middle-income urban contexts. This study evaluates a risk-based supervisory approach that integrates daily monitoring of the Traffic Management Plan (TMP) with a corporate risk management framework aligned with ISO 31000. The dataset includes 288 supervised workdays over 16 months (November 2023–February 2025), 99 non-conformity tickets, 96 signal-theft events (137 units), and seven traffic incidents. The analysis combines descriptive statistics, hypothesis testing, logistic regression, segmented longitudinal analysis, count models, response-time evaluation, and a composite risk index. TMP non-compliance decreased from 18.8% to 6.9% between the first and second halves of the study period (p=0.0028). The odds of non-compliance were significantly higher during the staff transition period in April–May 2024 (OR = 3.50; 95% CI: 1.24–9.82), while day and night shifts showed comparable rates. Monthly patterns indicate that staff instability and signal theft contributed to non-compliance levels, and ticket resolution remained slow (mean response time: 69.9 days). These findings highlight the importance of supervisory continuity, contractor stability, and timely corrective actions in improving work zone safety.

Future Transportation doi: 10.3390/futuretransp6020089

Authors: Christina Rehmeier Lars Boserup Iversen

The transition from diesel to electric trucks faces a critical adoption gap despite technological maturity and favorable economics. This study identifies multi-dimensional planning complexity, spanning technical, economic, operational, and organizational dimensions, as a primary barrier that existing decision support tools fail to address. Through systematic literature review and analysis of Danish transport sector data, we develop the AI-Readiness Framework for Fleet Electrification (ARFFE), a modular decision support system adapted to different organizational readiness levels. Our secondary data analysis illustrates that two frequently overlooked factors, the CO2-differentiated road tax savings of 430,000–465,000 DKK over five years and charging strategy decisions creating cost differences of 930,000 DKK, have greater economic impact than traditionally emphasized factors. The framework comprises five progressive modules mapped across four readiness stages and four planning dimensions, creating an integrated decision support system for evaluating an estimated 50,000+ scenarios. This research contributes theoretically by proposing AI as a “mediating technology” in socio-technical transitions and practically by providing an actionable framework illustrated through Danish transport sector analysis.

Future Transportation doi: 10.3390/futuretransp6020088

Authors: Mohammed Alshriem Yin Yang

Road traffic injuries represent one of the most critical public health challenges in the Gulf region. Predicting traffic accident severity is therefore a critical component of evidence-based road safety management. In this study, we develop machine learning frameworks for predicting traffic accident severity using Qatar’s national dataset (2020–2025), addressing extreme class imbalance and interpretability. A dataset of 588,023 accident records was systematically preprocessed from 1,000,500 raw reports. We compare three approaches: multi-class (four severity levels), binary (Safe vs. Severe), and cascaded two-stage (combining both). Six classifiers were evaluated across two encoding methods and three balancing strategies. Systematic hyperparameter tuning with 5-fold stratified cross-validation was performed for all models. The binary LightGBM classifier achieved BA = 71.04%, AUC-ROC = 0.772, Sensitivity = 61.03%, and Specificity = 81.05%, demonstrating superior performance over multi-class approaches. Temporal validation on 2025 data (trained on 2020–2024 data) supported good temporal generalization. Analysis of 10,000 test instances identified the time period as the dominant predictor of accident severity. The binary LightGBM framework provides an interpretable and effective approach for severe accident identification and risk prioritization, with SHAP findings supporting targeted temporal enforcement and pedestrian safety as evidence-based policy priorities.

Future Transportation doi: 10.3390/futuretransp6020087

Authors: Ateyah Alzahrani Naif Albelwi Ageel Abdulaziz Alogla

Global transition toward sustainable micro-mobility is an essential aspect of Saudi Vision 2030; however, high car dependency remains a significant barrier to public health and safety targets. In this context, this study explores the factors determining the adoption of electric bicycles (e-bikes) in Al-Qunfudhah, Saudi Arabia. The present research used a convenience sampling strategy through an online survey conducted via social media and texting, utilizing a designed questionnaire of 10 sections delivered to 171 participants, alongside a 5-point Likert scale. Additionally, the scientific validation and analysis were conducted utilizing internal consistency, validity and scale reliability via statistical analysis. The findings indicated a significant intention–action disparity; while respondents demonstrate a strong psychological intention to adopt e-bikes within 12 months (an average of 3.51), real household ownership was relatively low at 11.1%. In addition, a significant 71.9% of participants use private vehicles for short-distance travel (<5 km), influenced by an average bus stop distance of 21.22 km. The hierarchy of barriers indicates infrastructure and security as the main barrier, particularly the absence of dedicated bike lanes, and concerns regarding traffic safety. In contrast, a perception of physical fitness, and interpersonal interaction behave as significant facilitators. Public health data reveals an average weekly activity of 109.77 min, significantly lower than worldwide recommendations; however, 66.7% of individuals believe e-bikes may address the difference. The statistical evaluation acknowledged the questionnaire’s robustness, with significant Pearson correlation coefficients (p < 0.01) demonstrating internal consistency validity and Cronbach’s alpha values between 0.71 and 0.88 indicating high scale reliability, demonstrating a scientifically stable framework for assessing the measured behavioral determinants. The research recommends the establishment of shaded, dedicated micro-mobility networks and the enforcement of safety regulations to promote a healthy, multi-modal urban ecosystem.

Future Transportation doi: 10.3390/futuretransp6020086

Authors: Melis Tan Tacoglu Caner Tacoglu

This study addresses an integrated airport gate assignment and departure scheduling problem under capacity constraints while explicitly accounting for the operational role of apron resources. A bi-objective mixed integer linear programming model is developed to jointly determine gate or apron assignments and departure times by considering passenger transfer times, taxi operations, runway separation, and schedule deviations. The first objective minimizes a normalized composite measure of passenger transfer burden, taxi penalties, and departure schedule deviation, whereas the second objective minimizes apron usage. The epsilon constraint method is used to generate exact Pareto-efficient solutions. Computational experiments on synthetically generated congested hub airport instances with 20 flights show that increasing physical gate capacity from 3 to 5 improves the average value of Objective 1 from 1.37 to 0.92 and reduces average apron usage from 10.00 to 4.00 flights. In the highlighted 20-flight and 5-gate scenario, increasing apron usage from 3 to 5 assignments reduces the standard deviation of departure time deviations from 8.0 to 7.6 min. The results show that selective apron usage improves system-level schedule stability and that gate capacity and apron flexibility should be evaluated jointly in tactical airport planning.

Future Transportation doi: 10.3390/futuretransp6020085

Authors: Nawaf M. Alshabibi

Heavy vehicles leave a significant impact on passenger vehicles, which results in traffic instability. The size, acceleration, and behaviour of heavy vehicles notably influence the traffic flow. Considering this, traffic engineers have developed Passenger Car Equivalency (PCE) to examine the capacity, Level of Service (LOS), and flow of the urban roads. The aim of this study is to analyze the King Abdulaziz (KA) freeway in Dammam, Saudi Arabia, where heavy vehicles represent 35% of the peak hour traffic, which exceeds the PCE value given in the Highway Capacity Manual (HCM). This study addresses the given gap by employing the saturation headway approach. The study findings reveal PCE values of 1.78 for moving towards the port and 1.81 for coming from the port, respectively. These values are in line with the patterns of HCM, as the indication of low PCE denotes the appearance of increased heavy vehicles. Furthermore, the LOS was known to be of level E, reflecting frequent delays and slowdowns. The capacity in operations was reduced by 44–45%, thus emphasizing the requirement for strategic traffic approaches with functional interventions for heavy vehicle routes.

Future Transportation doi: 10.3390/futuretransp6020084

Authors: Mohammadreza Doostmohammadian

The safety and reliability of intelligent transportation systems (ITSs) can be greatly enhanced through adding redundancy in the information-sharing network of the vehicles. In this paper, we first model the mixed traffic of human-driven and autonomous vehicles as a distributed system observability problem using a network of communicating vehicles. We clearly show that a strongly connected network with a minimum of n links (with n as the network size) is sufficient for the observability of a mixed-traffic network. Then, we present graph-theoretic results on adding redundancy to the changing network of vehicles to make it resilient to the failure of a certain number of vehicles/sensors or their data-sharing links. Finally, we employ a distributed observer design to validate our results using a simple mixed-traffic example.

Future Transportation doi: 10.3390/futuretransp6020083

Authors: Hans Westerman John Black

Background: Regulations in some countries of the world allow self-driving vehicles (private cars and robo-taxis) to operate on geofenced, public roads, yet governments are slow to plan as how best to use this automated technology. We pose research questions about the Australian government’s preparedness, planning gaps for a transition to an autonomous public transport system, and specific system components that require attention. Method: We review the relevant literature, and podcasts of automobile manufacturing experts, and draw on our extensive professional experience advising governments in applying the systems approach to a planning system that includes autonomous transport. Results: Governments must include risk management in Type-II road corridors; develop mobility hubs that connect terminals for fully self-driving vehicles and robo-taxis to connect with public transport systems; and include body corporates when engaging the community in precinct planning. In the discussion, we argue the case for an autonomous urban public transport system where private ownership of vehicles is progressively reduced. Conclusions: Australian governments are not prepared with a systems-wide urban planning process that includes autonomous transport and self-driving vehicles. During the transition period, the existing and new transport systems must operate together, emphasising the leading role for governments. A roadmap for further research and development is outlined and this could provide the framework for urban planning in other jurisdictions.

Future Transportation doi: 10.3390/futuretransp6020082

Authors: Oleksander Ryzhanskyi Oleksander Barmak Eduard Manziuk Pavlo Radiuk Iurii Krak

Urban traffic signal control heavily impacts vehicle emissions, yet most reinforcement learning models falsely assume flat terrain, ignoring the energy penalties of uphill stop-and-go driving. This omission creates a structural misalignment between generic, delay-focused rewards and the energetic realities of hilly corridors. In this work, we propose a topography-aware deep reinforcement learning framework that mitigates this hidden ecological cost. Our Context-Specific Reward Design procedure selects, normalizes, and calibrates reward terms based on physical conditions and traffic composition. The controller was trained using a microscopic simulation calibrated from video-derived traffic data, featuring a 3.8-degree uphill approach, 14,800 vehicles over 9 h, and a 20% heavy-vehicle fleet. In the uphill setting, the specialized controller reduced total CO2 emissions to 256.97 million milligrams, corresponding to 8.6% and 4.7% reductions relative to a pressure-based and a standard deep Q-learning controller, respectively. The proposed method also achieved the lowest mean trip duration of 72.09 s and a queue length of 1.31 vehicles. Welch’s t-tests confirmed that these CO2, duration, and queue improvements were significant. Overall, treating topography as a foundational design variable is crucial for sustainable urban mobility.

Future Transportation doi: 10.3390/futuretransp6020081

Authors: Ruihang Li Huangnan Zheng Jian Wang Kaikai Xiao Zhe Yin Kehan Wang Wangliang Guo Hong Li Pan Lv Shijian Li Zhijie Pan

High-fidelity simulation testing is a critical component in ensuring the safety and reliability of autonomous driving systems. However, traditional methods for constructing simulation scenarios face two major bottlenecks. First, acquiring realistic road network topologies that adhere to physical and traffic rules is expensive. Second, the manual placement of scenario elements (e.g., vehicles and pedestrians) is a time-consuming and labor-intensive process, which struggles to meet the demands of large-scale and diverse testing. To address these challenges, this paper proposes an efficient and automated simulation scenario generation method and toolchain. The proposed approach begins by extracting road network topologies from real-world data sources (e.g., open map datasets) and then uses specialized tools, such as RoadRunner, to automatically assign traffic semantics and rules. The key innovation lies in leveraging the powerful image-text understanding capabilities of large multimodal models (LMMs) to analyze road network images and textual descriptions, generating a semantic heatmap that represents the spatial distribution probabilities of scenario elements. This heatmap guides the procedural content generation (PCG) process, enabling the intelligent and scalable deployment of traffic participants. Experimental results demonstrate that the proposed method can efficiently generate large-scale, high-fidelity, and cost-effective simulation scenarios. The generated scenarios not only maintain realism in topology and traffic rules but also feature rich perception and interaction capabilities. Furthermore, based on this method, we have constructed and released a novel simulation dataset tailored for training perception algorithms, further validating the practical value and advancement of the toolchain.

Future Transportation doi: 10.3390/futuretransp6020080

Authors: Lu Yang Tao Hong

Near-miss events, defined as hazardous traffic interactions without actual collisions, provide valuable indicators for proactive traffic safety assessment. However, existing studies mainly focus on collision detection or object-level perception, while near-miss interactions and their severity remain insufficiently explored. This study proposes a video-based framework for real-time near-miss detection and risk evaluation in complex urban intersections. The framework integrates an enhanced YOLOv11 detector with a small-object detection head, BoT-SORT multi-object tracking, and bird’s-eye-view (BEV) transformation to accurately extract trajectories and motion features of heterogeneous road users. A Near-Miss Risk Index (RI) is developed by jointly considering spatial proximity, time-to-collision, and motion intensity to quantify near-miss severity levels. Experimental results on real-world CCTV data demonstrate that the proposed method effectively identifies high-risk interactions among vehicles, motorcycles, and pedestrians, providing interpretable severity assessment and supporting proactive traffic safety analysis for intelligent transportation systems.

Future Transportation doi: 10.3390/futuretransp6020079

Authors: Genta Rexha Aleksandër Xhuvani Giuseppe Pompameo Antonio Zilli Michele Molfetta Rade Stanisic Antonio Cardillo Suad Mati

Disaster risk in port and municipal environments increasingly emerges from the interaction between natural hazards, critical infrastructure exposure, and governance complexity. Although formal risk assessment frameworks are established, challenges remain in translating static hazard analyses into dynamic situational awareness during rapidly evolving events. This study presents a comparative analysis of four reference areas in the Adriatic–Ionian region—Shkodra (Albania), Pescolanciano (Italy), the Port of Bar (Montenegro), and the Port of Taranto (Italy)—to identify vulnerabilities and monitoring gaps in disaster prevention systems. Based on document analysis and cross-case synthesis, the findings distinguish environmentally driven municipal risks from hybrid industrial–logistical risk profiles in port environments. The results indicate that regulatory frameworks are in place, yet constraints persist in obtaining high-resolution, near-real-time spatial information during flood, landslide, wildfire, and industrial scenarios. This study assesses UAV-based monitoring as a complementary tool to enhance situational awareness within existing governance structures, contributing to improved integration between risk assessment and operational disaster prevention.

Future Transportation doi: 10.3390/futuretransp6020078

Authors: Rahmad Hendri Pramudita Maya Arlini Puspasari Martino Luis Titis Wijayanto

Traffic accidents represent a significant threat to individuals, with motorcycles frequently involved. Despite concerted efforts by organizations like the World Health Organization and governments worldwide, reducing accident rates remains a challenge. Notably, Indonesia has witnessed a surge in traffic accidents, with motorcycles being a prominent mode of transport. This study aims to evaluate situational awareness and motorcycle riders’ behavior among Indonesians, with respect to factors such as riding time and age. This study involves laboratory-based research and uses quantitative primary data collected with the Situation Awareness Global Assessment Technique (SAGAT), the Situation Present Assessment Method (SPAM), and the Motorcycle Rider Behavior Questionnaire (MRBQ). The results indicate that overall situation awareness is low, with the lowest level among young riders. Nighttime situational awareness is also lower than during the daytime. Recommendations to improve situation awareness include periodic training with scenario-based sessions for motorcycle riders, strict adherence to driving regulations, the potential integration of motorcycle simulators, and prioritizing the program to enhance young riders’ situation awareness. These recommendations aim to boost rider safety and reduce motorcycle accidents.

Future Transportation doi: 10.3390/futuretransp6020077

Authors: Cristian Giovanni Colombo Jingbo Chen Sofia Borgosano Michela Longo

Wireless Power Transfer (WPT) for electric vehicles is transitioning from laboratory prototypes to deployable charging infrastructure, driven by the demand for safer, automated, and weather-robust charging in residential parking, depots, and public bays, and more recently by pilot electric-road concepts. This review focuses on near-field resonant inductive WPT and explicitly frames the discussion around standardization and deployment readiness, with SAE J2954 and related international frameworks as reference points for interoperability, alignment, conformance testing, and certification planning across static, quasi-dynamic, and dynamic solutions. Recent surveys and representative demonstrators are synthesized to consolidate dominant research and engineering themes, including magnetic coupler and shielding design, compensation-network and control co-design, segment architecture and handover strategies for dynamic tracks, safety functions, electromagnetic exposure verification, electromagnetic compatibility constraints, bidirectional operation, and data-driven methods supporting design and field adaptation. For light-duty static charging, interoperable pad families, alignment procedures, and mature compensation topologies enable repeatable high-efficiency operation and increasingly standardized validation workflows, supporting early commercial availability. Heavy-duty depot charging appears technically attractive where duty cycles favor opportunity charging and packaging constraints are manageable. Dynamic WPT has reached pilot readiness via segmented selective-energization tracks and coordinated localization and handover, but corridor-scale rollout remains limited by maintainability, seasonal reliability, cost per kilometer, and route and site-specific verification of safety, exposure, and EMC margins.

Future Transportation doi: 10.3390/futuretransp6020076

Authors: Esmail Ahmadi Mohsen Simab Bahman Bahmani-Firouzi

The accelerating integration of electric vehicles (EVs) presents considerable operational challenges for distribution networks, particularly through aggravated voltage deviations and compromised protection coordination during periods of simultaneous charging. In response, this study introduces a novel protection-constrained Binary Evolutionary Algorithm (BEA) designed for expedited electric vehicle-oriented Distribution Network Reconfiguration (DNR) to enhance EV hosting capacity without necessitating costly infrastructure upgrades. The proposed framework uniquely embeds the inverse time–current characteristics of protective fuses—termed Protection Curve Consideration (PCC)—within the optimization process. By explicitly accounting for the thermal inertia of protection devices, the algorithm identifies reconfiguration strategies that uphold voltage stability under elevated EV transportation loading, including configurations typically deemed infeasible by conventional voltage-driven approaches. This selective coordination precludes unnecessary fuse operations, thereby preserving the continuity of electric vehicle charging services. Simulation results on a 16-bus radial distribution system, evaluated under four high-demand scenarios reflective of concentrated EV transportation charging, validate the efficacy of the BEA-PCC methodology. The approach achieves a maximum voltage deviation reduction of up to 15.2%, thereby enhancing power quality for all consumers. Moreover, compared to standard metaheuristic techniques, it reduces Energy Not Supplied (ENS) by 8% and switching operations by 20%, contributing to improved grid resilience and operational efficiency. These outcomes underscore the potential of BEA-PCC as an effective real-time control strategy for distribution system operators seeking to accommodate increasing electric vehicle penetration while safeguarding protection coordination and minimizing customer disruptions.

Future Transportation doi: 10.3390/futuretransp6020075

Authors: Olayinka Bamigbe Aliyu M. Aliyu Ahmed Elseragy Ibrahim M. Albayati

The transition to net-zero energy systems requires operationally grounded decision-making frameworks that integrate technology performance, infrastructure readiness, and policy constraints at local scale. Food transportation represents a high-emission and operationally critical component of regional energy and supply chain systems, particularly in food-producing regions. This study proposes a preliminary Local Area Energy Planning (LAEP) framework to support operational decision-making for the decarbonisation of food transportation, using Lincolnshire, UK, as a case study. The framework evaluates alternative freight transport technologies—battery electric vehicles (BEVs), hydrogen fuel cell electric vehicles (HFCEVs), battery electric road systems (BERS), and conventional internal combustion engine vehicles—across energy efficiency, CO2 emissions, infrastructure requirements, and cost implications. Secondary data from national statistics, regional planning documents, and peer-reviewed literature are analysed using comparative quantitative and qualitative assessment methods. Results indicate that BEVs currently offer the most energy-efficient and cost-effective solution for short-haul and last-mile food logistics, achieving overall efficiencies of approximately 77–82% with zero tailpipe emissions. HFCEVs and BERS present potential long-term operational advantages for heavy-duty and long-haul freight, but remain constrained by high infrastructure investment, energy conversion losses, and system-level costs. The findings highlight the importance of phased technology adoption, renewable energy integration, and infrastructure prioritisation to enable sustainable energy operations in freight transport systems. By embedding technology comparison within a place-based planning framework, this study contributes actionable insights for local authorities, logistics operators, and policymakers seeking to support operational decision-making in sustainable energy systems. The proposed LAEP framework is transferable to other food-producing regions aiming to decarbonise freight transportation while maintaining operational efficiency.

Future Transportation doi: 10.3390/futuretransp6020074

Authors: Mireia Faus Alba Sancho Cristina Esteban Francisco Alonso

Background: Public satisfaction with public transport infrastructure is a factor in the social legitimacy of infrastructure investment policies. Methods: This study analyzes the determinants of citizen satisfaction with toll roads in Mexico using a hierarchical regression model applied to a nationally representative survey. Results: Satisfaction does not depend primarily on sociodemographic factors, but rather on users’ overall perception of the quality, safety, and management of the road system as a whole. Furthermore, the pattern of predictors varies according to usage experience, suggesting that satisfaction is influenced by different factors among users and non-users of these facilities. These findings support a contextual evaluation model, in which citizen assessments are based more on systemic interpretations than on isolated experiences. Conclusions: The study has direct implications for public policy design and infrastructure management in contexts where the use of toll roads responds to structural constraints rather than voluntary decisions. Although the study focuses on the Mexican case, its contributions offer useful interpretative insights for other countries with similar challenges in terms of mobility and institutional legitimacy.

Future Transportation doi: 10.3390/futuretransp6020073

Authors: Yousaf Ali Jing Shi Muhammad Hussain

The China–Pakistan Economic Corridor (CPEC) represents a transformative transport infrastructure initiative with the potential to reshape tourism in South Asia. However, the behavioral mechanisms through which corridor development translate into travel willingness remain insufficiently understood, particularly between domestic and cross-border tourism. This study investigated the determinants of domestic tourism willingness within Pakistan and cross-border tourism willingness toward China using a stated preference survey of 441 Pakistani respondents collected through an online questionnaire. To balance behavioral interpretation and predictive performance, this study integrated ordinal logistic regression (OLR) with multiple machine learning classifiers. The results revealed clear behavioral asymmetries between domestic and cross-border tourism decisions. Domestic tourism willingness was primarily driven by attitudinal evaluations, particularly perceived desirability, pleasantness, and comfort of travel along the CPEC. In contrast, cross-border tourism willingness was more strongly constrained by knowledge-related and institutional factors, including awareness of visa procedures, accommodation arrangements, and destination information. Comparative performance analysis indicated that machine learning models outperformed ordinal logistic regression, improving predictive accuracy by approximately 12.6 percentage points for domestic tourism (93.6% vs. 81.0%) and 1.7 percentage points for cross-border tourism (81.1% vs. 79.4%). These findings demonstrate that corridor-induced tourism demand is governed by distinct behavioral mechanisms across domestic and international contexts, highlighting the need for differentiated tourism development strategies. From a policy perspective, the results suggest that domestic tourism development along the CPEC should prioritize experiential quality and travel comfort, whereas cross-border tourism promotion should focus on reducing informational and procedural barriers such as visa knowledge, accommodation awareness, and travel facilitation.

Future Transportation doi: 10.3390/futuretransp6020072

Authors: Ziyad Shahin Ahmed Mahmoud Darwish Mohamed Shaaban Alfiqi

Informal public transportation remains the backbone of urban mobility in many developing cities. While these systems offer flexible and affordable services, they are often associated with safety issues, unreliability, congestion, and environmental impacts. Consequently, transitioning travelers toward formal public transportation is a key objective for sustainable transport planning. This study investigates travelers’ willingness to shift from their current travel modes to a proposed Metro system in Alexandria, Egypt. The analysis uses stated preference data collected through interviews that presented respondents with multiple service scenarios. A correlated random parameters bivariate probit model with heterogeneity in means is estimated to capture interdependence between responses. The results reveal strong and statistically significant cross-equation error correlations, confirming that decisions are not independent and supporting the use of a joint modeling approach. Empirical results indicate that willingness to shift is influenced by socio-demographic characteristics, trip attributes, and current travel conditions. Female travelers are more sensitive to waiting time, while low-income and older individuals are less likely to shift across scenarios. Physical accessibility, especially walkability to and from stations, emerges as the most influential factor in encouraging adoption. These findings provide policymakers with actionable insights for designing inclusive, accessible, and sustainable public transportation systems.

Future Transportation doi: 10.3390/futuretransp6020071

Authors: Fabián Díaz-Muñoz Xavier Merino-Vivanco

University mobility in medium-sized cities faces increasing challenges arising from traffic congestion, urban sprawl, and the limited availability of sustainable transport options. In this context, the bicycle represents an efficient and environmentally low-impact alternative, provided that safe and connected infrastructure exists to facilitate its adoption. This study assesses the potential for bicycle use in the Andean city of Loja, Ecuador, taking as a case study the university community of the Universidad Técnica Particular de Loja (UTPL). Geographic Information Systems (GIS) tools, origin–destination (OD) matrices, and logistic models were integrated to analyze the relationship between three key variables: terrain slope, minimum travel time, and the percentage of protected cycling infrastructure. The results show that protected cycling infrastructure shows the strongest positive association with the modeled probability of use, while slopes greater than 15% and trips longer than twenty minutes are associated with lower modeled probabilities. The geospatial analysis identified priority corridors where improvements in cycling protection would yield higher modeled modal returns. It is concluded that strengthening cycling connectivity and the continuity of protected routes may inform scenario-based planning to support active university mobility, offering a replicable framework for medium-sized cities with similar topographic conditions.

Future Transportation doi: 10.3390/futuretransp6020070

Authors: Danesh Hosseinpanahi Bo Zou Pooria Choobchian

Freight transportation is a significant contributor to greenhouse gas (GHG) emissions in the US. As an emerging technology, truck platooning leverages vehicle-to-vehicle communications to enable trucks to travel in convoys with close proximity, which reduces air drag and consequently truck fuel use and GHG emissions. However, uncertainties remain about how this emerging technology may be adopted and its climate impacts. To this end, this paper investigates the role of truck platooning adoption in mitigating the climate impact of trucking from a system perspective. Considering the dynamic nature of truck platooning adoption, system dynamics (SD) models based on stock and flow diagrams are developed to estimate the potential reduction in fuel use and CO2 emissions in the US trucking sector when truck platooning technology becomes available. The results show that adopting platooning could save 292 million metric tons of CO2 emissions in 180 months after the initial introduction of the technology in the US truck sector. The analysis provides insights for accelerating truck platooning adoption while enhancing its environmental impact.

Future Transportation doi: 10.3390/futuretransp6020069

Authors: Žarko Rađenović Giannis Adamos Milena Rajić Tamara Rađenović Marko Mančić

Transport is one of the few major sectors in Europe where greenhouse gas emissions have not declined despite tightening climate policy. Road transport remains dominated by fossil fuels, rising travel demand, and growing freight activity. This paper develops a multi-indicator benchmarking framework to assess the extent to which recent road-transport developments in EU-27 Member States align with structural expectations derived from 1.5 °C and 2 °C mitigation pathways. A multi-indicator framework is developed combining emissions and air-quality pressures, system drivers, and urban accessibility for 2019–2023, using harmonized Eurostat, European Environment Agency, WHO, and OECD data. The analysis follows a dual-track design. First, hierarchical agglomerative clustering identifies national transport–climate profiles. Second, PROMETHEE II is applied to generate an outranking-based performance index and country ranking. Five distinct clusters emerge, ranging from carbon-intensive, car-dependent systems with limited electrification and weak accessibility to “sustainability leaders” characterized by lower emissions, higher shares of low-emission vehicles, and strong public-transport accessibility. PROMETHEE results align with this typology: Nordic and north-western countries rank highest, while several southern and eastern countries show negative net flows linked to persistent car dependence, slower fleet transition, and higher pollution exposure. The results suggest that while several countries demonstrate structural progress toward transport decarbonization, none exhibit a performance profile fully consistent with transition patterns associated with 1.5 °C-aligned mitigation pathways.