Future Transp., Volume 5, Issue 3 (September 2025) – 41 articles

As the realization of fully autonomous driving becomes increasingly plausible, its rapid development raises serious privacy concerns. At present, while personal information of passengers and pedestrians is routinely collected, its purpose and usage history are rarely disclosed, and pedestrians in particular are effectively deprived of any meaningful control over their privacy. Furthermore, no institutional framework exists to prevent the misuse or abuse of such data by authorized insiders. This study proposes the application of a novel privacy protection framework—Verifiable Record of AI Output (VRAIO)—to autonomous driving systems. VRAIO encloses the entire AI system behind an output firewall, and an independent entity, referred to as the Recorder, conducts purpose-compliance screening for all outputs. The reasoning behind each decision is recorded in an immutable and publicly auditable format. In addition, institutional deterrence is enhanced through penalties for violations and reward systems for whistleblowers. Focusing exclusively on outputs rather than input anonymization or interpretability of internal AI processes, VRAIO aims to reconcile privacy protection with technical efficiency. This study further introduces two complementary mechanisms to meet the real-time operational demands of autonomous driving: (1) pre-approval for designated outputs and (2) unrestricted approval of internal system communication. This framework presents a new institutional model that may serve as a foundation for ensuring democratic acceptance of fully autonomous driving systems. Full article

This paper presents a comprehensive analysis of the impact of the road cross-section configuration and junction types on traffic safety along selected bypasses in Poland. The study evaluates the safety implications of different design alternatives based on empirical data collected from 18 bypasses, comprising both single and dual carriageway sections. The findings indicate that, although single-carriageway bypasses exhibit a lower absolute number of accidents and collisions, they demonstrate a higher concentration of such accidents or collisions compared to dual-lane bypasses. Furthermore, the severity of accidents is significantly higher on single-carriageway bypasses. The analysis also reveals that the majority of collisions, irrespective of carriageway type, occur at at-grade junctions and roundabouts, with only 20% occurring at grade-separated interchanges. This suggests that the implementation of grade-separated interchanges contributes to enhanced road safety outcomes. Additionally, the study concludes that, within the context of ring roads, roundabouts offer a safer alternative to traditional at-grade junctions. Full article

This study investigates the impact of railway transfer penalties on the demographic structure of the Tokyo Metropolitan Area. While previous research has emphasized travel time to the city center as a key determinant of socio-demographic structure, this paper highlights the additional influence of transfer penalties—specifically walking and waiting times—on urban demographic patterns. Using 1 km grids as the unit of analysis, travel time to Tokyo Station is calculated as a measure of accessibility, and the difference in travel time with and without accounting for transfers is defined as the transfer penalty for each grid. The spatial distribution of these penalties is mapped, and their effects on the population are estimated while considering heterogeneity based on distance to the city center. The results indicate that beyond accessibility, higher transfer penalties are associated with lower population densities. Moreover, the negative impact of transfer penalties is observed only in areas located at an intermediate distance from the city center (approximately 26–46 km). Finally, incorporating this spatial heterogeneity, the paper visualizes the projected contribution of transfer penalties to future population distribution. Full article

Using the 2022 National Household Travel Survey data, this study explores the socioeconomic and demographic factors influencing vehicle miles traveled and driving decisions of Americans with travel-limiting disabilities. It employs descriptive statistics, independent sample t-tests, multiple linear regression, and logistic regression. The study finds a higher prevalence of travel-limiting disabilities in urban areas compared to rural areas, and the prevalence of travel-limiting disabilities increases with age. The study also finds evidence of statistically significant differences in the means of trip-related factors for Americans with travel-limiting disabilities across geographic locations with higher vehicle miles traveled, total trip miles, and number of vehicles in households in rural areas compared to urban areas. Across genders, males drive higher total trip miles and represent a higher proportion of drivers in households, while females have higher vehicle miles traveled. The study concludes that in rural areas, there are higher group means for trip-related variables, there are more females with travel-limiting disabilities, the higher the age, the higher the prevalence of travel-limiting disabilities, and age and number of drivers in the household are the two common significant predictors of vehicle miles traveled and driving decisions of Americans with travel-limiting disabilities. Full article

In this paper, we develop a dynamic pricing mechanism for a meal delivery platform that offers multiple transportation modes for order deliveries. We consider orders from heterogeneous customers who select their preferred delivery mode based on individual generalized cost (GC) functions, where GC captures the trade-off between price and delivery latency for each transportation option. Given the logistics of the underlying transportation network, the platform can utilize a pricing mechanism to guide customer choices toward delivery modes that optimize resource allocation across available transportation modalities. By accounting for variability in the latency and cost of modalities, such pricing aligns customer preferences with the platform’s operational objectives and enhances overall satisfaction. Due to the computational complexity of finding the optimal policy, we adopt a deep reinforcement learning (DRL) approach to design the pricing mechanism. Our numerical results demonstrate up to $143\%$ higher profits compared to heuristic pricing strategies, highlighting the potential of DRL-based dynamic pricing to improve profitability, resource efficiency, and service quality in on-demand delivery services. Full article

Strategic fleet renewal represents a fundamental challenge for small and medium-sized enterprises (SMEs) and public entities seeking to align their operational objectives with sustainable mobility practices. This paper proposes a hybrid decision support model based on fuzzy logic, combining the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method with the Fleet Renewal Priority Index (FRPI). The model evaluates and prioritizes different vehicle alternatives based on multiple economic, environmental, and operational criteria, including total cost of operation, CO₂ emissions, maintenance, autonomy, infrastructure compatibility, and energy independence. The criteria are evaluated by linguistic judgments converted into triangular fuzzy numbers (TFN), allowing uncertainty and subjectivity to be addressed. A simulated case study illustrates the application of the model, identifying the vehicles most aligned with a sustainability and efficiency strategy, as well as those that present a greater urgency for replacement. The results demonstrate the potential of the approach to support rational, transparent and sustainable decisions in fleet modernization. Full article

With the increasing digitalization and connectivity of transportation systems, there are many opportunities for data-based approaches in transportation planning and management. In this context, data quality management has a special role to play, including the systematic quality assessment of data assets. Data quality is particularly crucial for emerging data that has not yet been widely researched from a quality perspective. Emerging data is often found in Smart Parking Systems (SPSs). Currently, it remains unclear how SPS-generated data can be exploited by potential data consumers, such as municipal parking managers. One reason is the lack of knowledge about the quality of available data sources and the data provided. This paper presents an approach to assessing and defining data quality in the field of on-street parking. It examines relevant quality issues in this field and consolidates the findings into relevant quality indicators. The methodology includes a cross-check analysis of data sources and an inductive taxonomy development. The cross-check analysis provided empirical findings through qualitative analyses of available parking data in Hamburg, Germany, considering various conventional and SPS-based data sources. Based on this, a set of relevant quality criteria and quality metrics was developed. Full article

This paper explores how modular construction can enhance airport sustainability, highlighting example applications at US airports. Whereas traditional construction builds sequentially on-site, modular construction (also known as prefabricated, off-site and industrialized construction) utilizes prefabricated modules that are built off-site, transported, and integrated into the final structure on-site. Modular construction shifts activities to the fabrication site, accelerating the construction schedule on-site and reducing disruptions to airport operations. Modular construction also supports airport sustainability, which encompasses operations, economic, environment and community impacts. Modular construction is increasingly utilized at airports due to its significant advantages: (1) minimizing disruption to airport operations, supporting operations; (2) accelerating on-site construction schedules by shifting activities to the fabrication site, supporting economic and operations components; (3) reducing issues with airport security, construction noise and disruption by moving module construction to the fabrication site, supporting all components of sustainability; and (4) increasing safety for construction workers, passengers, and airport workers, supporting the community component of sustainability. Although modular construction is increasingly common at airports, there is little documentation of its use in the scholarly literature, and even less discussion of the benefits of modular construction for airport sustainability. This paper addresses that gap by documenting modular construction activities at US airports and identifying how these projects contribute to airport sustainability. Full article

Electric Level 4 connected automated vehicles (CAVs) are now allowed to demonstrate their automation capability in shared mobility robotaxi and microtransit services in geofenced areas in several cities around the world. Private and public sector stake-holders need predictions of their adoption without regulatory constraints for personal mobility and use in shared mobility services. In anticipation of the future presence of CAVs in transportation vehicle fleets, governments are planning necessary regulatory and infrastructure changes. Accompanying this need for forecasts is the acknowledgement that CAV adoption decisions must be made under uncertain states of technology and infrastructure readiness. This paper presents a Bayesian predictive modelling framework for electric Level 4 CAV adoption in the 2030–2035 application context. The inputs to the Bayesian model are obtained from effectiveness estimates of CAV applications that are processed with the Monte Carlo method to account for uncertainties in these estimates. Scenarios of CAV adoption in the 2030–2035 period are analyzed using the Bayesian model, including the quantification of the value of new information obtainable from demonstration studies intended to reduce uncertainties in technology and infrastructure readiness. The results show that in the 2030–2035 application context, the CAVs are likely to be adopted, provided that the trajectory of progress in technology and infrastructure readiness continues, and potential adopters are offered opportunities to learn about Level 4 CAV technological capabilities in a real life service environment. The threshold level of the probability of adoption enhances significantly with high-reliability demonstration results that can reduce uncertainties in adoption decisions. The findings of this research can be used by private and public sector interest groups. Full article

Reducing carbon dioxide emissions in freight transportation is considered a key objective in contemporary sustainable supply chain management. While several tools and standards have been developed to estimate transport-related emissions, most rely on static assumptions, generic emission factors and are limited to single-scenario evaluation. Therefore, their operational applicability remains restricted especially in dynamic and complex environments where fast responsiveness is essential. Moreover, these tools are often disconnected from real-world constraints and rarely incorporate expert’s input. To address this gap, this study introduces a hybrid decision-support CO₂ assessment framework combining theoretical models with field-based inputs. The proposed approach combines structured interviews conducted with 300 supply chain consultants and is operationalized through a dynamic digital tool that enables users to simulate multiple scenarios simultaneously. The tool accounts for critical variables including transport mode, routing distance, vehicle configuration, and shipment characteristics, thereby enabling a contextualized and flexible analysis of carbon emissions. A validation case study was conducted to confirm the applicability of the tool to industrial settings. Computational results show significant variation in emissions across different routing strategies and modal configurations, highlighting the tool’s capacity to support environmentally informed decisions. This research offers both a replicable methodology and a practical contribution: a user-centered, multi-scenario tool that improves the accuracy, adaptability, and strategic value of CO₂ emission calculations in freight transport planning. Full article

The growing complexity of urban mobility necessitates more adaptive, data-driven approaches to transport demand forecasting. This study incorporates anonymized Call Detail Record (CDR) data—originally collected for mobile network billing—into the conventional four-step travel demand model to more accurately estimate trip behavior. Employing a stay-based method, significant user locations are identified, and individual mobility patterns are reconstructed. These patterns are then aggregated at the zonal level and validated against a large-scale household survey conducted in Sri Lanka. The proposed framework enables the extraction of origin–destination matrices and supports route assignment using CDR data, demonstrating a strong correlation with traditional survey results. This research highlights the potential of repurposed CDR data as a scalable, cost-efficient alternative to conventional travel surveys for estimating travel demand. Full article

This paper presents the design and implementation of a high-precision vehicle detection and classification system for toll stations on national highways in Colombia, leveraging LiDAR-based 3D point cloud processing and supervised machine learning. The system integrates a multi-sensor architecture, including a LiDAR scanner, high-resolution cameras, and Doppler radars, with an embedded computing platform for real-time processing and on-site inference. The methodology covers data preprocessing, feature extraction, descriptor encoding, and classification using Support Vector Machines. The system supports eight vehicular categories established by national regulations, which present significant challenges due to the need to differentiate categories by axle count, the presence of lifted axles, and vehicle usage. These distinctions affect toll fees and require a classification strategy beyond geometric profiling. The system achieves $89.9\%$ overall classification accuracy, including $96.2\%$ for light vehicles and $99.0\%$ for vehicles with three or more axles. It also incorporates license plate recognition for complete vehicle traceability. The system was deployed at an operational toll station and has run continuously under real traffic and environmental conditions for over eighteen months. This framework represents a robust, scalable, and strategic technological component within Intelligent Transportation Systems and contributes to data-driven decision-making for road management and toll operations. Full article

Personal Rapid Transit (PRT) and Group Rapid Transit (GRT) are classes of fully automated public transport systems, where passengers can travel in small vehicles on an interconnected, grade-separated network of guideways, non-stop, from origin to destination. PRT and GRT are considered sustainable as they are low-emission and able to attract car drivers. The parameterized cost modeling framework developed in this paper has the advantage that profitability of different PRT/GRT systems can be rapidly verified in a transparent way and in function of a variety of relevant system parameters. This framework may contribute to a more transparent, rapid, and low-cost evaluation of PRT/GRT schemes for planning and decision-making purposes. The main innovation is the introduction of the “peak hour network saturation” S: the number of vehicles in circulation during peak hour divided by the maximum number of vehicles running at line speed with minimum time headways. It is an index that aggregates the main uncertainties in the planning process, namely the demand level relative to the supply level. Furthermore, a maximum S can be estimated for a PRT/GRT project, even without a detailed demand estimation. The profit per trip is analytically derived based on S and a series of more certain parameters, such as fares, capital and maintenance costs, daily demand curve, empty vehicle share, and physical properties of the system. To demonstrate the ability of the framework to analyze profitability in function of various parameters, we apply the methods to a single vehicle PRT, a platooned PRT, and a mixed PRT/GRT. The results show that PRT services with trip length proportional fares could be profitable already for $S>0.25$. The PRT capacity, profitability, and robustness to tripled infrastructure costs can be increased by vehicle platooning or GRT service during peak hours. Full article

While there are studies on bicycle crashes, no study has investigated the spatial analysis of fatal and injury bicycle crashes in the state of Ohio. This study fills this gap in the literature by mapping and investigating the bicycle crash-prone areas in the state. It analyzes fatal and injury bicycle crashes from 2014 to 2023 by utilizing four exploratory spatial data analysis techniques: nearest neighbor index, global Moran’s I index, hotspot and cold spot analysis, and local Moran’s I index at the state, county, census tract, and block group levels. Results vary slightly across techniques and spatial scales but consistently show that bicycle crash locations are clustered statewide, particularly in the state’s major metropolitan areas such as Columbus, Cincinnati, Toledo, Cleveland, and Akron. These urban centers have emerged as hotspots, indicating a higher vulnerability to bicycle crashes. While global Moran’s I analysis at the county level does not reveal significant spatial autocorrelation, a strong positive autocorrelation is observed at both the census tract (p = 0.01) and block group (p = 0.00) levels, indicating significant high clustering, signifying that finer geographical units yield more robust results. Identifying specific hotspots and vulnerable areas provides valuable insights for policymakers and urban planners to implement effective safety measures and improve conditions for non-motorized road users in Ohio. The study highlights the need for targeted mitigation strategies in high-risk areas, including comprehensive safety measures, infrastructure improvements, policy changes, and community-focused initiatives to reduce crash risk and create safer environments for cyclists throughout Ohio’s urban fabric. Full article

Motor vehicle crashes are a persistent cause of unintentional deaths in the United States. Scholarship on how manmade interventions and natural phenomena interact to effectuate such calamitous outcomes is longstanding. One manmade intervention of interest in the literature is daylight saving time (DST). Unfortunately, results on how the natural phenomena attributable to DST interact with driver behavior are inconsistent. To advance knowledge on DST-safety interactions, this study adopts a multilevel model approach to fatal motor vehicle crash outcomes in the contiguous United States. Results from a national analysis contextualize results from zonal analyses to unmask within- and between-time zone differences in DST-safety interactions. In the national analysis, motor vehicle crash fatalities decrease somewhat during DST (−0.10%). In the zonal analyses, motor vehicle crash fatalities decrease more so in the Central and Eastern time zones (−2.00% and −2.00%, respectively), but increase somewhat in the Pacific and Mountain time zones (+0.30%) during DST. The spatiotemporal context of the national analysis highlights specific policy implications from the zonal analyses to decrease the lethality of motor vehicle crashes. Specifically, interdictions to target alcohol and/or drug involvement in the northern latitudes of the Pacific and Mountain time zones during DST, the Central time zone at dawn or dusk before or after DST, and the northern latitudes in the Eastern time zone before or after DST are important. Generally, national DST-safety benefits mask zonal DST-safety costs in the Pacific and Mountain time zones. Full article

Urban transport is a key driver of greenhouse gas emissions in Europe, making its decarbonization essential to achieving EU climate neutrality targets. This study examines how European strategies, such as the Green Deal, the Sustainable and Smart Mobility Strategy, and the Fit for 55 package, are reflected in Romania’s transport policies, with a focus on implementation challenges and urban outcomes in Bucharest. By combining policy analysis, stakeholder mapping, and comparative mobility indicators, the paper critically assesses Bucharest’s current reliance on private vehicles, underperforming public transport satisfaction, and limited progress on active mobility. The study develops a context-sensitive reform framework for the Romanian capital, grounded in transferable lessons from Western and Central European cities. It emphasizes coordinated metropolitan governance, public trust-building, phased car-restraint measures, and investment alignment as key levers. Rather than merely cataloguing policy intentions, the paper offers practical recommendations informed by systemic governance barriers and public attitudes. The findings will contribute to academic debates on urban mobility transitions in post-socialist cities and provide actionable insights for policymakers seeking to operationalize EU decarbonization goals at the metropolitan scale. Full article

The structural design of rail vehicle bodies significantly influences rail vehicle performance, passenger comfort, and operational efficiency. This study presents a comparative analysis of three key concepts of a rail vehicle body, namely a differential, an integral, and a hybrid structure, with a focus on their structural principles, material utilization, and implications for manufacturability and maintenance. Three rail vehicle body variants were developed, each incorporating a low-floor configuration to enhance accessibility and interior layout flexibility. The research explores the suitable placement of technical components such as a power unit and an air-conditioning system, and it evaluates interior layouts aimed at maximizing both passenger capacity and their travelling comfort. Key features, including door and window technologies, thermal comfort solutions, and seating arrangements, are also analyzed. The study emphasizes the importance of compromises between structural stiffness, reparability, production complexity, and passenger-oriented design considerations. A part of the research includes a proposal of three variants of a rail vehicle body frame, together with their strength analysis by means of the finite element method. These analyses identified that the maximal permissible stresses for the individual versions of the frame were not exceeded. Findings contribute to the development of more efficient, accessible, and sustainable regional passenger rail vehicles. Full article

Previous reviews have examined specific facets of Geographic Information Systems (GIS) in transportation planning, such as transit-focused applications and open source geospatial tools. However, this study offers the first systematic, PRISMA-guided longitudinal evaluation of GIS integration in transportation planning, spanning thematic domains, data models, methodologies, and outcomes from 2004 to 2024. This study addresses this gap through a longitudinal analysis of GIS-based transportation research from 2004 to 2024, adhering to PRISMA guidelines. By conducting a mixed-methods analysis of 241 peer-reviewed articles, this study delineates major trends, such as increased emphasis on sustainability, equity, stakeholder involvement, and the incorporation of advanced technologies. Prominent domains include land use–transportation coordination, accessibility, artificial intelligence, real-time monitoring, and policy evaluation. Expanded data sources, such as real-time sensor feeds and 3D models, alongside sophisticated modeling techniques, enable evidence-based, multifaceted decision-making. However, challenges like data limitations, ethical concerns, and the need for specialized expertise persist, particularly in developing regions. Future geospatial innovations should prioritize the responsible adoption of emerging technologies, inclusive capacity building, and environmental justice to foster equitable and efficient transportation systems. This review highlights GIS’s evolution from a supplementary tool to a cornerstone of data-driven, sustainable urban mobility planning, offering insights for researchers, practitioners, and policymakers to advance transportation strategies that align with equity and sustainability goals. Full article

This work proposes a new method to refill fuel cell electric vehicle hydrogen tanks from a storage system in hydrogen refueling stations. The new method uses the storage tanks in cascade to supply hydrogen to the refueling station dispensers. This method reduces the hydrogen compressor power requirement and the energy consumption for refilling the vehicle tank; therefore, the proposed alternative design for hydrogen refueling stations is feasible and compatible with low-intensity renewable energy sources like solar photovoltaic, wind farms, or micro-hydro plants. Additionally, the cascade method supplies higher pressure to the dispenser throughout the day, thus reducing the refueling time for specific vehicle driving ranges. The simulation shows that the energy saving using the cascade method achieves 9% to 45%, depending on the vehicle attendance. The hydrogen refueling station design supports a daily vehicle attendance of 9 to 36 with a complete refueling process coverage. The carried-out simulation proves that the vehicle tank achieves the maximum attainable pressure of 700 bars with a storage system of six tanks. The data analysis shows that the daily hourly hydrogen demand follows a sinusoidal function, providing a practical tool to predict the hydrogen demand for any vehicle attendance, allowing the planners and station designers to resize the elements to fulfill the new requirements. The proposed system is also applicable to hydrogen ICE vehicles. Full article

Transportation is often cited as a significant barrier to healthcare access by Veterans, particularly those from minority groups, who have disabilities, or live in rural areas. Autonomous shuttles (AS) offer a potential solution, yet limited research has explored Veterans’ experiences with this technology. This study qualitatively investigated Veterans’ lived experiences with AS through focus groups, enrolling participants aged 18+ from Gainesville, The Villages, and Lake Nona, Florida. Via a directed content analysis, six key themes were identified: Perceived Benefits, Safety, Experience with Autonomous Vehicles (AV), AS Experience, AV Adoption, and Perception Change. Among 26 participants (aged 30–85; 77% men; 88% urban residents), prominent themes included Safety (n = 161), Perceived Benefits (n = 153), and AS Experience (n = 118), with predominantly positive counts in all themes except AS Experience. Participants acknowledged safety advantages and multitasking potential of AS over human-operated vehicles while recommending improvements to the shuttle’s slow speed, availability and convenience. While the AS ride was positively received overall, some participants noted issues with comfort and braking, emphasizing the need for further technological enhancements. Real-world exposure to AS appeared to influence acceptance positively, offering insights for policymakers and industry stakeholders aiming to optimize AS deployment for mobility-vulnerable Veterans. Full article

Urban areas are facing increasing traffic congestion, pollution, and infrastructure strain. Traditional urban transportation systems are often fragmented. They require users to plan, pay, and travel across multiple disconnected services. Mobility-as-a-Service (MaaS) integrates these services into a single digital platform, simplifying access and improving the user experience. This review critically examines the role of MaaS in fostering sustainable mobility ecosystems. MaaS aims to enhance user-friendliness, service variety, and sustainability by adopting a customer-centric approach to transportation. The findings reveal that successful MaaS systems consistently align with multimodal transport infrastructure, equitable access policies, and strong public-private partnerships. MaaS enhances the management of routes and traffic, effectively mitigating delays and congestion while concurrently reducing energy consumption and fuel usage. In this study, the authors examine MaaS as a new mobility paradigm for a sustainable transportation system in smart cities, observing the challenges and opportunities associated with its implementation. To assess the environmental impact, a sustainability index is calculated based on the use of different modes of transportation. Significant findings indicate that MaaS systems are proliferating in both quantity and complexity, increasingly integrating capabilities such as real-time multimodal planning, dynamic pricing, and personalized user profiles. Full article

We introduce a waypoint-based flow capture location model (WbFCLM) for siting facilities on networks with the objective of maximally capturing flows. The advantages of the waypoint-based formulation are that (1) demand is modeled along observed trajectories rather than assumed travel paths, and (2) demand is allowed to vary across geographic space. We demonstrate the WbFCLM using a test dataset derived from vehicle tracking data. Though solving the WbFCLM can require considerable computing power, it can be used to site facilities on networks where both flows and demand vary spatially. Full article

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems. Full article

This study investigates the factors influencing school trip mode choice among senior high school students in the Chiang Rai urban area, Chiang Rai, Thailand, under normal and adverse weather conditions. Utilizing data from 472 students across six extra-large urban schools, a Multinomial Logit (MNL) regression model was applied to examine the effects of socio-demographic attributes, household vehicle ownership, travel distance, and spatial variables on mode selection. The results revealed notable modal shifts during adverse weather, with motorcycle usage decreasing and private vehicle reliance increasing, while school bus usage remained stable, highlighting its role as a resilient transport option. Car ownership emerged as a strong enabler of modal flexibility, whereas students with limited access to private transport demonstrated reduced adaptability. Additionally, increased waiting and travel times during adverse conditions underscored infrastructure and service vulnerabilities, particularly for mid-distance travelers. The findings suggest an urgent need for transport policies that promote inclusive and climate-resilient mobility systems, particularly in the context of Chiang Rai, including expanded school bus services, improved first-mile connectivity, and enhanced pedestrian infrastructure. This study contributes to the literature by addressing environmental variability in school travel behavior and offers actionable insights for sustainable transport planning in secondary cities and border regions. Full article

The swift advancements in autonomous vehicle systems have facilitated their implementation across various industries, including agriculture. However, studies primarily focus on passenger vehicles, with fewer examining autonomous trucks. Therefore, this study reviews autonomous truck systems implementation in North Dakota’s agricultural industry to develop comprehensive technology readiness frameworks and strategic deployment approaches. The review integrates systematic literature review and event history analysis of 52 studies, categorized using Social–Ecological–Technological Systems framework across six dimensions: technological, economic, social change, legal, environmental, and implementation challenges. The Technology Readiness Level (TRL) analysis reveals 39.5% of technologies achieving commercial readiness (TRL 8–9), including GPS/RTK positioning and V2V communication demonstrated through Minn-Dak Farmers Cooperative deployments, while gaps exist in TRL 4–6 technologies, particularly cold-weather operations. Nonetheless, challenges remain, including legislative fragmentation, inadequate rural infrastructure, and barriers to public acceptance. The study provides evidence-based recommendations that support a strategic three-phase deployment approach for the adoption of autonomous trucks in agriculture. Full article

Efficient detection of road surface defects is vital for timely maintenance and traffic safety. This study introduces a novel AI-powered web framework, TriRoad AI, that integrates multiple versions of the You Only Look Once (YOLO) object detection algorithms—specifically YOLOv8 and YOLOv11—for automated detection of potholes and cracks. A user-friendly browser interface was developed to enable real-time image analysis, confidence-based prediction filtering, and severity-based geolocation mapping using OpenStreetMap. Experimental evaluation was conducted using two datasets: one from online sources and another from field-collected images in Ankara, Turkey. YOLOv8 achieved a mean accuracy of 88.43% on internet-sourced images, while YOLOv11-B demonstrated higher robustness in challenging field environments with a detection accuracy of 46.15%, and YOLOv8 followed closely with 44.92% on mixed field images. The Gemini AI model, although highly effective in controlled environments (97.64% detection accuracy), exhibited a significant performance drop of up to 80% in complex field scenarios, with its accuracy falling to 18.50%. The proposed platform’s uniqueness lies in its fully integrated, browser-based design, requiring no device-specific installation, and its incorporation of severity classification with interactive geospatial visualization. These contributions address current gaps in generalization, accessibility, and practical deployment, offering a scalable solution for smart infrastructure monitoring and preventive maintenance planning in urban environments. Full article

Climate change, the consequences of which have been more intense than ever in the last few decades, makes the need for sustainable transportation even more imperative. The promotion of public transportation and the discouragement of private car use are among the main priorities of sustainable transport planning in modern urban areas. However, the selection of the most appropriate transport project, apart from significant opportunities, is also accompanied by significant challenges, especially under the demand of compromising—often conflicting—social, environmental, and economic criteria, as well as different stakeholders’ interests. The aim of the present paper is to provide decision analysts and policy-makers with a decision-support tool for the prioritization and optimum selection of public transport projects for an urban area within the framework of sustainability. For this purpose, a comprehensive inventory of criteria for the evaluation of urban public transport systems (alternatives), along with a standardized table with the relevant performance of the most common alternatives (i.e., metro, tram, monorail, and BRT) are provided based on international literature review. A multi-criteria decision-aiding methodology based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), allowing for the direct exclusion of an alternative not meeting certain “binding” criteria from further evaluation, thus saving time, effort and cost, taking into account different stakeholders’ interests and preferences, as well as the particularities and special characteristics of the study area, is then proposed and tested through a theoretical case study. Full article

As electric vehicles (EVs) become increasingly integrated into urban mobility, the load on electrical grids increases, prompting innovative energy management strategies. This paper investigates the deployment of vehicle-to-grid (V2G) services at the University of Rome Tor Vergata, leveraging high-resolution floating car data (FCD) to forecast and schedule energy transfers from EVs to the grid. The methodology follows a four-step process: (1) vehicle trip detection, (2) the spatial identification of V2G in the campus, (3) a real-time scheduling algorithm for V2G services, which accommodates EV user mobility requirements and adheres to charging infrastructure constraints, and finally, (4) the predictive modelling of transferred energy using ARIMA and LSTM models. The results demonstrate that substantial energy can be fed back to the campus grid during peak hours, with predictive models, particularly LSTM, offering high accuracy in anticipating transfer volumes. The system aligns energy discharge with campus load profiles while preserving user mobility requirements. The proposed approach shows how campuses can function as microgrids, transforming idle EV capacity into dynamic, decentralised energy storage. This framework offers a scalable model for urban energy optimisation, supporting broader goals of grid resilience and sustainable development. Full article

This study aims to predict the purpose of the use of IC card data in LRT immediately after its opening by means of a questionnaire survey and to understand the changes in the number of commuters to better understand the growth in LRT commuter ridership, which has not been fully clarified in Japan. Furthermore, to assess long-term commuter retention for LRT systems, the analysis revealed the following three points. First, a discriminant analysis based on a national PT survey revealed that commuting and leisure or business activities can be classified with high accuracy. Second, it was found that commuter numbers increased immediately after opening, while the number of leisure or business users decreased in the first few months after opening and then leveled off. Third, the increase in the number of commuters was modeled using a logistic curve, and the annual rate of change in ridership was predicted to be less than 1% in the first three to four years after opening. Full article

This paper explores the potential of public webcams as a source of data for transport research. Eight different open-source object detection models were tested on three publicly accessible webcams located in the city of Brunswick, Germany. Fifteen images at different lighting conditions (bright light, dusk, and night) were selected from each webcam and manually labelled with regard to the following six categories: cars, persons, bicycles, trucks, trams, and buses. The manual counts in these six categories were then compared to the number of counts found by the object detection models. The results show that public webcams constitute a useful source of data for transport research. In bright light conditions, applying out-of-the-box object detection models can yield reliable counts of cars or persons in public squares, streets, and junctions. However, the detection of cars and persons was not reliably accurate at dusk or night. Thus, different object detection models might have to be used to generate accurate counts in different lighting conditions. Furthermore, the object detection models worked less well for identifying trams, buses, bicycles, and trucks. Hence fine-tuning and adapting the models to the specific webcams might be needed to achieve satisfactory results for these four types of traffic participants. Full article