Future Transp., Volume 5, Issue 3 (September 2025) – 53 articles

Medium- and long-term international road safety goals require continued advancement of scientific research, especially with regard to the human component. Recent technological advances in sensor technology offer new opportunities to more accurately characterize driving behavior, helping to reduce the uncertainty associated with driver reactions. This study evaluated the effectiveness of specific physiological variables, detected by low-cost wearable sensors, to obtain reliable indicators of the driver’s workload. Heart rate and skin conductivity were analyzed in a real driving environment, in the absence of evident emotional stresses, to test their sensitivity to an ordinary level of physical and mental engagement. An experiment was conducted on a sample of users (10 drivers) along a rural road in Sicily, Italy. Data analysis, carried out through ANOVA and generalized linear models on three distinct curves, produced preliminary results indicating that subtle road geometry changes can be detected by physiological sensors, validating their potential for integration into driver monitoring systems. Statistically significant mean differences were found for speed (for all curves, p < 0.001), heart rate (R1 vs. R2, p = 0.009), and tonic GSR (R1 vs. R2, p = 0.006; R2 vs. R3, p = 0.013; A vs. B, p = 0.013; A vs. C, p = 0.006) as a function of different radius (R1, R2, R3) and deviation angle values (A, B, C). Future developments will require a significant increase in the sample size and the number of scenarios to achieve results of general utility. Full article

Autonomous vehicles (AVs) are rapidly becoming a reality, with a series of cities in the world currently testing applications. Despite these developments, the existing analyses in the literature concerning the impacts of such developments on urban transportation systems have yielded a body of evidence marked by significant divergence and contradictory conclusions. Such conflicting findings critically hamper the synthesis of a coherent understanding and the formulation of evidence-based strategies, a challenge exacerbated by the potentially multifaceted nature of these impacts. The potential disruptive technology and the game-changing force of automated vehicles make this lack of congruence in analytical outcomes severely complicate efforts to derive clear insights or actionable conclusions. The purpose of the paper is to explore and define the optimal strategies for implementing autonomous vehicle technologies, to predict their effects on anomalies, in the Kuhnian sense, of urban transportation, and to propose a desirable urban vision and a paradigm shift consisting of a decline of car ownership dependence and the rise of shared AVs. This study is undertaken to address the escalating crisis in urban transportation globally. Cities are facing unprecedented strain due to rapid urbanization, leading to severe traffic congestion, pervasive air and noise pollution, significant safety risks, and persistent accessibility gaps, all of which profoundly diminish urban quality of life and impede economic vitality. The new vision has been assessed based on a literature selection, some qualitative and quantitative analyses, and applications and projects currently in testing. The results are largely positive and promise to change urban transportation radically, as well as to resolve the mismatches between the vision, what the paradigm predicts, and what is revealed in the implementation. The success of the vision ultimately depends on policy and regulation to manage the way in which AVs are implemented in urban areas, if they are not to lead to a worsening of the urban environment, accessibility, and health. This thoughtful implementation should address all potential challenges through integrated planning of transportation, land use, and digital systems. Full article

Achieving global carbon neutrality by 2050 requires active decarbonization efforts from both developed and developing countries, with the latter being responsible for most greenhouse gas (GHG) emissions. This study examines the potential of low-carbon mobility transitions, focusing on the electrification of light commercial vehicles (LCVs)—a rapidly expanding segment with high emissions in urban freight. While Total Cost of Ownership (TCO) analyses show electric powertrains to be cost-effective in developed markets, there is limited empirical evidence for developing economies. To address this gap in the research, this paper compares the TCO for electric LCVs (eLCVs) in Italy and Pakistan, representing contrasting stages of electric mobility adoption. Using a bottom-up model for Pakistan and robust datasets for Italy, this study assesses how macroeconomic conditions, tax structures, and policy frameworks shape lifecycle costs. The TCO assessment underscores a stark geographic divergence: in Italy, eLCVs (0.359 EUR/km) are currently 19.7% more expensive than their diesel counterparts (0.300 EUR/km). In contrast, Pakistan demonstrates favorable competitiveness for eLCVs, with a TCO of 0.119 EUR/km compared to 0.136 EUR/km for diesel equivalents. The analysis reveals stark contextual differences in cost components, infrastructure, annual distance travelled, and policy effects, highlighting the need for context-specific strategies. The findings offer practical guidance for policymakers and fleet operators, supporting more equitable and effective decarbonization strategies globally. Full article

One type of transportation system developed in several cities is the Bus Rapid Transit (BRT) system. BRT systems are influenced by various factors, and route planning is one of the most important ones, which involves aspects such as route design, bus schedules, and passenger load. BRT systems can generate certain service data, which can be useful for calculating passenger load. However, these service data are insufficient to accurately predict future passenger loads. Processes such as origin–destination matrix analysis are required, which are time-consuming and not suitable in most cases. This paper proposes a machine learning (ML) model that allows predicting passenger load at the key stations of a BRT system. An exploration of datasets from several BRT systems was performed for a particular use case. Open data on the Transmilenio BRT system from Bogotá (Colombia) was determined as the source. The obtained results showed that the model using the Long-Short Term Memory (LSTM) algorithm obtained the best results in the metrics using one of the two generated datasets. However, the initial results were not satisfactory enough, so it was necessary to use a hyperparameter-tuning tool and vary the range of dates in the dataset to improve the respective metrics. Full article

Automated vehicles (AVs) are expected to shape the future of transportation and to improve traffic flow and safety. Studies have focused on AVs effects on traffic flow during the transition to full automation, with few examining their influence on human-driven vehicles (HDVs). This study investigated potential changes in HDVs’ driving behavior induced by the presence of AVs with different driving styles (aggressive vs. cautious) at varying market penetration rates (MPRs) (0%, 25%, 50%, and 75%). First, a driving simulator experiment with 160 people (56 females, 104 males) was conducted to collect HDV trajectory data. Then, a microsimulation model was implemented in VISSIM, where HDV behavioral parameters were calibrated using the driving simulator data. Average time headway (THW), relative velocity (RelVel), average acceleration (Acc), average deceleration (Dec), and lane change frequency (LnCh) were used as behavioral metrics. A two-way ANOVA was applied for analysis. Results showed that higher AVs’ MPRs decreased THW, Acc, and Dec in HDVs, while RelVel increased with cautious AVs and decreased with aggressive AVs. Similar trends were observed for LnCh. These findings highlight the need to consider potential HDVs behavioral adaptation during the transition phase, as neglecting it may lead to inaccurate traffic assessments and ineffective policies. Full article

As road transport continues to evolve with advancements in automation and intelligent traffic management, optimizing emergency response operations remains a critical challenge in urban mobility. This study presents an innovative data-driven framework for optimizing fire station placement in Birjand, Iran, integrating transportation efficiency with emergency service accessibility. A binary integer programming model was developed to minimize response time and transportation costs while incorporating real-world constraints. Using dynamic simulations in MATLAB 2019b, the study analyzed existing fire station coverage across seven urban regions, assessing travel efficiency based on an average vehicle speed of 52.5 km/h and a 5 min response threshold. Key findings highlight disparities in emergency service accessibility, with high-demand areas such as R4 and R5 lacking sufficient coverage, while low-demand regions like R6 remain underserved. To address this, a genetic algorithm (GA) with 100 individuals over 20 generations was implemented. Optimizing total penalized response time, calculated as the objective value of GA, is 25.89 min. This value represents the sum of penalized response times across all station-area assignments. A cost–benefit analysis revealed Station 2 as the most efficient investment, achieving a net benefit of 3163 million IRR at a 1% discount rate, outperforming Station 1 (2831 million IRR). Sensitivity analysis confirmed Station 2’s financial advantage across discount rates up to 10%. This research contributes to emerging transportation challenges by bridging emergency response optimization with urban mobility strategies. The proposed decision support system (DSS) integrates adaptive planning, data-driven analytics, and infrastructure investment to enhance resilience and response efficiency in dynamic urban environments. Full article

Over recent years, driven by intertwined economic, social, environmental, and technological factors, urbanization has accelerated at an unprecedented pace, posing complex challenges to metropolitan transport systems. This has intensified the demand for innovative mobility solutions, notably Mobility as a Service (MaaS), which promotes a paradigm shift from private vehicle ownership to mobility consumed as a service. With rapid advances in digital technologies, MaaS has gained substantial momentum, attracting significant scholarly attention for its potential to enable intelligent and sustainable transportation systems. This study aims to provide a comprehensive conceptual foundation of MaaS and its components, and to systematically examine how artificial intelligence (AI), machine learning (ML), and big data techniques are applied in this domain. Following PRISMA guidelines, a bibliometric and systematic review was conducted on peer-reviewed articles published between 2020 and 2024 and indexed in the Scopus and Web of Science databases. The analysis classifies AI applications across four MaaS integration levels: basic, intermediate, advanced, and full integration. The results show that machine learning and basic optimization dominate at the basic level; blockchain and big data are most prominent at the advanced and full levels; and deep learning is applied across all levels, with a particularly strong presence at the advanced stage for real-time, personalized mobility solutions. The findings also indicate that while most implementations focus on developed countries, there is substantial potential for adaptation in emerging markets. The paper concludes by discussing key challenges in regulatory compliance, inclusivity, and the protection of sensitive user data, and outlines future research avenues for building socially equitable, intelligent, and sustainable MaaS ecosystems. Full article

The decarbonization and automation of port operations are emerging as key strategies to enhance the sustainability and efficiency of maritime logistics. This study proposes a simulation-based framework to assess the operational and environmental impacts of transitioning from traditional Internal Combustion Engine (ICE) tractors to Battery Electric Tractors (BET) and Automated Electric Guided Tractors (e-AGT) in Roll-on/Roll-off (Ro–Ro) port terminal operations. The proposed framework is applied to simulate a full vessel turnaround at the Ro–Ro terminal of the Port of Ravenna (Italy). A set of Key Performance Indicators (KPIs) is defined to evaluate turnaround time, vehicle productivity, energy consumption and CO₂ emissions across three scenarios. The results indicate that both BET and e-AGT configurations significantly reduce emissions compared to ICE, with reductions up to 40%. However, the e-AGT scenario reveals operational drawbacks, including increased unloading time and reduced fleet availability due to charging constraints and routing limitations. These findings highlight the environmental potential of automation and electrification but also emphasize the need for integrated planning of fleet size, charging infrastructure and circulation specifications. The proposed framework provides a replicable decision-support tool for port authorities and logistics operators to evaluate alternative handling technologies under realistic conditions. Full article

Efficient threat detection in X-ray cargo inspection is critical for the security of the global supply chain. This study evaluates YOLO-based object-detection models from YOLOv5 to the latest, YOLOv11, which is enhanced with modified loss functions and Soft-NMS to improve accuracy. The YOLO model comparison also includes DETR (Detection Transformer) and Faster R-CNN (Region-based Convolution Neural Network). Standard loss functions struggle with overlapping items, low contrast, and small objects in X-ray imagery. To overcome these weaknesses, IoU-based loss functions—CIoU, DIoU, GIoU, and WIoU—are integrated into the YOLO frameworks. Experiments on a dedicated cargo X-ray dataset assess precision, recall, F1-score, mAP@50, mAP@50–95, GFLOPs, and inference speed. The enhanced model, YOLOv11 with WIoU and Soft-NMS, achieves superior localization, reaching 98.44% mAP@50. This work highlights effective enhancements for YOLO models to support intelligent logistics in transportation services and automated threat detection in cargo security systems. Full article

Light Electric Vehicles (LEVs) offer a promising response to environmental and urban mobility challenges. This study is among the first to exploratorily examine their use, user characteristics, and owner evaluations. A qualitative pre-study with four LEV owners was conducted and informed a subsequent quantitative phase involving 23 owners. Over two weeks, participants recorded all LEV trips using GPS tracking and completed two questionnaires. Findings show that LEVs are regularly used for commuting, shopping, and work-related trips. Notably, many users live outside urban centers, indicating strong potential for short-distance travel in rural and small-town contexts for our sample. This challenges the view of LEVs as primarily urban or recreational vehicles. Within our sample, usage patterns were diverse, indicating that even among early adopters there is no single typical usage profile. While cars were perceived as slightly safer, no participant reported feeling unsafe in their LEV. User satisfaction was high: 24 of 27 respondents would choose the same vehicle again. Overall, LEVs emerge as a versatile and satisfying mobility option, relevant beyond city limits. Given their wide range of uses and positive user feedback, LEVs should be more strongly considered in transport policy to promote more sustainable and needs-based mobility. Full article

This paper presents an initial contribution to a broader research initiative focused on the aggregate calibration of travel demand sub-models using low-cost and widely accessible data. Specifically, this first phase investigates methods and algorithms for the aggregate calibration of destination choice models, with the objective of assessing the possible utilization of an external observed matrix, eventually derived from opportunistic data. It can be hypothesized that such opportunistic data may originate from processed mobile phone data or result from the application of data fusion techniques that produce an estimated observed trip matrix. The calibration problem is formulated as a simulation-based optimization task and its implementation has been tested using a small-scale network, employing an agent-based model with a nested demand structure. A range of optimization algorithms is implemented and tested in a controlled experimental environment, and the effectiveness of various objective functions is also examined as a secondary task. Three optimization techniques are evaluated: Simultaneous Perturbation Stochastic Approximation (SPSA), Particle Swarm Optimization (PSO), and Adaptive Moment Estimation (ADAM). The application of the ADAM optimizer in this context represents a novel contribution. A comparative analysis highlights the strengths and limitations of each algorithm and identifies promising avenues for further investigation. The findings demonstrate the potential of the proposed framework to advance transportation modeling research and offer practical insights for enhancing transport simulation models, particularly in data-constrained settings. Full article

Combining public transportation (PT) with Bike-Sharing Systems (BSSs) offers a pathway toward the sustainable development of urban mobility. These systems can reduce fuel consumption, air pollution, and street congestion, especially during peak hours. Moreover, PT and BSS are frequently used by individuals without access to private vehicles, including low-income groups and students. Whereas increasing PT network infrastructure is constrained by issues such as high capital costs and limited street space (which inhibits mass transit options like BRT or trams), BSS can be used as an adaptable and affordable solution to fill these gaps. In particular, BSS can facilitate the “first-mile–last-mile” legs of PT journeys. However, many transit agencies still rely on traditional joint service planning and overlook BSS as a critical mode in integrated travel chains. This paper proposes that PT and BSS be considered as a unified network and introduces a framework to assess whether access to this integrated system is equitably distributed across urban areas. The framework estimates demand for travel using public mobility options and supply at the level of Traffic Analysis Zones (TAZs), treating PT and BSS as complementary modes. Spatial accessibility analysis is employed to examine connectivity using factors that affect access to both PT and BSS. The proposed approach is tested by taking Tehran as the focus of the case analysis. The results identify the most accessible areas and highlight those that require improved PT-BSS integration. These findings provide policy-relevant suggestions to promote equity and efficiency in urban transport planning. The outcomes reveal that central TAZs in Tehran receive the highest level of PT-BSS integration, while the western and southern TAZs are in urgent need of adjustment to ensure better distribution of integrated public transportation and bike-sharing services. Full article

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