Future Transp., Volume 6, Issue 2 (April 2026) – 41 articles

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

The integration of truck platooning and electrification presents a promising avenue for improving operational efficiency and environmental sustainability in freight transportation. Realizing the energy and cost saving as well as emission reduction benefits requires a holistic design of truck routing, scheduling, and platooning strategies that account for practical operational constraints. This study investigates the integrated planning problem of routing, scheduling, and platooning for a mixed fleet of conventional trucks (CTs) and electric trucks (ETs), referred to as mixed fleet truck platooning (MFTP) problem. The MFTP incorporates charging scheduling and key operational factors, such as platooning leader–follower positioning under the battery constraints of ETs, charging station availability and capacity, and the positional configuration of trucks within a platoon. The objective is to minimize the total operation cost of the MFTP system, including charging cost, fuel cost, travel labor cost, charging labor cost, and platoon formation labor cost, while ensuring timely arrivals across multiple origin–destination (OD) pairs. The proposed MFTP is formulated as a novel mixed-integer linear program (MILP). Extensive numerical experiments on the simplified Illinois interstate highway network are conducted to examine the effectiveness and efficiency of the proposed model. Numerical results show that incorporating platooning reduces the total operational cost by 7.6% relative to the non-platooning scenario. The findings also shed some light on planning mixed fleets of CTs and ETs with platooning, offering valuable managerial insights for decision-makers. Full article

Pooled rideshare is considered an effective future travel mode for improving vehicle utilization and reducing congestion in urban transportation systems. However, its adoption remains limited due to insufficient passenger acceptance and uncertain economic benefits for transportation network companies (TNCs). The emergence of autonomous vehicles brings new momentum to pooled ridesharing services through centralized fleet management. Nevertheless, most existing studies examine traveler behavior and fleet operations separately, leaving the interaction between passenger preferences and operational strategies insufficiently represented. This study proposed an integrated behavioral–operational framework that jointly considers traveler choice behavior and fleet management decisions. An Integrated Choice and Latent Variable (ICLV) model is estimated using 8296 national survey responses collected in the United States in 2025 to capture post-pandemic traveler attitudes toward pooled rideshare. The behavioral model is embedded into a proactive assignment and repositioning strategy implemented on the POLARIS agent-based simulation platform. Simulation experiments are conducted in two urban networks, Greenville (SC) and Austin (TX), under multiple fleet size scenarios. Results show that the new pooling behavior model significantly increases pooling adoption compared with the previous mixed logit model, indicating that it better captures real-world traveler behavior. And the higher pooling adoption also reshapes the TNC trip structure in Greenville. Compared to the baseline in the POLARIS platform, the integrated framework increases pooling adoption and TNC profitability while reducing VMT, empty seat rates, and overall energy consumption. These findings provide insights for the sustainable deployment of pooled SAV services in heterogeneous urban environments. Full article

The city of Kinshasa faces severe traffic congestion, requiring strategic enhancements to its transport infrastructure capacity. Although a comprehensive transport master plan has been proposed by the Japanese International Cooperation Agency (JICA) report its implementation requires substantial financial investment, which presents a significant challenge for the resource-constrained environment of the Democratic Republic of Congo. This research proposes an indicative optimisation-based network augmentation strategy that accounts for traveller equilibrium behaviour, the primary origin–destination demand patterns, and the underlying network structure. The study formulates the problem as a bilevel Transport Network Design Problem (TNDP) under a construction length budget constraint. Greedy-Simulated Annealing and Greedy-Tabu Search are proposed as the recommended computational search approaches, as they achieved the highest travel time reductions in the experimental study while also demonstrating stable and repeatable solution performance compared with other classical metaheuristic methods commonly used in TNDP research. Greedy Simulated Annealing and Greedy-Tabu Search are proposed as the recommended computational search approaches, as they achieved the highest travel time reductions in the experimental study while also demonstrating stable and repeatable solution performance compared with other classical metaheuristic methods commonly used in TNDP research. The computational experiments indicate a 30% reduction in total travel time, accompanied by a substantial decrease in highly congested links from 52.94% in the baseline network to 3.45% in the optimised design and nearly threefold improvement in edge betweenness centrality for a 100 Km constrained budget. The study further provides recommended new link constructions, together with alternative network redesign solutions that achieve comparable performance improvements. Full article

Urban short-haul cold-chain distribution operates under strict service constraints while facing increasing pressure to reduce carbon emissions under the dual-carbon goals. Existing emission-aware routing studies often treat carbon emissions as external constraints or ex post evaluation indicators, limiting their influence on operational decision making. This study addresses this gap by developing a cold-chain distribution network optimization model that integrates internal carbon pricing (ICP), enabling carbon emissions to be internalized as economic costs within routing and scheduling decisions. Using the student milk cold-chain distribution system serving 54 primary and secondary schools in Fuzhou as an empirical case, the model incorporates multiple cost components, including energy consumption, warehouse operation, carbon emissions, and low-load penalties, while embedding operational constraints such as vehicle capacity, delivery time windows, and minimum economic loading requirements. An improved genetic algorithm is applied to solve the model. Scenario analyses are conducted across carbon price variation and demand fluctuation. Results show that when the internal carbon price increases from 97.49 RMB/t to 2000 RMB/t, the total distribution cost rises from 3531.2 RMB to 4082.842 RMB, indicating that carbon costs become an increasingly important factor in operational decision making. The distribution network exhibits a core-route-dominated structure, with key routes remaining stable across carbon price scenarios, suggesting that the influence of ICP is primarily reflected through cost internalization rather than route substitution. Demand analysis further shows that a 10% demand reduction reduces costs through route consolidation, while a 20% reduction weakens load efficiency and reduces vehicle utilization without triggering low-load penalty costs. These findings demonstrate that integrating ICP into routing optimization provides an effective pathway for aligning operational decisions with low-carbon transition objectives in rigid-demand cold-chain distribution systems. Full article

Accurate environmental perception is fundamental to safe autonomous driving; however, most existing multimodal systems rely on fixed or heuristic sensor fusion strategies that cannot adapt to scene-dependent variations in sensor reliability. This paper proposes Cross-Modal Adaptive Attention (CMAA), a unified end-to-end Bird’s-Eye-View (BEV) perception framework that dynamically fuses camera, LiDAR, and RADAR information through learnable, context-aware modality gating. Unlike static fusion approaches, CMAA adaptively reweights sensor contributions based on global scene descriptors, enabling the robust integration of semantic, geometric, and motion cues without manual tuning. The proposed architecture jointly performs 3D object detection, multi-object tracking, and motion forecasting within a shared BEV representation, preserving spatial alignment across tasks and supporting efficient real-time deployment. Experiments conducted on the official nuScenes validation split demonstrate that CMAA achieves 0.528 mAP and 0.691 NDS, outperforming fixed-weight fusion baselines while maintaining a compact model size and efficient inference. Additional tracking evaluation using the official nuScenes tracking devkit reports improved tracking performance, while motion forecasting experiments show reduced trajectory displacement errors (minADE and minFDE). Ablation studies further confirm the complementary contributions of adaptive modality gating and bidirectional cross-modal refinement, and a stratified dynamic analysis reveals consistent reductions in velocity estimation error across object classes, motion regimes, and environmental conditions. These results demonstrate that adaptive multimodal fusion improves robustness, motion reasoning, and perception reliability in complex traffic environments while remaining computationally efficient for deployment in safety-critical autonomous driving systems. Full article

This paper investigates the influence of rolling resistance on fuel consumption in Class 8 heavy-duty vehicles, with a focus on a modeling approach through variations in the rolling resistance coefficient ($C_{rr}$) across different driving scenarios. Leveraging TruckSim’s multibody modeling approach for vehicle dynamics and MATLAB/Simulink co-simulation capability, the study provides insights into how tire rolling resistance affects energy efficiency under varying conditions while enabling controlled, repeatable comparisons across various scenarios. Results show that across the evaluated scenarios, increases in $C_{rr}$ impact the vehicle’s speed, fuel consumption, engine torque, and crankshaft spin. Specifically, increasing $C_{rr}$ from 0.004 to 0.013 was found to lead up to 68% higher fuel consumption in high demand scenarios. These findings aim to guide efforts to optimize tire design and vehicle performance that help achieve improved fuel efficiency. Full article