Future Transp., Volume 5, Issue 1 (March 2025) – 20 articles

The use of traffic simulation to analyze traffic safety and performance has become common in transportation engineering. Microsimulation methods are increasingly used to analyze driving performance for different road geometries and environmental elements. Drivers’ perception has an important impact on driving performance factors contributing to traffic safety on transportation facilities (highways, arterials, intersections, etc.). Impaired vision leads to failure in drivers’ perception and making right decisions. Various studies investigated the impact of environmental elements (fog, rain, snow, etc.) on driving performance. However, there is limited research examining the potentially detrimental effects on driving capabilities due to differing exposure to natural light brightness, in particular sun exposure. Autonomous vehicles (AVs) showed a significant impact enhancing traffic capacity and improving safety margins in car-following models. AVs may also enhance and/or complement human driving under deteriorated driving conditions such as sun glare. This study uses a calibrated traffic simulation and surrogate safety assessment model to improve traffic operations and safety performance under impaired visibility using different types of autonomous vehicles. A combination of visibility reduction, traffic flow characteristics, and autonomy levels of AVs was simulated and assessed in terms of the number of conflicts, severity level, and traffic operations. The simulation analysis results used to reveal the contribution of conflicts to the risk of crashes varied based on the influence of autonomy level on safe driving during sun glare exposure. The outcome of this study indicates the benefits of using different levels of AVs as a solution to driving under vision impairment situations that researchers, traffic engineers, and policy makers can use to enhance traffic operation and road safety in urban areas. Full article

Electric-connected automated vehicle (CAV) shuttles, as a part of the sustainable microtransit system, have the potential to fill public transit service gaps. Following technology and traveler acceptance tests that are underway around the world, mass-produced CAVs will be considered for shared mobility service, including “first/last mile” travel between public transit hub stations and medical campuses or other activity centres. Thus, there is a need for increased knowledge on treating risk in such applications. This paper covers the planning and economic feasibility of an advanced technology level 4 automated vehicle-based microtransit system, considering uncertain service and economic feasibility factors. The methods used are advanced for addressing uncertainties in travel demand, service factors, and the economic feasibility of investments by public and private sector entities. Specifically, a probability-based macro simulation approach is used to treat demand and supply-side service factors as stochastic, and it is adapted for risk analysis in financial decision-making. The effects of uncertain life-cycle costs on fares and the rate-of-return are described. Results are favourable regarding the technical and economic feasibility of advanced technology-based microtransit first/last mile service. The findings reported here are a contribution to knowledge on the feasibility of implementing CAV-based first/last mile, and other microtransit services, under uncertainty. Full article

Autonomous vehicles (AVs) and shared mobility constitute two of the “Three Revolutions” that portend major changes to surface transportation. AVs promise to reduce accidents, expand accessibility, and decrease congestion, while shared mobility provides the benefits of automotive transportation without requiring the purchase of a vehicle or the ability to drive it. Despite great promise to alleviate the negative externalities imposed by transportation, there remains much to be understood about the combined diffusion and impact of AVs and shared mobility. There is little demonstrated experience and application of AVs to the public, and how and where people would use automated shared mobility relative to their current travel is largely unknown. This study advances our understanding by utilizing an intercept survey of 232 respondents in Ann Arbor, Michigan who had made a discretionary trip to one of two central and two suburban locations. The novel approach of using intercept surveys allows us to gather more valid data about the willingness of respondents to replace the mode they just used for either a privately owned or a shared AV and do so for the trip purpose most conducive to using such a vehicle. We incorporate descriptive and spatial analyses and then utilize multinomial logit models to predict the factors influencing the encouragement or discouragement of substituting a private and a shared AV for their previous trip. We found that active mobility and transit trips work in competition with private AVs, while youth encourages interest. Meanwhile, active mobility, increasing age, and one of our measures of density discourage interest, while female respondents and the same measure of density increase interest. The results suggest that future efforts to facilitate the adoption of shared AVs target areas of the city that are relatively dense and residents in these areas where a shared AV would enhance individuals’ mobility. Full article

This study investigates the application of machine learning (ML) to understand and mitigate winter road risks while addressing model interpretability. Using 26,970 winter crash records collected over four years in Edmonton, Canada, we developed and compared three ML-based winter crash frequency models: XGBoost, Random Forest, and LightGBM. To enhance interpretability, we applied SHapley Additive exPlanations (SHAP), providing insights into feature contributions. Our analysis incorporated micro-level variables such as collision records, weather conditions, and road characteristics, as well as macro-level variables such as land use patterns, spatial characteristics (via Hot Spot Analysis), and traffic exposure (estimated using Ordinary Kriging). Among the models tested, XGBoost outperformed others, achieving a testing R² of 92.67%, MAE of 3.64, and RMSE of 5.77. SHAP analyses on XGBoost provided both global and local explanations. At a global level, road type, speed limit, and traffic enforcement cameras were identified as key factors influencing crash frequency while locally, distinct features of high- and low-crash locations were highlighted, supporting targeted risk mitigation strategies. By bridging the gap between model accuracy and interpretability, this study demonstrates the value of interpretable ML models in improving winter road safety, offering actionable insights for informed decision-making and resource allocation in winter road maintenance. Full article

This study identified the main factors affecting car selection decisions through discrete choice experiments based on a large dataset collected in four European countries in 2023 using stated choice questionnaires. The choice set includes six current and popular car powertrains with factors related to vehicle features, user characteristics, and specific geographical contexts, which can influence the adoption of vehicles with electrified powertrains. An easily applicable multinomial logit model was first proposed to explore the effects of selected attributes and the model’s ability to reproduce user preferences with different incentive policies, geographical contexts, and energy prices. A mixed logit model and a segmented multinomial logit model were introduced to consider the sample’s heterogeneity. The first captures the preference dispersion among respondents related to incentives and operational costs. The second, which specifically classifies users based on car market segments, showed a greater variation in factors related to the purchase cost and battery range. The models estimate the weight of nine factors, offering support for targeted policy recommendations. Cost-related factors confirm their relevance in choices, and the analysis shows that users who want to enhance their vehicle range by 1 km are willing to pay approximately EUR 80. Full article

The inherent uncertainty in travel forecasting models—arising from potential and unknown errors in input data, parameter estimation, or model formulation—is receiving increasing attention from both the scholarly and practicing communities. In this research, we investigate the variance in forecasted traffic volumes resulting from varying the mode and destination choice parameters in an advanced trip-based travel demand model. Using Latin hypercube sampling to construct several hundred combinations of parameters across the plausible parameter space, we introduce substantial changes to implied travel impedances and modal utilities, on the order of a 10 percent variation. However, the aggregate effects of these changes on forecasted traffic volumes are small, with a variation of approximately 1 percent on high-volume facilities. It is likely that in this example—and perhaps in others—the network assignment places constraints on the possible volume solutions and limits the practical impacts of parameter uncertainty. Nevertheless, parameter uncertainty may not be the largest contributor to error in practical travel forecasts. Further research should examine the robustness of this finding across other less constrained networks and within activity-based travel model frameworks. Full article

This research investigates the Saudi marine sector’s response to climate change. In particular, it assesses industry stakeholder awareness, attitudes, and actions concerning climate-related challenges. A complete survey was distributed to a varied range of industry participants, including executives, managers, seafarers, and academics, to assess their understanding and involvement. The research indicates moderate levels of awareness and engagement, and significant challenges, including financial limitations, a lack of experience and knowledge, and insufficient regulatory support, to implementing more sustainable practices. The study also mentions ongoing attempts to satisfy International Maritime Organization (IMO) requirements, while present mitigating techniques have limited efficacy. Compared to other regions, Saudi Arabia mostly depends on fossil fuels, which poses specific difficulties in the transformation of sustainable maritime practices. The study identifies current strategies and proposes prospects such as raising financial assistance, and the adoption of innovative technologies. These findings are critical to providing the link between the Saudi marine sector and the climate targets, as well as the Saudi Vision 2030. Full article

The wing-in-ground (WIG) effect occurs when air pressure is created beneath a craft moving close to the ground. The pressure created adds upwards lift, resulting in less need for propulsion for moving forward. Over the years, several companies in various countries have developed wing-in-ground crafts—marine vessels, looking like airplane, that operate using the ground effect. However, no commercial routes are currently in operation using such crafts. This article seeks to identify the critical factors that contribute to the successful commercialization of WIG crafts. The study is composed of a literature review, a company comparison and an analysis of one case study close to successful commercialization. The study indicates that the following actions are critical for the commercial success of a company engaged in WIG operations: engaging community, enhancing R&D, establishing a robust technological system and focusing on safety and compliance. It is also noted that technological readiness itself does not guarantee the successful implementation of WIG crafts on commercial routes. Full article

In 2023, the EU set the target to reduce greenhouse gas (GHG) emissions by 55% until 2030 compared to 1990. The European Transport Policy sees battery–electric vehicles as a key technology to decarbonize the transport sector, so governments support the adoption through dedicated funding programs. Battery–electric trucks hold great potential to decarbonize the transport sector, especially for high-impact, heavy-duty trucks. Theoretical life-cycle assessments (LCA) predict a lower CO₂e emission impact from battery–electric trucks compared to conventional diesel trucks. Yet, one concern repeatedly mentioned by potential users is the doubt about the ecological advantage of battery–electric vehicles. This is rooted in the problem of a much higher CO₂e impact of the lithium-ion batteries production process. As heavy-duty trucks have a much larger battery, the hypothec in the construction phase of the vehicle is significantly higher, which must be regained during the use phase. Although theoretical assessments exist, CO₂e evaluations using real-world application data are almost nonexistent, as the technology is at the very start of the adoption curve. Exemplary is the fact that there were only 72 registered battery–electric heavy-duty tractor trucks throughout the whole of Germany at the start of 2023. This paper aims to deliver one of the first real-world quantifications using operational data for the actual reduction impact of battery–electric heavy-duty trucks compared to diesel trucks. This study uses the methodology of the life-cycle assessment approach according to ISO 14040/14044 to gain a systematic and holistic technology comparison. For this LCA, the system boundaries are considered from cradle to cradle. This includes the production of raw materials and energy, the manufacturing of the trucks, the use phase, and the recycling afterward. The research objects of this study are battery–electric and diesel Volvo FM trucks, which have been in use by the German freight company Nord-Spedition GmbH since May 2023. The GREET^® database is used to assess the emission impact of the material production and manufacturing process. The Volvo tractor trucks resemble a critical case, as the vehicles have a battery size of 540 kWh—around 11 times larger than a usual passenger car. The operation data is directly provided by the logistics company to observe fuel/electricity consumption. Other factors are assessed through company interviews as well as a wide literature research. Finally, a large question mark concerning total emissions lies in the cradle-to-cradle capabilities of large-scale lithium-ion batteries and the electricity grid mix. Different scenarios are being considered to assess potential disposal or recycling paths as well as different electricity grid developments and their impact on the overall balance. The findings estimate the total emissions reduction potential to range between 34% and 69%, varying with assumptions on the electricity grid transition and recycling opportunities. This study displays one of the first successful early-stage integrations of battery–electric heavy-duty trucks into the daily operation of a freight company and can be used to showcase the ecological advantage of the technology. Full article

Pedestrian safety is a growing global concern, particularly in urban areas, where rapid urbanization and increased mobile device usage have led to an increase in distracted walking. This study investigates the impact of technological distractions, specifically mobile usage (MU), on pedestrian behavior and safety at signalized urban intersections. Data were collected from 11 signalized intersections in New Delhi, India, using video recordings. Key inputs to the modeling process include pedestrian demographics (age, gender, group size) and behavioral variables (crossing speed, waiting time, compliance behaviors). The outputs of the models focus on predicting mobile usage behavior and its association with compliance behaviors such as crosswalk and signal adherence. The results show that 6.9% of the pedestrians used mobile phones while crossing the road. Advanced machine learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN), have been applied to analyze and predict MU behavior. Key findings reveal that younger pedestrians and females are more likely to exhibit distracted behavior, with pedestrians crossing alone being the most prone to mobile usage. MU was significantly associated with increased levels of crosswalk violation. Among the machine learning models, the CNN demonstrated the highest prediction accuracy (94.93%). The findings of this study have a practical application in urban planning, traffic management, and policy formulation. Recommendations include infrastructure improvements, public awareness campaigns, and technology-based interventions to mitigate pedestrian distractions and to enhance road safety. These findings contribute to the development of data-driven strategies to improve pedestrian safety in rapidly urbanizing regions. Full article

This article aims to investigate the economic feasibility of renewing a fleet of diesel light commercial vehicles (LCVs) with equivalent more environmentally friendly vehicles in the distribution of frozen and chilled foods. A Total Cost of Ownership (TCO) approach is proposed that includes all pertinent expenses to compare the cost competitiveness of battery electric, fuel-cell electric, and bio-diesel LCVs with respect to their conventional diesel counterparts, and to perform policy scenarios. We adopt both a private and a social perspective by also accounting for the external costs of transportation. We found that electric LCVs outperform their rivals in the city and panel LCV categories even in the absence of government subsidies while being cost competitive in box LCV segment, while FCEVs require the development of refueling infrastructure and government subsidies to compete with diesel counterparts. Full article

Integrating autonomous shuttles into public transportation systems holds immense potential to revolutionize urban mobility and enhance accessibility. This paper focuses on a comprehensive analysis of the perceptions of practitioners and industry experts and proposes best practices for effectively integrating autonomous shuttles into public transportation systems. Perceptions of stakeholders have been collected, and a two-fold analysis was performed. Critical barriers for the adoption of autonomous shuttles were identified using the Garette ranking method and principal component analysis (PCA). Recommendations covering different aspects, including underutilization, safety concerns, seating arrangements, reliability, data security, operational aspects, sensor technology, and lane use, are provided. They encompass operational adjustments, infrastructure enhancements, safety measures, policy considerations, and economic foresight. The findings emphasize the importance of extending pilot deployment trial periods, improving autonomy, strategically positioning sensors, enhancing road signage, and providing dedicated lanes for autonomous shuttles. Data-security policies, operator training, and stakeholder responsibilities are also highlighted to build trust and facilitate a seamless transition to autonomous shuttles. This paper concludes by providing recommendations to ensure the successful integration of autonomous shuttles, fostering widespread acceptance and shaping the future of urban transportation. Full article

Transportation 5.0 is an advanced and sophisticated system combining technologies with a focus on human-centered design and inclusivity. Its various components integrate intelligent infrastructure, autonomous vehicles, shared mobility services, green energy solutions, and data-driven systems to create an efficient and sustainable transportation network to tackle modern urban challenges. However, this evolution of transportation is also intended to improve accessibility by creating environmentally benign substitutes for traditional fuel-based mobility solutions, even when addressing traffic management and control issues. Consequently, to promote synergy for sustainability, the diversified nature of the Transportation 5.0 components ought to be efficiently and effectively managed. Thus, this study aims to reveal the involvement of Transportation 5.0 core component prediction in the sustainable transportation system through a systematic literature review. This study also contemplates the causal model under system dynamics modeling in order to address sustainable solutions and the movement toward sustainability in the context of Transportation 5.0. From this review, in addition to the developed causal model, it is identified that every core component management method in the sustainable Transportation 5.0 system reduces environmental impact while increasing passenger convenience and the overall efficiency and accessibility of the transport network, with greater improvements for developing nations. As the variety of transportation options, including electric vehicles, is successfully integrated, this evolution will eventually enable shared mobility, green infrastructure, and multimodal transit options. Full article

While cargo bikes are becoming an increasingly popular alternative to larger, more polluting vehicles in both the logistics and private mobility sectors, there has been comparatively little research on their use for private mobility. The potential of shared cargo bikes to replace car trips has been examined in some studies, but no previous research has investigated the critical factors that make it a valued alternative. By studying users’ willingness to pay, this paper examines the perceived value of a free cargo bike sharing service for users. The research is based on a survey of 321 users of the Fietje cargo bike sharing service in Bremen, conducted in 2022. In this sample, 38 to 55% of shared cargo bikes trips would otherwise have been performed by car. The paper identifies the transport of objects and children as critical features that provide value to users and create the potential to replace car trips. The results also draw attention to the fact that a cargo bike sharing service is likely to be a more effective tool for reducing car use if it is free. Introducing a fee would increase car trips by 14 to 18% of the total trips enabled by the service. Full article

Performing synchronization in public transport is one of the most challenging tasks that transport managers perform when organizing the processes of passenger servicing. Many variables characterizing existing public transport lines should be considered in the final timetable; in addition, the complexity of the transportation system, the variability in transport demand, and the stochasticity of the servicing process both in time and space have a significant influence on the result of synchronization. The synchronization problem in real-world applications does not have an exact solution, so in practice, a variety of techniques were developed to achieve a rational solution in a reasonable time. In our paper, we classify existing approaches to solving the problem of public transport synchronization, describe the essence of the most promising methods, and study their popularity based on the most recent scientific publications. As the result of our research, we show the most promising directions for the future development of synchronization methods and their application in public transportation. Full article

In the rapidly evolving landscape of digital connectivity, airlines have integrated these advancements as indispensable tools for a seamless consumer experience. However, digitisation has increased the scope of risk in the cyber realm. Limited studies have systematically investigated cybersecurity risks in the airline industry. In this context, we propose a novel questionnaire model to investigate consumers’ perceptions regarding the cybersecurity of airlines. Data were collected from 470 Chinese participants in Nanjing City. The analytical approach encompassed a range of statistical techniques, including descriptive statistics, exploratory factor analysis, difference analysis, and correlation. The constructs based on Maddux’s Protective Motivation Theory and Becker’s Health Belief Model were reliable, indicating the suitability of the proposed scales for further research. The results indicate that gender significantly influences passengers’ perceptions of airline cybersecurity, leading to variations in their awareness and response to cybersecurity threats. Additionally, occupation affects passengers’ information protection behaviour and security awareness. On the other hand, factors such as age, education level, and Frequent Flyer Program participation have minimal impact on passengers’ cybersecurity perceptions. Based on questionnaire content and data analysis, we propose three recommendations for airlines to enhance consumer cybersecurity perception. First, airlines should provide personalised network security services tailored to different occupations and genders. Second, they should engage in regular activities to disseminate knowledge and notices related to network security, thereby increasing passengers’ attention to cybersecurity. Third, increased resources should be allocated to cybersecurity to establish a safer cyber environment. This study aims to improve the quality of transportation policy and bridge the gap between theory and practice in addressing cybersecurity risks in the aviation sector. Full article

The European Union’s path towards zero carbon dioxide emissions for new passenger vehicles necessitates a transitional period in which conventional vehicles coexist with zero-emission alternatives. This shift requires targeted strategies from engineers and policymakers, particularly in the area of road design, to reduce pollution. This study aims to investigate the environmental benefits of converting a two-lane urban roundabout into a turbo-roundabout through a virtual microsimulation approach using PTV VISSIM. The simulated model was calibrated and validated with real-world daily traffic data by properly adjusting the driving behavior parameters and comparing observed and modeled traffic volumes and queues. The Vehicle-Specific Power (VSP) emission method was applied to model, calculate and illustrate emissions by analyzing vehicle trajectories for the examined scenarios. Results show a statistically significant reduction in emissions for nearly all trips, with emissions decreasing by up to 44% across the intersection and its surrounding areas, and up to 23% at the intersection itself. Emissions are largely influenced by trip duration and traffic efficiency, both of which are enhanced by the improved geometric configuration of the case study intersection. These findings highlight that turbo-roundabouts represent an effective, environmentally sustainable design solution for urban intersections. Full article

This paper studies the Greek interregional road network (GRN) using network, statistical, and empirical analysis. The research aims to extract the socioeconomic information embedded in the topology of the GRN and to interpret to what extent this network serves and promotes regional development. The analysis reveals that the topology of the GRN is subject to spatial constraints, relevant to the theoretical model of the lattice network but with some geographically dispersed hub-and-spoke modules. It also reveals that the network structure is described by an adjusted gravitational pattern, with priority given to serving regions according to their population and, secondarily, geographical remoteness, and that its association with regional variables outlines an elementary pattern of “axial development through road connectivity”. Interesting contrasts between metropolitan and non-metropolitan (excluding Attica and Thessaloniki) cases emerge from the study. Overall, this paper highlights the effectiveness of complex network analysis in modeling spatial-economic and, in particular, transportation networks and promotes the network paradigm in transportation research. Full article

This paper addresses the critical issue of road safety in the indispensable role of transportation for societal well-being and economic growth. Despite global initiatives like Vision Zero, traffic accidents persist, largely influenced by driver behavior. Advanced driver monitoring systems (ADMSs) utilizing computer vision have emerged to mitigate this issue, but existing systems are often costly and inaccessible, particularly for bus companies. This study introduces a lightweight, deep-learning-based ADMS tailored for real-time driver behavior monitoring, addressing practical barriers to enhance safety measures. A meticulously curated dataset, encompassing diverse demographics and lighting conditions, captures 4966 images depicting five key driver behaviors: eye closure, yawning, smoking, mobile phone usage, and seatbelt compliance. Three object detection models—Faster R-CNN, RetinaNet, and YOLOv5—were evaluated using critical performance metrics. YOLOv5 demonstrated exceptional efficiency, achieving an FPS of 125, a compact model size of 42 MB, and an mAP@IoU 50% of 93.6%. Its performance highlights a favorable trade-off between speed, model size, and prediction accuracy, making it ideal for real-time applications. Faster R-CNN achieved an FPS of 8.56, a model size of 835 MB, and an mAP@IoU 50% of 89.93%, while RetinaNet recorded an FPS of 16.24, a model size of 442 MB, and an mAP@IoU 50% of 87.63%. The practical deployment of the ADMS on a mini CPU demonstrated cost-effectiveness and high performance, enhancing accessibility in real-world settings. By elucidating the strengths and limitations of different object detection models, this research contributes to advancing road safety through affordable, efficient, and reliable technology solutions. Full article

This paper discusses the modeling and solution of a cold chain logistics (CCL) problem using artificial intelligence of things (AIoT). The presented model aims to reduce the costs of the entire CCL network by maintaining the minimum quality of cold products distributed to customers. This study considers equipping distribution centers and trucks with IoT tools and examines the advantages of using these tools to reduce logistics costs. Also, four algorithms based on artificial intelligence (AI), including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), and Emperor Penguin Optimizer (EPO), have been used in solving the mathematical model. The analysis results show that equipping trucks and distribution centers with the Internet of Things has increased the total costs by 15% compared to before. This approach resulted in a 26% reduction in operating costs and a 60% reduction in transportation costs. As a result of using the Internet of Things, total costs have been reduced by 2.78%. Furthermore, the performance of AI algorithms showed that the high speed of these algorithms is guaranteed against the high accuracy of the obtained results. So, EPO has achieved the optimal value of the objective function compared to a 70% reduction in the solution time. Further analyses show the effectiveness of EPO in the indicators of average objective function, average RPD error, and solution time. The results of this paper help managers understand the need to create IoT infrastructure in the distribution of cold products to customers. Because implementing IoT devices can offset a large portion of transportation and energy costs, this paper provides management solutions and insights at the end. As a result, there is a need to deploy IoT tools in other parts of the mathematical model and its application. Full article