Future Transportation

This paper presents the findings of a dual-approach research study conducted for the Utah Department of Transportation to evaluate the effectiveness of variable message sign (VMS) treatments on Utah roadways. The study aimed to determine the impact of VMS treatments on mobility and safety for Utah’s unique road conditions and configurations. Two primary analyses were performed: a diversion rate analysis to assess the effectiveness of VMS messages on Utah freeways during incidents and a weather analysis to evaluate the effectiveness of VMS messages on driver speeds in Utah canyons during winter weather. The findings of the diversion rate analysis indicate that the activation of VMS messages during crash incidents increased diversion rates by 18 percent. The weather analysis showed that the activation of VMS messages warning drivers to slow down due to inclement weather conditions increased driver speeds by 0.23 mph (0.37 kph) during weather conditions, which was statistically significant, but holds little practical significance. The methodologies and findings of this study will assist departments of transportation and other interested parties in developing their VMS policies to more effectively influence driver behaviors. Full article

This study critically examines the technological feasibility, regulatory challenges, and societal acceptance of Pilotless Passenger Aircraft (PPAs) in commercial aviation. A mixed-methods design integrated quantitative passenger surveys (n = 312) and qualitative pilot interviews (n = 15), analyzed using SPSS and NVivo to capture both statistical and thematic perspectives. Results show moderate public awareness (58%) but limited willingness to fly (23%), driven by safety (72%), cybersecurity (64%), and human judgement (60%) concerns. Among pilots, 93% agreed automation improves safety, yet 80% opposed removing human pilots entirely, underscoring reliance on human adaptability in emergencies. Both groups identified regulatory assurance, demonstrable reliability, and human oversight as prerequisites for acceptance. Technologically, this paper synthesizes advances in AI-driven flight management, multi-sensor navigation, and high-integrity control systems, including Airbus’s ATTOL and NASA’s ICAROUS, demonstrating that pilotless flight is technically viable but has yet to achieve the airline-grade reliability target of 10⁻⁹ failures per flight hour. Regulatory analysis of FAA, EASA, and ICAO frameworks reveals maturing but fragmented approaches to certifying learning-enabled systems. Ethical and economic evaluations indicate unresolved accountability, job displacement, and liability issues, with potential 10–15% operational cost savings offset by certification, cybersecurity, and infrastructure expenditures. Integrated findings confirm that PPAs represent a socio-technical challenge rather than a purely engineering problem. This study recommends a phased implementation roadmap: (1) initial deployment in cargo and low-risk missions to accumulate safety data; (2) hybrid human–AI flight models combining automation with continuous human supervision; and (3) harmonized international certification standards enabling eventual passenger operations. Policy implications emphasize explainable-AI integration, workforce reskilling, and transparent public engagement to bridge the trust gap. This study concludes that pilotless aviation will not eliminate the human element but redefine it, achieving autonomy through partnership between human judgement and machine precision to sustain aviation’s uncompromising safety culture. Full article

Urban population growth and expanding economic activity have intensified the demand on transportation networks, resulting in higher traffic volumes, increased spillbacks, and a declining level of service (LOS). Signalized intersections, as critical components, play a vital role in managing urban congestion. This study examines a heavily congested intersection in Miami, Florida, using Highway Capacity Software (HCS7) to assess operational performance and test improvement strategies. The baseline analysis revealed excessive delays, severe queue spillbacks, and LOS F during the PM peak period. Two data-driven scenarios were evaluated: (1) signal timing optimization, and (2) a combined approach involving both optimized timing and a proposed grade-separated pedestrian bridge. Scenario 2 achieved the most significant performance gains by reducing average delays by approximately 53% and improving the intersection’s LOS from F to E. Beyond operational benefits, the pedestrian bridge is supported by crash reduction evidence (CMF), complies with Americans with Disabilities Act (ADA) standards, and promotes long-term urban sustainability. The study’s methodology offers transferable insights for similar urban intersections facing high demand and multimodal conflict. Full article

The continuous tightening of European regulatory requirements, particularly under the General Safety Regulation (GSR), has considerably increased the scope and cost of proving ground testing required for the validation of Advanced Driver Assistance Systems (ADASs) and Automated Driving Systems (ADSs). This study presents a methodology for constructing complex proving ground test scenarios aimed at supporting early-stage functional validation and cost-efficient preparation for type approval. The method is based on the systematic analysis of proving ground–relevant ADAS regulations and the classification of test case variations according to sensing, actuation, and execution complexity. By filtering and combining representative test cases, minimum and maximum complexity scenarios were developed and evaluated on the ZalaZONE proving ground in Hungary. The results demonstrate that the proposed approach can substantially reduce test duration, facility occupancy, and overall validation costs, while maintaining the representativeness and credibility of results. Beyond cost savings, the methodology offers a scalable and practical framework for physical validation, supporting manufacturers in achieving regulatory compliance with reduced time and expenditure. Full article

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