Future Transportation

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

The integration of Connected Vehicles into conventional traffic systems presents significant challenges due to the diverse behaviors and objectives of different drivers. Conventional vehicle drivers typically follow User Equilibrium principles, aiming to minimize their individual travel times without considering the overall network impact. In contrast, Connected Vehicle drivers, guided by real-time information from central authorities or private service providers, can adopt System Optimum strategies or Cournot-Nash oligopoly behaviors, respectively. The coexistence of these distinct player classes in mixed-traffic environments complicates the task of achieving optimal traffic flow and network performance. This paper presents a comprehensive framework for optimizing mixed-traffic road networks through a multiclass traffic assignment model. The framework integrates three distinct types of players: conventional vehicle drivers adhering to User Equilibrium principles, Connected Vehicle drivers following System Optimum principles under a central governing authority, and Connected Vehicle drivers operating under Cournot-Nash oligopoly conditions with access to services from private companies. The methodology includes defining a model to achieve optimal mixed equilibria, designing an algorithm for multiclass traffic assignment, formulating strategic games to analyze player interactions, and establishing key performance indicators to evaluate network efficiency and effectiveness. The framework is applied to a real-world road network, validating its practicality and effectiveness through computational results. The extraction and analysis of computational results are used to propose optimal traffic management policies for mixed-traffic environments. The findings provide significant insights into the dynamics of mixed traffic networks and offer practical recommendations for improving traffic management in increasingly complex urban transportation systems. Full article

The emergence of micro-mobility transportation in urban areas has led to a transformative shift in mobility options, yet it has also brought about heightened traffic conflicts and crashes. This research addresses these challenges by pioneering the integration of image-processing techniques with machine learning methodologies to analyze crash diagrams. The study aims to extract latent features from crash data, specifically focusing on understanding the factors influencing injury severity among vehicle and micro-mobility crashes in Michigan’s urban areas. Micro-mobility devices analyzed in this study are bicycles, e-wheelchairs, skateboards, and e-scooters. The AlexNet Convolutional Neural Network (CNN) was utilized to identify various attributes from crash diagrams, enabling the recognition and classification of micro-mobility device collision locations into three categories: roadside, shoulder, and bicycle lane. This study utilized the 2023 Michigan UD-10 crash reports comprising 1174 diverse micro-mobility crash diagrams. Subsequently, the Random Forest classification algorithm was utilized to pinpoint the primary factors and their interactions that affect the severity of micro-mobility injuries. The results suggest that roads with speed limits exceeding 40 mph are the most significant factor in determining the severity of micro-mobility injuries. In addition, micro-mobility rider violations and motorists left-turning maneuvers are associated with more severe crash outcomes. In addition, the findings emphasize the overall effect of many different variables, such as improper lane use, violations, and hazardous actions by micro-mobility users. These factors demonstrate elevated rates of prevalence among younger micro-mobility users and are found to be associated with distracted motorists, elderly motorists, or those who ride during nighttime. Full article

Intersection control systems have been actively studied in recent years as they could potentially replace traffic signals via the utilization of the communication and automatic driving capabilities of connected and autonomous vehicles (CAVs). In these studies, conflicting travel trajectories at intersections that could cause accidents and delays were safely and efficiently avoided by controlling the vehicle’s speed. However, routing approaches for avoiding conflicts at intersections have only been discussed in a few studies. To investigate the feasibility of avoiding intersection conflicts through network-level route allocation, we propose a cooperative route allocation model using reinforcement learning than can model the relationship between the complex traffic environment and optimal route solutions. Models aimed at decreasing the total travel time and those with high delay importance owing to conflicts in travel times were trained and verified under multiple traffic conditions. The results indicate that our model effectively allocates vehicles to their optimal routes, reducing the number of intersection conflicts and decreasing the average travel time by up to approximately 40 s compared to random allocation, demonstrating the potential of reinforcement learning for cooperative route allocation in the management of multiple vehicles. Full article

In the context of increasing research on algorithms for different modules of the autonomous driving stack, the development and evaluation of these algorithms for deployment onboard vehicles is the next critical step. In the development and verification phases, simulations play a pivotal role in achieving this aim. The uncertainty quantification of Autonomous Vehicle (AV) systems could be used to enhance safety assurance and define the error-handling capabilities of autonomous driving systems (ADSs). In this paper, a virtual validation methodology for the control module of an autonomous driving stack is proposed. The methodology is applied to a rule-defined Model Predictive Controller (MPC)-based motion planner, where uncertainty quantification (UQ) is performed across various scenarios, based on the intended functionality within the algorithm’s operational design domain (ODD). The framework is designed to assess the performance of the algorithm under localization uncertainties, while performing obstacle vehicle-overtaking, vehicle-following, and safe-stopping maneuvers. Full article

Florida’s population is projected to grow by 8.8 million residents over the next 25 years. This increase places demands on the transportation system, particularly for mobility-vulnerable populations, potentially impacting equitable transportation options and access for all users. Developing transit solutions for mobility-vulnerable populations may enhance economic activity, health, and well-being. Inquiry is needed to explore transportation stakeholders’ strategies and perspectives on the challenges and opportunities of the existing transportation system. Therefore, this study examined stakeholders’ views on strategies to address user needs, related challenges, and opportunities while identifying gaps, strengths, weaknesses, and threats relevant to addressing transportation among mobility-vulnerable populations. The team conducted 13 semi-structured interviews with city planners, transportation employees, industry stakeholders, and state representatives. Findings indicate gaps in the transportation system, particularly available funding and limited common goals among stakeholders. Participants emphasized the need for enhanced educational resources and collaboration with community members. They identified strengths like a willingness to pilot innovative transit technologies, weaknesses such as unreliable options, opportunities for innovation, and threats, including COVID-19′s impact on transportation use. Understanding transportation stakeholders’ shared challenges and opportunities is crucial for identifying transit needs and developing strategies to reduce disparities for mobility-vulnerable populations. Full article

The development and land use surrounding airports are a concern and interest for airport operators, public communities, business communities, and local authorities. Airport development and operations are governed by both national and international regulations that often extend beyond airport property boundaries. Typical international airports’ regulations, recommendations, and guidance documents (e.g., Noise Exposure and Obstacle Limitation Surfaces) and their national counterparts focus on airport land-use planning. Individual third-party risk assessment of airport operations serves as a complementary tool to these regulations, providing means to assess and manage land-use compatibility and control activities near airport perimeters. Developing robust risk assessment models is essential for defining and validating public safety areas and Runway Protection Zones to ensure land-use compatibility and public safety. Although several quantitative risk assessment models exist, significant differences remain in their methodologies and applications. Over the past 20 to 35 years, most models have evolved based on historical data from aircraft accidents. This article provides a comprehensive review of risk analysis methods for areas surrounding airports and presents a quantitative comparison of two specific approaches, the ENAC/Sapienza and ACRP methods, along with their associated calculation software. Full article

Flexible public transport is defined as a future mobility solution that adapts to user needs and the fluctuating demand patterns that mainly appear in rural areas. However, the temporal variations in traveler preferences for flexible bus services remain largely unexplored in existing research. This constrains the realization of adaptive and customized solutions. Therefore, this study attempts to develop a distinct method for strategic planning of a flexible bus service. To this end, a combinatorial method is undertaken: quantitative social research (questionnaires) and spatial analysis. This combinatorial approach is applied at Korinth and Loutraki in Greece, two significant rural areas neighboring the Athens Metropolitan Area. The results signify that cost and time are the most crucial factors affecting the use of a flexible service. Furthermore, respondents preferred a door-to-door service in the morning and a stop-based service in the afternoon/evening. Concerning route planning, eight routes with different purposes are suggested (e.g., train feeder, touristic, etc.) covering adequately both urban and rural parts of the study area. Notably, the applied methodological approach can be a guideline for planners and policymakers, assisting them in finding effective strategies for introducing flexible public transport in rural areas, especially in contexts where collective transport culture is limited. Full article