Future Transp., Volume 5, Issue 4 (December 2025) – 74 articles

Vehicle re-identification (re-ID) across disparate sensing modalities remains a fundamental challenge for transportation research. In this work, we introduce a deep multi-view vehicle re-ID framework that leverages Siamese networks to compare pairs of vehicle images and produce matching scores, enabling robust association across drastically different viewpoints such as those from UAVs, surveillance cameras, and ground sensors. The model exploits convolutional neural networks to learn features that remain discriminative under changes in angle, distance, and illumination, supporting more generalizable re-ID performance. As part of this effort, we also developed an automated pipeline to synchronize roadside and UAV video streams, producing a multi-perspective dataset that complements preexisting real collections and a synthetic dataset generated in this study. Together, these contributions advance the capability to re-identify vehicles across wide viewing baselines; establish a foundation for scalable, reproducible research in vehicle re-ID; and open pathways for future applications, such as inferring routine behaviors, movement patterns, and daily habits of the individual associated with the vehicle. Full article

As part of transportation planning processes, simulators are used to mirror real-world situations to test new policies and evaluate infrastructure changes. In practice, simulator selection has often been based on availability rather than on technical suitability, particularly for microscopic-scale applications. In this study, a quantitative methodology focusing on simulation runtime, memory usage, runtime consistency, travel time, safe gap distance, and scalability is proposed. A combined index was developed to assess simulators across different scales and traffic densities. VISSIM, SUMO, and MATSim were tested, and the results indicate that SUMO and MATSim demonstrate strong performance in runtime and memory usage. In large-scale scenarios, both simulators proved suitable for high-demand simulations, with MATSim exhibiting greater scalability. VISSIM matches real-world travel times more closely and fairly handles realistic safe gap distances, making it more suitable for less dense, detailed, microscopic simulations. Full article

The production and sales figures for electric vehicles are showing a steady upward trend, clearly indicating the growing importance of sustainability goals. A unique historical situation has developed in the US: the owner of the leading electric car manufacturer (Tesla), Elon Musk, has taken an active role in political life. Amid a rising trend in electric vehicle (EV) adoption aligned with global sustainability goals, the political activism of Musk has provoked public backlash, including acts of vandalism and aggression toward Tesla vehicles. Using a multidisciplinary approach, the study explores (1) the psychological underpinnings of object-directed violence, (2) the legal classification of politically motivated vandalism, and (3) the broader market implications of corporate politicization. Our findings confirm that object-directed aggression stems from displaced frustration, especially when individuals feel politically powerless or morally outraged. Our analysis revealed that most Tesla-related vandalism will likely be prosecuted as property crimes. Although U.S. officials have labeled some acts as domestic terrorism or hate crimes, legal thresholds are generally not met. Our interdisciplinary model suggests that the politicization of Tesla has broader implications. Tesla’s symbolic status in the electric vehicle market means that attacks on it risk triggering a decline in public trust toward electric mobility. Full article

This paper reviews the role of high-speed rail (HSR) and other fast rail technologies in decarbonising inter-urban transport. It first outlines the global deployment of HSR, with particular emphasis on Europe and China, and situates these networks within the wider geography of fast rail systems. The paper then compares HSR with competing modes such as air transport and passenger cars along key dimensions including door-to-door travel time, energy use and emissions. Building on a qualitative synthesis of the international literature, it discusses the environmental, economic and social impacts of HSR, highlighting conditions under which HSR can deliver substantial modal shift and life-cycle greenhouse gas savings, as well as situations where benefits are more limited or unevenly distributed. Finally, the review briefly considers emerging fast rail concepts such as Maglev and Hyperloop and argues that they should currently be treated as complementary, long-term options rather than immediate substitutes for conventional HSR. Full article

The attractiveness of public transport services plays an important role in urban sustainability as the greater use of public transport reduces individual transport and thereby the amount of congestion, noise, and pollution. However, in order to make public transport more financeable, networks and timetables are often rationalized by minimizing the costs in such a way that the currently assessed travel demands remain served. Although the efficient use of public resources is obviously a matter of public interest, such service rationalization often leads to the public transport network becoming too complicated and difficult for passengers to understand, which worsens the competitiveness of public transport. The question of the applicable service frequencies is also an important component of high-quality services. This paper examines these two major factors by presenting some suitable indicators as well as the feasibility conditions of the recommendations in the relevant literature, focusing on a case study from Győr, Western Hungary. Full article

Accurate prediction of road traffic crash severity is essential for developing data-driven safety strategies and optimizing resource allocation. This study presents a predictive modeling framework that utilizes Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbors (KNN) to estimate segment-level frequencies of fatalities, serious injuries, and slight injuries on Hungarian roadways. The model integrates an extensive array of predictor variables, including roadway geometric design features, traffic volumes, and traffic composition metrics. To address class imbalance, each severity class was modeled using resampled datasets generated via the Synthetic Minority Over-sampling Technique (SMOTE), and model performance was optimized through grid-search cross-validation for hyperparameter optimization. For the prediction of serious- and slight-injury crash counts, the Random Forest (RF) ensemble model demonstrated the most robust performance, consistently attaining test accuracies above 0.91 and coefficient of determination (R²) values exceeding 0.95. In contrast, for fatalities count prediction, the Gradient Boosting (GB) model achieved the highest accuracy (0.95), with an R² value greater than 0.87. Feature importance analysis revealed that heavy vehicle flows consistently dominate crash severity prediction. Horizontal alignment features primarily influenced fatal crashes, while capacity utilization was more relevant for slight and serious injuries, reflecting the roles of geometric design and operational conditions in shaping crash occurrence and severity. The proposed framework demonstrates the effectiveness of machine learning approaches in capturing non-linear relationships within transportation safety data and offers a scalable, interpretable tool to support evidence-based decision-making for targeted safety interventions. Full article

As ports evolve to meet sustainability targets, seamless coordination between road and rail operations becomes fundamental to success. This study addresses the Train Loading Planning Problem (TLPP) which focuses on assigning outbound containers to train wagons under slot, weight, and pattern constraints aiming to examine its broader systemic implications. A compact mixed-integer programming formulation is developed and enhanced through a column-generation approach that efficiently prices feasible wagon plans. The optimization module is embedded within a discrete-event simulation of terminal processes including yard handling, gate operations, and train timetables. The study tests a TLPP-based rail planning algorithm within a DES of terminal and hinterland operations to quantify the impact under realistic variability. Using operational data from the Port of Valencia, realistic planning scenarios are evaluated across varying demand mixes and train frequencies. Results indicate that integrating rail capacity with optimized wagon loading reduces set-up time by 20%, delivery lead time by 54%, container dwell time by 80%, and greenhouse gas emissions by 54% compared with a trucking forwarding baseline, while maintaining throughput and alleviating congestion at terminal gates and yards. From a computational perspective, the column-generation approach achieves improved runtimes to the compact MIP and scales linearly to the number of variables. The proposed framework delivers ready to use load plans and practical insights for the deployment of additional rail capacity, supporting sustainable logistics in port environments. Full article

The transport sector is a major contributor to global greenhouse gas emissions, making its decarbonization critical for climate change mitigation efforts. The Marginal Abatement Cost (MAC) curve is a vital tool that evaluates the cost-effectiveness of mitigation measures by comparing their emission reduction potential against their implementation costs. This paper conducts a literature review to analyze the application of the MAC curve in the transport sector, identifying common mitigation measures, comparing abatement costs, and assessing the tool’s role in shaping decarbonization policies. The findings reveal a predominance of technology-focused, bottom-up methodologies, with a significant research gap in the freight sector, which is largely overlooked compared with passenger transport. The results show that the abatement costs for similar measures vary considerably across geographical contexts, influenced by local factors such as fuel prices and gross domestic product (GDP). The analysis suggests that combining technological solutions with behavioral and structural changes creates synergistic effects, yielding greater benefits than isolated actions. The strong alignment observed between measures analyzed in the literature and subsequent national climate policies confirms the MAC curve’s strategic importance as an evidence-based instrument for policymakers to construct economically rational decarbonization pathways. Full article

Given the undoubtable similarities between the dynamic behavior of the vehicular traffic flow in terms of its response to boundary condition alterations dictated in the form of obstacles, and the specific case of supersonic compressible fluid flow fields, the current manuscript addresses developing a target trajectory for the overtaking maneuver of autonomous vehicles. The path-planning is pursued in analogy to the governing principles of the supersonic compressible fluid flow fields, with the specific definition of a physically meaningful dimensionless group, namely the Traffic Mach number (M_T), which grants the initial access point to the said set of fundamental equations. This practical application is a follow-up to the primarily established proof-of-concept level introduction and analysis of the more general case of collision avoidance for autonomously driven vehicles in accordance with the supersonic compressible fluid flow field, where the Traffic Mach number was first introduced. The proposed trajectory is then taken to the next block of the investigation, namely the tracking and control aspects of the maneuvering vehicle’s dynamics. The path tracking controller is designed based on sliding mode control technique and the algorithm is applied on a 7-DOF simulation model, used for validation and discussion of results. The proposed method is shown to be suitable for overtaking maneuvers of autonomous vehicles, whilst meeting the criteria for a relative velocity from the constant-velocity vehicle ahead of the road in the supersonic regime based on the defined Traffic Mach number. The results are then presented, first, in the scope of the aerodynamics field configuration and their verifications, followed by the vehicle dynamics remarks showing the practicality of the proposed method in terms of vehicle motion. It is observed that the distance corresponding to the delayed maneuver maximizes at highest velocities of the ego vehicle, consistent with the highest M_T values, yet in all simulated cases, the control system of the vehicle model was capable of performing the maneuver based on the assigned trajectories through the present model. Full article

Greenhouse gas (GHG) emissions from rail infrastructure are increasingly examined in response to climate policy demands. Yet current assessment methods, such as ISO-based LCAs, FTIP, “Standardisierte Bewertung”, EN 15804 with c-PCR 023, and EIB’s Climate Proofing, differ substantially in assumptions and comparability. This study investigates the transferability of systematic criteria from semi-quantitative risk assessment as defined in the German pre-standard DIN V VDE V 0831-101 to GHG assessment methods. A two-step analysis was conducted. First, risk assessment criteria, including scope definition, granularity, conservatism, justification, system definition, sensitivity, monotonicity, transparency, calibration, variable interdependency, and result applicability, were reviewed for relevance to GHG assessment. Second, these criteria were applied to existing GHG methods to assess their coverage and identify shortcomings. The findings indicate that many systematic criteria are transferable and are largely fulfilled in LCA-based approaches, although LCAs are often very time and cost-intensive, especially regarding data collection and analysis. Current semi-quantitative frameworks, such as FTIP, lack granularity, justification, and calibration. The results suggest that a semi-quantitative GHG assessment method integrating systematic, legal, and topic-specific requirements could offer a harmonized, transparent, and practical tool for infrastructure planning. Such an approach promises balanced rigor and usability, facilitating more consistent decision-making and comparability across and within projects. Full article

This study presents a systematic literature review of critical risk factors affecting the time and cost performance of highway construction projects. Drawing from 83 peer-reviewed studies across multiple geographic regions, the paper identifies recurrent drivers of project delay and cost overrun in highway construction. The most frequently reported risks include (1) financial constraints, (2) political regulatory issues; (3) land-acquisition and right-of-way delays; (4) design and scope changes; (5) utilities relocation/conflicts; (6) materials and equipment shortages; (7) contractor-related issues; (8) planning and scheduling weaknesses; (9) labour and personnel issues; and (10) weather conditions. These risk factors collectively highlight the importance of robust planning, proactive stakeholder coordination, and the integration of risk-informed decision-making tools. The findings emphasize the need for targeted risk mitigation during early project stages and provide a foundation for refining risk assessment frameworks and future research directions in transport infrastructure development. Full article

This study compares the performance of three YOLO-based object detection models—YOLOv3, YOLOv5, and YOLOv8—for vehicle detection and classification at an urban intersection in Montería, Colombia. Recordings from five consecutive days, spanning three time slots, were used, totaling approximately 135,000 frames with variability in lighting and weather conditions. Frames were preprocessed by maintaining the aspect ratio and were normalized according to each model. The evaluation employed models pre-trained on COCO, without fine-tuning, enabling an objective assessment of their generalization capacity. Precision, recall, F1-score, and mAP@0.5 were computed globally and by vehicle class. YOLOv5 achieved the best balance between precision and recall (F1-score = 0.78) and the highest mAP (0.63), while YOLOv3 showed lower recall and mAP, and YOLOv8 performed competitively but slightly below YOLOv5. Cars and motorcycles were the most robust classes, whereas bicycles and trucks showed greater detection challenges. Visual evaluation confirmed stable performance on cloudy days and in light rain, with reduced accuracy under sunny conditions with high contrast. These findings highlight the potential of modern YOLO architectures for intelligent urban traffic monitoring and management. The generated dataset constitutes a replicable resource for future mobility research in similar contexts. Full article

Crashes on roadways continue to represent a major global public health concern due to high rates of death and injury, underscoring the need for predictive tools that can identify high-risk conditions and guide prevention strategies. This study develops a framework that combines structured crash records and road information with unstructured police narratives to predict injury severity using machine learning and natural language processing (NLP). The dataset is used to train, validate, and test nine models, combining three algorithms (Random Forest, AdaBoost, and XGBoost) with two NLP methods (TF-IDF and Word2Vec). Model performance is evaluated using macro-average F1-scores to address severe class imbalance. Results show that XGBoost with TF-IDF achieves the best performance (macro-F1 = 0.644), demonstrating measurable improvements from incorporating narrative features compared to structured data alone. Beyond prediction, a simulation-based sensitivity analysis is conducted on the top 100 features, identifying 11 variables with the greatest impact on severity outcomes in Kentucky. Seatbelt non-use, occupant entrapment, and impaired driver control emerge as the most influential factors, with simulated improvements leading to notable reductions in fatalities and major injuries. The study introduces a “prediction-to-prevention” framework that links injury severity prediction with simulation-based sensitivity analysis. By integrating structured and narrative crash data, the framework identifies how changes in key behavioral and roadway factors can shift injury outcomes toward less severe levels. These findings highlight the dual contribution of this study: improving predictive accuracy through narrative integration and offering actionable insights to support evidence-based traffic safety interventions. Full article

To address the evolving mobility requirements of local (suburban) and regional public transportation systems, it is imperative to employ service models capable of adapting to low-density and variable demand. This paper develops and tests a practical methodology aimed at identifying regions optimally suited for demand-responsive transport (DRT) and integrating DRT into regional public transport frameworks. At the beginning, a review of DRT system implementation practices in other countries is presented, and an analysis of international public transport macro-models is provided, which reveals structural differences between urban and regional environments. Then, the article describes the development and verification of a public transport macro-model for a selected region. With the help of the model, potential DRT territories in the analyzed region are defined and, using the macro-modeling of the PTV Vissum program, the implementation and results of DRT are evaluated. The fourth section of the article describes the refined methodology for selecting DRT territories and its transferability and parameterization for the wider application in other regions. The proposed methodology integrates multi-criteria spatial assessment, clustering techniques, and service scenario testing to identify low-demand zones, measure accessibility deficiencies, and select DRT designs that are appropriate for specific needs. The results showed that after changing the organization of the public transport service, the total bus mileage decreased from 287,684.18 km per month to 284,078.27 km/month (which is 1.25%), and the total time spent by passengers on trips decreased by 0.5% (the difference is 118 h 11 min). Full article

Urban freight generates a disproportionate share of urban externalities, yet the large-scale integration of eco-friendly vehicles (EFVs) remains limited. Barriers include high capital costs, inadequate charging/refuelling infrastructure, and fragmented governance frameworks. This article examines how regulatory structures and stakeholder alignment shape EFV adoption in Rome, analysing two pilot solutions: (i) a shared hub for electric and hydrogen freight vehicles, and (ii) a cargo-bike programme combining service-trip separation with reverse logistics. The methodological approach integrates a structured review of recent scholarship—organised in line with PRISMA guidance and enriched with bibliometric analysis—with empirical insights from five Living Lab workshops involving logistics providers, energy firms, technology suppliers, and industry associations. The findings highlight that progress depends less on technological capability than on policy mixes matched to stakeholder incentives. For the hub, decisive factors include siting, governance, and scale, while for cargo-bikes, reliability of dispatch, remuneration models, and certified training are critical. The study concludes that Rome’s path to freight decarbonisation requires regulatory and financial packages continuously tailored to actors’ operational priorities and behavioural responses. Full article

Truck platooning refers to a group of heavy-duty vehicles travelling in close succession through cooperative driving technologies and inter-vehicle communication. This transport solution is increasingly investigated as a promising strategy to enhance the efficiency and sustainability of road freight transport. The expected benefits include fuel and operational cost savings, reduced emissions, improved traffic flow and congestion mitigation, as well as enhanced safety for both platoon drivers and surrounding traffic. This paper presents a literature review of truck platooning, with a specific focus on the expected benefits and on how they are addressed across two fundamental perspectives: planning and control. Planning encompasses issues related to platoon formation, maintenance and reconfiguration during transport operations, whereas control focuses on the methods and schemes used to coordinate vehicle behaviour within and between platoons. The reviewed contributions are further analysed according to the methodology adopted, the level of vehicle automation, and the specific control approaches implemented. The resulting classification provides an integrated view of how different research streams contribute to economic, environmental, safety and social benefits. Finally, the current gaps and promising research directions are outlined to support future developments in large-scale platooning deployment. Full article

The increasing global emphasis on sustainable transportation drives the need for strong electric vehicle (EV) charging networks. While national plans set high targets for EV adoption, translating these into practical infrastructure placement poses a significant hurdle. This study tackles this by creating detailed maps to show suitable locations for EV charging stations (EVCS) across the Attica region of Greece. Our main approach combines Geographic Information System (GIS) with Multi-Criteria Decision-Making (MCDM), specifically using the Analytic Hierarchy Process (AHP). After reviewing existing research to find important location factors, we adjusted these to fit the unique urban and social features of metropolitan Athens. We established four main criteria, accessibility, social, energy, and environmental, which were then divided into nine sub-criteria for our analysis. We developed four different models, each applying a unique weighting to these criteria (basic, energy-focused, environmental, and social) to see how various planning goals affect spatial outcomes. These models generated graded suitability maps, highlighting areas with high potential for new infrastructure. Central Athens consistently showed the highest suitability, which matches current research and confirms our method’s reliability. This work provides a useful, repeatable framework for local governments to strategically deploy EVCS, supporting urban planning and helping meet national goals for decarbonization and air quality. Full article

This study applied clustering algorithms to reveal latent structures in 9791 Brazilian civil aviation occurrences recorded from 2007 to 2023. We tested K-means, hierarchical clustering, and K-medoids, using aircraft type, flight phase, and severity as variables in different configurations. The K-medoids method with Manhattan distance produced the best separation. It formed clusters that isolated accidents involving helicopters, ultralights, and critical phases such as takeoff and landing. It also highlighted a specific group of specialized operations. Results confirm that occurrences with similar operational profiles tend to group together, which may help prioritize investigation and prevention actions. The analysis also shows that combining different types of aviation in the same dataset reduces specificity, as heterogeneous operations are mixed. Even so, the findings provide a first overview of safety dynamics in Brazilian civil aviation. The study concludes that clustering can expose latent structures not detected by traditional descriptive analyses and may support the development of more targeted safety policies. Full article

We are witnessing a global commitment to sustainable mobility that requires advanced materials and manufacturing techniques, such as fused filament fabrication (FFF), to create lightweight, durable, and recyclable machine components. Acknowledging that friction and wear significantly contribute to energy loss globally, developing high-performance polymeric materials with customizable properties is essential for greener mechanical systems. FFF inherently drives resource efficiency and offers the geometric freedom necessary to engineer complex internal structures, such as the gyroid pattern, enabling substantial mass reduction. This study evaluates the tribological performance of FFF-printed thermoplastic polyurethane (TPU 82A) specimens fabricated with three distinct gyroid infill densities (10%, 50%, and 100%). Ball-on-disc testing was conducted under dry sliding conditions against a 100Cr6 spherical ball, with a constant normal load of 5 N, resulting in an initial maximum theoretical Hertz contact pressure of 231 MPa, over a total sliding distance of 300 m. Shore A hardness and surface roughness (R_a) were also measured to correlate mechanical and structural characteristics with frictional response. Results reveal a non-monotonic relationship between infill density and friction, with a particular absence of quantifiable mass loss across all samples. The intermediate 50% infill (75.9 ± 1.80 Shore A) exhibited the peak mean friction coefficient of $\overline{\mu }=1.002$ (${\mu }_{\max }=1.057$), which can be attributed to its balanced structural stiffness that promotes localized surface indentation and an increased real contact area during sliding. By contrast, the rigid 100% infill (86.3 ± 1.92 Shore A) yielded the lowest mean friction ($\overline{\mu }$ = 0.465), while the highly compliant 10% infill (44.3 ± 1.94 Shore A) demonstrated viscoelastic energy damping, stabilizing at $\overline{\mu }$ = 0.504. This work highlights the novelty of using FFF gyroid architectures to precisely tune TPU 82A’s tribological behavior, offering design pathways for sustainable mobility. The ability to tailor components for low-friction operations (e.g., μ ≈ 0.465 for bushings) or high-grip requirements (e.g., μ ≈ 1.002 for anti-slip systems) provides eco-efficient solutions for automotive, railway, and micromobility applications, while the exceptional wear resistance supports extended service life and material circularity. Full article

Airport pavement condition assessment plays a critical role in ensuring operational safety, surface functionality, and long-term infrastructure sustainability. Traditional visual inspection methods, although widely used, are increasingly challenged by limitations in accuracy, subjectivity, and scalability. In response, the field has seen a growing adoption of automated and intelligent inspection technologies, incorporating tools such as unmanned aerial vehicles (UAVs), Laser Crack Measurement Systems (LCMS), and machine learning algorithms. This systematic review aims to identify, categorize, and analyze the main technological approaches applied to functional pavement inspections, with a particular focus on surface distress detection. The study examines data collection techniques, processing methods, and validation procedures used in assessing both flexible and rigid airport pavements. Special emphasis is placed on the precision, applicability, and robustness of automated systems in comparison to traditional approaches. The reviewed literature reveals a consistent trend toward greater accuracy and efficiency in systems that integrate deep learning, photogrammetry, and predictive modeling. However, the absence of standardized validation protocols and statistically robust datasets continues to hinder comparability and broader implementation. By mapping existing technologies, identifying methodological gaps, and proposing strategic research directions, this review provides a comprehensive foundation for the development of scalable, data-driven airport pavement management systems. Full article

The rapid advancement of Advanced Driver Assistance Systems (ADAS) is reshaping the future of mobility by offering potential improvements in safety, efficiency, and driving experience, yet consumer acceptance remains uneven across regions. This study addresses the gap in knowledge and trust by examining how Hungarian drivers, as part of the Central and Eastern European context, perceive and adopt ADAS technologies. To achieve this, we conducted two expert in-depth interviews to refine the research instrument, followed by an online survey of 179 drivers. Using k-means cluster analysis, we identified three distinct consumer segments: Conservative Controllers, who demonstrate low levels of trust and willingness to adopt ADAS; Cautious Adopters, who weigh costs and benefits carefully; and Pragmatic Innovators, who are open to experimentation and display the highest acceptance and willingness to pay. The results reveal that awareness and familiarity strongly influence acceptance, highlighting the role of consumer education and transparent communication in shaping adoption. The findings suggest that manufacturers, driving schools, and policymakers can accelerate the diffusion of ADAS by developing targeted strategies tailored to different consumer groups. Strengthening knowledge and trust in these systems will not only support their market success but also contribute to safer, more sustainable transportation. Full article

In the rapidly evolving landscape of logistics and last-mile delivery, optimizing efficiency and minimizing costs are paramount. This paper introduces a novel heuristic approach designed to enhance the efficiency of a truck-and-drone delivery system. Our method addresses the complex challenge of coordinating the movements of a truck, which serves as a mobile depot, and an unmanned aerial vehicle (UAV or drone), which performs rapid, short-distance deliveries. Our system proposes a two-step heuristic. For truck routes, we utilized the Concorde Solver to determine the optimal path, based on real-world road distances between locations in Bacău County, Romania. This data was meticulously collected and processed as a Traveling Salesman Problem (TSP) instance with precise geographical information. Concurrently, a drone is deployed for specific deliveries, with routes calculated using the Haversine formula to determine accurate distances based on geographical coordinates. A crucial aspect of our model is the integration of the drone’s limited autonomy, ensuring that each mission adheres to its operational capacity. Computational experiments conducted on a real-world dataset including 93 localities from Bacău County, Romania, demonstrate the effectiveness of the proposed two-stage heuristic. Compared to the optimal truck-only route, the hybrid truck-and-drone system achieved up to 15.59% cost reduction and 38.69% delivery time savings, depending on the drone’s speed and autonomy parameters. These results confirm that the proposed approach can substantially enhance delivery efficiency in realistic distribution scenarios. Full article

Urban intersections are critical for pedestrian safety, as they usually account for high rates of traffic-related injury and fatalities. This study assesses smart crosswalks as an alternative approach to improve road safety that is inherently aligned with the development of intelligent transportation system technology. After a brief background on this technological advance, this study proceeds with the analysis of long-term crash records from Greek urban roads, concentrating on pedestrians’ behavior in incidents involving road crossing. Thereafter, challenges related to the adoption of an implementation framework are identified. The results confirmed the vulnerability of pedestrians, especially during cases with no specific crossing areas, based on a considerable number of available recorded crashes from a publicly available Greek database. Substantial reductions over the analysis period (i.e., years 2005–2022) in pedestrian-based incidents with injuries and fatalities at a rate of 44% and 52%, respectively, provide evidence-based insights that infrastructural interventions like improved crosswalk design can be translated into measurable benefits for pedestrian safety. Key factors toward a wider applicability framework for even safer interventions through smart crosswalks include maintenance strategies, user education, and systematic integration of funding into urban mobility plans. Full article

This study addresses a critical policy paradox in transport infrastructure planning: the necessity for substantial decarbonization investments amid declining freight demand forecasts in less developed territories. Despite reduced demand, such investments remain justified for advancing sustainability, regulatory compliance, and long-term system resilience. Herein, an integrated decision support framework is developed that optimizes infrastructure investment sequencing while maximizing private capital participation and ensuring technology–regulation alignment. Using comprehensive freight transport data from Latvia (2012–2023), a scenario tree analysis integrated with S-curve technology adoption models is employed to evaluate optimal infrastructure sequencing strategies for hydrogen fuel cell vehicles (HFCVs) and battery electric vehicles (BEVs). The methodology combines Autoregressive Integrated Moving Average (ARIMA) demand forecasting with total cost of ownership (TCO)-based technology adoption curves and hierarchical modal split modeling. The analysis further identifies distinct market segments and adoption trajectories, demonstrating how strategic infrastructure sequencing can accelerate low- and zero-emission technology uptake across different freight distances and policy scenarios. The results demonstrate that strategic sequencing generates net present value (NPV) savings of approximately EUR 18.2 million (at a 4% discount rate) compared to immediate full-scale deployment while maintaining regulatory compliance timelines. The framework provides policymakers with systematic evidence-based criteria for infrastructure investment timing, contributing to the efficient allocation of scarce public resources in the transition to sustainable freight transport. Full article

The first extensive Bus Rapid Transit (BRT) system in the Philippines, the EDSA Busway, was put into place as a result of Metro Manila’s ongoing traffic congestion. This study uses an integrated framework that combines cost–benefit analysis (CBA), commuter perception survey, and traffic simulation to assess its economic, social, and environmental implications. The operational viability and traffic impact of the planned Magallanes BRT station were evaluated through simulation using PTV VISSIM. A total of 385 commuters participated in a survey measuring their impressions of safety, accessibility, and satisfaction using a four-point Likert scale. The Busway’s excellent economic feasibility was confirmed by the CBA results, which showed a Benefit–Cost Ratio (BCR) of 15.38 and a Net Present Value (NPV) of ₱778.64 billion. Results from the simulation showed a 24% decrease in PM₂ emissions, a 75% increase in throughput, and a 64% reduction in bus trip time. According to survey results, 61% of commuters said accessibility had improved and 62% said travel satisfaction had increased. The study supports the EDSA Busway’s status as a feasible model for future BRT expansion in Metro Manila and other emerging metropolitan regions by showing how it greatly improves environmental sustainability and mobility efficiency. Full article

Identifying road surface cracks by semantic segmentation is a difficult problem. This is because segmentation typically detects objects by area, whereas cracks are string-like. Conventional loss functions such as Binary Cross-Entropy (BCE), Dice, and IoU often fail to capture the fine, elongated features of cracks, as they rely on pixel-level, area-based overlap, leading to suboptimal performance. To address this, we investigate one of the skeleton-based losses, the Centerline Dice (clDice) loss, which emphasizes the preservation of tubular structures via soft skeletonization. We improve road crack segmentation by combining clDice with conventional loss functions, systematically evaluating its role by varying the weight parameter and skeletonization iterations. Experiments are conducted on the EdmCrack600 and CrackForest datasets using two segmentation models: a customized CNN-based U-Net++ and a transformer-based SegFormer. Performance is evaluated using the Dice coefficient, IoU, clDice, and Hausdorff Distance. Results show that combining clDice and IoU loss with customized U-Net++ achieves superior performance. Compared to a standard BCE baseline, it improves the Dice coefficient by 4.9 and 2.8 percentage points on EdmCrack600 and CrackForest and improves the clDice score by 3.9 and 1.7 percentage points. These results highlight improved segmentation of thin, linear cracks, supporting practical advancements in road monitoring and segmentation of linear structures. Full article

Peru faces critical challenges in the development and maintenance of its national road infrastructure, comprising over 32,000 km, of which only 26% are classified as being in good condition. This infrastructural deficit significantly elevates logistics costs and undermines national competitiveness, particularly in key sectors such as agriculture and mining. In this context, improving the accuracy and reliability of cost estimation in road infrastructure projects is imperative to optimize resource allocation and mitigate the risk of cost overruns. This study proposes a stochastic cost estimation framework that integrates Monte Carlo simulation with correlation matrices, enabling the modeling of uncertainty and the complex interdependencies among critical cost drivers. The methodology was applied to the Oyon Ambo highway in Peru. Historical input cost databases were analyzed to define probabilistic distributions, and correlation coefficients were employed to represent the dependencies between variables such as material prices, labor productivity, and equipment efficiency. The stochastic model produced probabilistic cost forecasts with associated confidence intervals and quantified risk exposure. The findings demonstrate that the proposed integrated approach significantly enhances the precision and robustness of cost estimates, providing project managers and decision-makers with a rigorous, data-driven tool for risk-informed budgeting and strategic financial planning in complex infrastructure projects. Full article

The Faster Region-based Convolutional Network (Faster R-CNN) is an efficient object detection model. However, its large size and significant computational requirements limit its applicability in embedded systems and real-time environments. Quantization is a proven method for reducing models’ size and computational requirements, but there is currently no open-source general implementation for quantizing Faster R-CNN. The main reason is that individual architecture components need to be quantized separately due to their structural characteristics. We present a general Faster R-CNN quantization algorithm, for which our implementation is open-source and compatible with the PyTorch (2.7.0+cu126, pt12) ecosystem. Our solution reduces the model size by 67.2% and the detection time by 50.4% while maintaining the accuracy measured on the test data within an error margin of 8.2% and a standard deviation of ±3.4%. It also allows for the visualization of model errors by extracting the model’s internal activation maps, supporting a more efficient understanding of its behavior. We demonstrate that the proposed method can effectively quantize Faster R-CNN, enabling the model to run on low-power hardware. This is particularly important in applications such as autonomous vehicles, embedded sensor systems, and real-time security surveillance, where fast and energy-efficient object detection is crucial. Full article

Globally, the rise in car ownership and usage has intensified parking challenges, particularly within central business districts (CBDs) of many developed cities. Scarce parking infrastructure and escalating land values have further exacerbated these issues, leading to heightened competition among business owners, residents, shoppers, and clients for the limited available paid and free on-street parking spaces. Against this backdrop, the present study sought to model the determinants of motorists’ parking challenges using a recursive structural equation model (RSEM), drawing on empirical evidence from Greater Kumasi, Ghana. Primary data were collected through a structured survey involving 1000 drivers within the designated catchment area, employing cluster and systematic sampling techniques to ensure representativeness. The findings reveal that four out of five structural paths of the constructs exerted significant influences on the structural model components. Both time-related indices and parking costs demonstrated direct and indirect effects on parking challenges, with vehicle type serving as a mediating variable. Furthermore, most of the measurement models significantly impacted the latent factors, either positively or negatively, highlighting the complex interrelationships between parking behavior and underlying determinants. Overall, this study makes several contributions: it provides localized empirical evidence from a developing-country context, offers theoretical refinements to existing models, demonstrates methodological rigor through the application of RSEM, and proposes practical policy insights to address urban parking challenges in rapidly growing African cities such as Kumasi. Full article

There have been many attempts to model the flow of vehicular traffic in analogy to the flow of fluids. Given the evident change in distance between vehicles driving in platoons, the compressibility of traffic flow is inferred and, considering the reaction time-scales of the driver (human or autonomous), it is argued that this compressibility is increased as relative velocities increase—giving the lag in imposed redirection by the driver and the controller units a higher relative importance. Therefore, a supersonic compressible flow field has been opted for as the most analogous base flow. On this point, added to by the overall extreme similarities of the two above-mentioned flows, the non-dimensional group of the traffic Mach number M_T has been defined in the present research, providing the possibility of calculating a suggested flow field and its corresponding shockwave systems, for any given obstacle ahead of the traffic flow. This suggested flow field is then taken as the basis to obtain trajectories designed for avoiding collision with the obstacle, and in compliance with the physics of the underlying analogous fluid flow phenomena, namely the internal supersonic compressible flow around a double wedge. It should be noted that herein we do not model the traffic flow but propose these trajectories for more optimal collision avoidance, and therefore the above-mentioned similarities (explained in detail in the manuscript) suffice, without the need to rely on full analogies between the two flows. The manuscript further analyzes the applicability of the proposed analogy in the path-planning process for an autonomous passenger vehicle, through dynamics and control of a full-planar vehicle model with an autonomous path-tracking controller. Simulations are performed using realistic vehicle parameters and the results show that the fluid flow analogy is compatible with the vehicle dynamics, as it is able to follow the target path generated by fluid flow calculations with minor deviations. Simulation results demonstrate that the proposed method produces smooth and dynamically consistent trajectories that remain stable under varying traffic scenarios. The controller achieves accurate path tracking and rapid convergence, confirming the feasibility of the fluid-flow analogy for real-time vehicle control. Full article