Abstract: Artificial intelligence is developing at a rapid pace, with examples of machine learning already being used in aviation to improve efficiency. In the coming decade, it is likely that intelligent assistants (IAs) will be deployed to assist aviation personnel in the cockpit, the air traffic control center, and in airports. This will be a game-changer and may herald the way forward for single-pilot operations and AI-based air traffic management. Yet in aviation there is a core underlying tenet that ‘people create safety’ and keep the skies and passengers safe, based on a robust industry-wide safety culture. Introducing IAs into aviation might therefore undermine aviation’s hard-won track record in this area. Three experts in safety culture and human-AI teaming used a validated safety culture tool to explore the potential impacts of introducing IAs into aviation. The results suggest that there are indeed potential negative outcomes, but also possible safety affordances wherein AI could strengthen safety culture. Safeguards and mitigations are suggested for the key risk owners in aviation organizations, from CEOs to middle managers, to safety departments and frontline staff. Such safeguards will help ensure safety remains a priority across the industry.

Abstract: The supply chain sector plays a crucial role in driving economic development and globalization. However, the environmental repercussions of logistics and freight transport have become more pronounced. Nowadays, there is an ever-increasing acceptance regarding the opinion that the use of more sustainable urban freight transport has the potential to offer great social, economic, and environmental benefits. This study examines and highlights, via a systematic literature review, the urban environment’s factors that can essentially influence the promotion and usage of cargo bikes for last-mile deliveries in the urban environment. The aforementioned literature review revealed the importance of the quality of the urban environment’s components for the perceived and objective safety of people who make use of cargo bikes. In particular, the most essential factors for the increased use of cargo bikes were found to be traffic load, speed limits, and heavy vehicle traffic. Bicycle infrastructure is also an important factor in bicycling acceptance, as it provides the backbone for a comfortable and safe bicycle ride. Two other factors that can seriously affect cyclists’ sense of safety are street intersections and the width and number of road lanes, as the interaction between cargo bikes and motorized vehicles increases the possibility of traffic accidents. All the above factors need to be considered via various public policies that are not isolated countermeasures but form part of Sustainable Urban Mobility Plans that are currently being implemented in many European cities to ensure continuity and create a sustainable future.

Abstract: Shared micromobility has gained significant attention in the field of transportation engineering in recent years as an environmentally friendly, convenient, and easily accessible transportation mode. Like other medium-sized cities, Birmingham, Alabama implemented a shared micromobility pilot program in 2021 that captured the attention of local travelers. This study examined shared e-scooter usage and associated travel patterns in Birmingham using 2021–2022 field data. From these data, ArcGIS maps were used to showcase trip origins and destinations. To gain a further understanding of e-scooter travel patterns in the study area, zip code and block group densities were calculated. Additionally, a negative binomial regression model was constructed to identify determinants of shared e-scooter trips. The analysis results showed that the usage of shared e-scooters was the highest during the nighttime (9109 trips between 9 p.m. to 10 p.m.), on weekends (20,077 trips on Saturday), and in the fall season (a total of 29,024 trips). Furthermore, the research findings indicated that shared e-scooters experienced their highest utilization rates in areas with a higher proportion of educated and higher-income individuals. These findings suggest that travelers’ mode choice related to the use of micromobility modes is influenced by environmental and demographic factors. Overall, this case study offers valuable contributions to the understanding of the role of shared e-scooters in Birmingham’s transportation landscape and can guide transportation authorities in other medium-sized cities in their efforts to plan for micromobility options.

Abstract: Transport Demand Management (TDM) is crucial in shaping travel behavior and enhancing urban mobility by promoting sustainable transport options. This study represents a comprehensive analysis of employee commuting behavior across seventy-seven cities in Italy, with a focus on Rome as a case study. It investigates some requirements of the workplace travel plan as a TDM strategy for promoting sustainable commuting. An online survey conducted in June 2022 yielded 2314 valid responses, including 1320 from private car drivers. K-means clustering was used to identify distinct behavioral patterns among commuters, revealing four clusters based on demographic factors and transport preferences, such as age, gender, family circumstances, vehicle ownership, willingness to walk, ride bicycles, or e-scooters, and reasons for mode choice. This study analyzed Rome’s public transport network, land use, and private car use. Results underscore the need for tailored transport policies that enhance inclusivity and accessibility, especially for employees with family members who cannot commute independently. A spatial analysis of Rome reveals significant infrastructure deficiencies, such as complicated transfers and inaccessible stations, which discourage PT use. Future research should explore the impact of remote work and psychological factors and conduct in-depth subgroup analyses to inform inclusive transport policy development.

Abstract: Advances and cost reductions in Light Detection and Ranging (LiDAR) sensor technology have allowed for their implementation in detecting vehicles, cyclists, and pedestrians at signalized intersections. Most LiDAR use cases have focused on safety analyses using its high-fidelity tracking capabilities. This study presents a methodology to transform LiDAR data into localized, verified, and linear-referenced trajectories to derive Purdue Probe Diagrams (PPDs). The following four performance measures are then derived from the PPDs: arrivals on green (AOG), split failures (SF), downstream blockage (DSB), and control delay level of service (LOS). Noise is filtered for each detected vehicle by iteratively projecting each sample’s future location and keeping the subsequent sample that is close enough to the estimated destination. Then, a far side is defined for the analyzed intersection’s movement to linear reference sampled trajectories and to remove those that do not cross through that point. The technique is demonstrated by using over one hour of LiDAR data at an intersection in Utah to derive PPDs. Signal performance is then estimated from these PPDs. The results are compared to those obtained from comparable PPDs derived from connected vehicle (CV) trajectory data. The generated PPDs from both data sources are similar, with relatively modest differences of 1% AOG and a 1.39 s/veh control delay. Practitioners can use the presented methodology to estimate trajectory-based traffic signal performance measures from their deployed LiDAR sensors. The paper concludes by recommending that unfiltered LiDAR data are used for deriving PPDs and extending the detection zones to cover the largest observed queues to improve performance estimation reliability.

Abstract: This research germinates from the statement that cities need to solve the impacts caused by freight transport to improve their sustainability by implementing a set of city logistic measures. Urban freight distribution through environmentally friendly vehicle measures is one of the main sustainable actions being implemented worldwide, with a significant potential to reduce the congestion and pollution levels according to the assessment performed around the world. In this context, this paper aims to explore the use of railways for urban freight transport and then focuses on the potential of shifting from a road to railway system, which uses an advanced demand modelling framework specified and calibrated according to the results of surveys carried out in the study area. Subsequently, the potential benefits of introducing this urban freight transport through the metro system in Rome (Italy) are investigated, showing significant positive effects, both in terms of operational and external costs.

Abstract: Split failures have been a conventional method to estimate overcapacity at signalized intersections. Currently, split failures are estimated from high-resolution (HR) traffic signal controller event data by evaluating occupancy at the stop bar. Recently, a technique that uses high-fidelity connected vehicle (CV) trajectory data to estimate split failures has been developed and has been adopted by some agencies. This paper compares cycle-by-cycle split failure estimations from both techniques for 42 signalized intersections across central Indiana. CV trajectories were assigned to a cycle based on their arrival characteristics. Then, HR and CV data were used to determine whether each cycle split fails. Finally, agreements and discrepancies were quantified and evaluated. The results obtained after analyzing over 35,000 cycles showed that both techniques produce similar overall split failure estimations. The HR and the CV methods identified 4% and 3% of all cycles as split failing, respectively. However, only 23% of all cycles determined as split failing with the HR approach were also identified as split failing with CV data. Similarly, only 30% of all cycles determined as split failing with the CV approach were also identified as split failing with the HR approach. This indicates significant discrepancies on a cycle-by-cycle basis. Using CV data to identify split failing cycles produces more conservative results and is based on the entire experience of traversing vehicles. If data are available, the authors recommend the CV approach when allocating limited agency resources for operational improvement activities.

Abstract: Introducing Advanced Air Mobility (AAM) as a novel transportation mode poses unique challenges due to limited practical and empirical data. One of these challenges involves accurately estimating future passenger demand and the required number of air taxis, given uncertainties in modal shift dynamics, induced traffic patterns, and long-term price elasticity. In our study, we use mobility data obtained from a Dresden traffic survey and modal shift rates to estimate the demand for AAM air taxi operations for this regional use case. We organize these operations into an air taxi rotation schedule using a Mixed Integer Linear Programming (MILP) optimization model and set a tolerance for slight deviations from the requested arrival times for higher productivity. The resulting schedule aids in determining the AAM fleet size while accounting for flight performance, energy consumption, and battery charging requirements tailored to three distinct types of air taxi fleets. According to our case study, the methodology produces feasible and high-quality air taxi flight rotations within an efficient computational time of 1.5 h. The approach provides extensive insights into air taxi utilization, charging durations at various locations, and assists in fleet planning that adapts to varying, potentially uncertain, traffic demands. Our findings reveal an average productivity of 12 trips per day per air taxi, covering distances from 13 to 99 km. These outcomes contribute to a sustainable, business-focused implementation of AAM while highlighting the interaction between operational parameters and overall system performance and contributing to vertiport capacity considerations.

Abstract: Shared micromobility has gained significant attention in the field of transportation engineering in recent years as an environmentally friendly, convenient, and easily accessible transportation mode. Like other medium-sized cities, Birmingham, Alabama implemented a shared micromobility pilot program in 2021 that captured the attention of local travelers. This study examined shared e-scooter usage and associated travel patterns in Birmingham using 2021–2022 field data. From these data, ArcGIS maps were used to showcase trip origins and destinations. To gain a further understanding of e-scooter travel patterns in the study area, zip code and block group densities were calculated. Additionally, a negative binomial regression model was constructed to identify determinants of shared e-scooter trips. The analysis results showed that the usage of shared e-scooters was the highest during the nighttime (9109 trips between 9 p.m. to 10 p.m.), on weekends (20,077 trips on Saturday), and in the fall season (a total of 29,024 trips). Furthermore, the research findings indicated that shared e-scooters experienced their highest utilization rates in areas with a higher proportion of educated and higher-income individuals. These findings suggest that travelers’ mode choice related to the use of micromobility modes is influenced by environmental and demographic factors. Overall, this case study offers valuable contributions to the understanding of the role of shared e-scooters in Birmingham’s transportation landscape and can guide transportation authorities in other medium-sized cities in their efforts to plan for micromobility options.

Abstract: Background: Consumer preferences are one of the most dominant factors shaping the implementation of last-mile delivery innovations. This study investigates how innovative delivery methods affect consumers’ last-mile delivery preferences and focuses on understanding consumer expectations for integrating these methods. Methods: A discrete choice experiment was implemented. Data from 480 participants in Istanbul were analyzed by multinomial logistic regression using the Apollo package in R Studio. Results: For the selection of delivery to the address, the delivery price, delivery term, and the delivery time window are significant attributes. However, the delivery method and information and tracking attributes do not emerge as decisive attributes in this choice. For the selection of delivery points, the delivery price, delivery term, distance, pick-up accessibility, information and tracking, and the delivery method have been identified as key influencing attributes. Conclusions: The study suggests actionable recommendations aimed at improving negative perceptions of delivery points, advocating for harmonized regulatory frameworks, strategically integrating technology, and developing delivery schedules to enhance overall service quality. This study fills a gap in the literature by examining different last-mile delivery attributes and locations. It also provides valuable insights in understanding consumer expectations and innovative delivery methods.

Abstract: This paper describes the design of a control unit for efficient battery charge management in battery electric vehicles (BEVs). The system design aims at controlling the performance of the charging process of dual lithium-ion battery blocks in electric vehicles, with a main battery that powers the vehicle and an auxiliary one for servicing the ancillary equipment. In this paper, we design and analyze the protocol of a control unit that operates and regulates the battery charge in electric vehicles to obtain optimum performance. The so-designed system enhances the battery charge process and protects the main battery from capacity reduction, thus enlarging the driving range of the electric vehicle. We design a specific protocol for an electric circuit that reproduces the structure of the battery charge system of an electric vehicle. The control system improves the efficiency of the auxiliary battery charge by 4.5%. The theoretical simulation matches experimental values in a simulation test by 98.4%.

Abstract: Further reducing greenhouse gas and pollutant emissions from road vehicles is a major task for the automotive industry. Stricter regulations regarding emissions and fleet fuel consumption require the continuous development of new powertrains and methods. In particular, the combination of hybrid powertrains on the technical side and the focus on real driving emissions (RDE) on the legislative side pose significant challenges to the vehicle calibration process. Against this background, new test methods and environments are being investigated to counteract the high number of interactions between hybrid drive systems and quasi-infinite test conditions due to RDE. Complementary to new test environments, innovative methods for data analysis are needed that allow the exploitation of the complete potential of measurement data. The application of such a method in the field of emission calibration is presented in this paper. For this purpose, a clustering method (HDBSCAN) is applied to critical sequences from emission tests. Within this presentation, the clustering process is based on a single signal only. This paper shows how signals of various characteristics can be processed with dynamic time warping and generically structured with the clustering method used. Here, 959 single events are automatically categorized into 24 clusters. This provides a new basis for system evaluation, enabling the automatic identification, categorization, and prioritization of calibration weaknesses. Using twelve signals of different characteristics, the generic usability of the clustering method is demonstrated.

Abstract: Integrating autonomous vehicles (AVs) into public roads presents profound implications for pedestrian safety and the broader acceptance of this emerging technology. This work examines the complex interactions between AVs and pedestrians, a dynamic influenced by the variability of pedestrian behaviors and the absence of traditional communication mechanisms, such as eye contact and gestures, commonly relied upon in human-driven scenarios. Given the nascent stage of AV deployment, this research addresses the challenges of evaluating AV−pedestrian interactions amid safety concerns and technological limitations. We review and synthesize global research on pedestrian behavior in the context of AV technology to track changes in pedestrians’ acceptance over time and identify the factors driving these shifts. Additionally, this review incorporates insights from transportation authorities to highlight potential safety issues and the need for innovative communication strategies that ensure safe interactions between pedestrians and AVs. By analyzing these factors, the research aims to contribute to the development of guidelines and communication protocols that enhance pedestrian safety and facilitate the integration of AVs into urban environments.

Abstract: A bikeshare system offers a convenient and cost-effective transportation service, providing shared bicycles for short-term use by individuals. It promotes affordability for users while fostering a healthier environment. By offering an alternative for those without access to private vehicles, it helps mitigate the rise in private car usage. Bike sharing also provides an important first-mile/last-mile commuting option. This study focuses on investigating the effects of the COVID-19 pandemic outbreak on bikeshare ridership, with a specific case study centered around Houston, Texas. The employed methodology involves a descriptive analysis and Negative Binomial regression modeling to uncover the relationship between the dependent variable (ridership) and the independent variables. The descriptive analysis revealed an overall increase in ridership during the COVID-19 period in 2020. Notably, longer duration trips were substantially higher in 2020 compared to 2019. Furthermore, the majority of trips occurred during off-peak hours, followed by evening and morning peak periods. Through regression analysis, this study found that the COVID-19 pandemic had a statistically significant positive impact on average daily ridership, with the number of COVID-19 cases positively influencing ridership levels. Additionally, the weekend indicator had a statistically significant positive impact on the average daily ridership. On the other hand, the temperature indicator did not show any significant impact on the average daily ridership, while precipitation had a statistically significant negative impact, leading to decreased ridership levels. The study highlights the significance of various factors in influencing bikeshare usage, contributing to a better understanding of urban transportation dynamics during such unprecedented times.

Abstract: This study presents a carbon dioxide (CO2), exhaust particulate matter (PM2.5) and nitrogen oxide (NOx) tailpipe emission analysis of passenger cars in nine countries, representing different world regions up to 2050 using a bottom-up calculation method. A diffusion model is used to analyze the development of different drivetrain/fuel technologies in the respective vehicle stocks of each world region. Drivetrain- and country-specific emission factors are weighted according to the modelled stock compositions. The obtained stock fleets’ average emission factors are multiplied by the transport demand in order to obtain the total passenger car emissions. Our findings reveal global passenger car CO2, NOx and PM2.5 emissions decrease by approximately 45%, 63% and 54%, respectively, between 2015 and 2050. Gasoline will remain a significant energy carrier in 2050 with about a 25% stock share. However, electric vehicles will be in the lead, especially after 2040. Additionally, rising transport demand offsets emission reductions in some regions. This study aims to provide global and regional insights into future emissions trends and their driving factors.

Abstract: This paper offers a novel view for managing and controlling the movement of driverless, i.e., autonomous, vehicles by converting this movement to a simulated train movement moving on a rail track. It expands on the “virtual track” theory and creates a model for virtual track autonomous vehicle management and control based on the ideas and methods of railway train operation. The developed model and adopted algorithm allow for large-scale autonomous driving vehicle control on the highway while considering the temporal-spatial distribution of vehicles, temporal-spatial trajectory diagram optimization, and the management and control model and algorithm for autonomous vehicles, as design goals. The ultimate objective is to increase the safety of the road traffic environment when autonomous vehicles are operating in it together with human-driven vehicles and achieve more integrated and precise organization and scheduling of these vehicles in such mixed traffic conditions. The developed model adopted a “particle swarm” optimization algorithm that is tested in a hypothetical network pending a full-scale test on a real highway. The paper concludes that the proposed management and control model and algorithm based on the “virtual track” theory is promising and demonstrates feasibility and effectiveness for further development and future application.

Abstract: Future adoption of shared automated vehicles (SAVs) should lead to several societal benefits, but both automated vehicles (AVs) and ridesharing must overcome their barriers to acceptance. Previous research has investigated age differences in ridesharing usage and factors influencing the acceptability and acceptance of AVs. Further complicating our understanding of SAV acceptance, much of the public lack accurate knowledge and/or actual experience regarding AVs. In this study, we employed a 3 (age group) × 4 (video condition) longitudinal mixed experimental design to investigate age differences in anticipated SAV acceptance after viewing different types of introductory videos related to AVs (educational, experiential, or both) or currently available ridesharing provided by transportation network companies (control). Younger, middle-aged, and older adults were randomly assigned to watch (1) an educational video about SAV technologies and potential benefits, (2) an experiential video showing an SAV navigating traffic, (3) both the experiential and educational videos or (4) a control video explaining how current ridesharing services work. Attitudes toward SAVs (intent to use, trust/reliability, perceived usefulness, perceived ease of use, safety, desire for control, cost, authority, media, and social influence) were measured before and after viewing the video(s). Significant differences in how SAV attitudes changed were found between the educational and experiential video conditions relative to the control video and between different age groups. Findings suggest that educational and/or experiential videos delivered in an online format can have modest but significant improvements to their viewers’ attitudes toward SAVs—particularly those of older adults.

Abstract: International trade is a critical part of the United States economy. Land border crossings between the United States and Mexico accounts for a large proportion of the USD 779 billion in trade between these two countries. Monitoring and managing the operations of these land border crossings is critical for ensuring efficient trade and providing appropriate security. This paper examines the opportunity to use connected vehicle data to monitor the travel time delay of passenger vehicles crossing the border for system level assessment across 26 border crossing locations over an analysis period of 25 days in August 2020. A sample size of 51,341 trips from the US to Mexico and 41,708 trips from Mexico to the US were used in this study. Furthermore, 97% trips to the US and 76% trips to Mexico experienced delays. The average delay was 34 min for trips to the US compared to only 2 min for trips to Mexico. In terms of the predictability of border crossing times, there was also substantial variation by direction. The interquartile range of vehicle delay from the US to Mexico was 2 min, while the interquartile range of delay for vehicles travelling from Mexico to the US was 46 min. Border crossings were also ranked using four performance metrics—trip counts, median delay, delayed trip counts and total delays in vehicle hours. Methods for summarizing delay trends by time of the day and day of the week to identify time windows of interest are also presented. Land border crossing operations have a significant influence on security and economic efficiency. We believe the techniques presented in this paper provide a scalable methodology for providing near real-time factual data on border crossing delays that provide important information for land border transport-managing stakeholders to make informed management decisions that balance security and economic efficiency.

Abstract: Distracted driving is a major cause of road traffic crashes in Yaoundé. This is partly due to the scarcity of enforcement and a lack of evidence and investigation using the distraction safety performance indicator (SPI), hindering evidence-based interventions. This study aimed to address this evidence gap by evaluating the distraction SPI using a proven methodology. Data on distracted driving (handheld mobile device; interaction; eating/smoking/drinking) were collected from roadside observations on 36 randomly selected road sections carefully spread to cover the city. SPIs were computed and weighted with traffic volume to ensure the representativeness of the values. A total of 41,004 drivers were observed (38,248 in cars; 1116 in vans; 977 in trucks; 663 in buses). The prevalence of distracted driving in Yaoundé is 13.69% for the three distractions type combined. The prevalence is 7.84% for interaction, 4.89% for handled mobile device usage and 0.96% for eating/smoking/drinking. Leveraging these insights, a seven year (2024–2030) fighting strategy aimed at halving the prevalence was developed. The strategy contains interventions including legislation/enforcement, which have been proven to be effective. This study, pioneered in Yaoundé, provides stakeholders with evidence of the issue and measures to implement and can also be used when developing a road safety strategy. Future research should consider investigation at national level.

Abstract: This paper provides a detailed literature review of the environmental implications of vehicle platooning, a topic gaining significant attention in transportation. While previous reviews have focused on the safety, planning, fuel economy, and microsimulation aspects of platooning, this paper delves into environmental aspects. It identifies a lack of research adopting a holistic approach to transport and environmental benefits and emphasizes the need for further research to enhance vehicle efficiency and improve air quality and health conditions. This study traces the historical evolution of platooning, highlighting the shift in research focus over the decades. It advocates for more research on platooning’s environmental aspects, particularly pollutant emissions and air quality. The primary contributions of this work are threefold and include the following: firstly, it delineates simulation methodologies for platooning and the associated pollutant emissions; secondly, it offers a critical assessment of the existing literature on vehicle emissions, fuel consumption, and energy savings; and thirdly, it illuminates the prospective research challenges within the specialized domain of vehicle platooning.

Abstract: Greenhouse gas (GHG) emissions from transport contribute significantly to climate change. Some of the transport policies with the greatest potential to mitigate climate change are related to zero-emission vehicles. This study aimed to analyse the different factors, and their importance, influencing purchase decisions for battery electric vehicles (BEV). Experts’ perceptions were collected with a Delphi study consisting of a two-round survey to assess factors that would increase the probability of a petrol- or diesel-car owner purchasing a BEV in Finland in the year 2025. Increasing the possibilities for home charging and the provision of a purchase subsidy were seen as the most important factors. Public fast charging and the difference in use costs between current technology vehicles and BEVs were also recognised as important factors. Existing systems of financial instruments and policies must be constantly evaluated and updated due to the evolving BEV industry.

Abstract: Variable Speed Limit (VSL) systems aimed at reducing congestion and improving safety performance have been implemented around the world in previous years. However, field studies have shown controversial results regarding traffic performance improvement. This study integrates statistical testing methods and clustering techniques for assessing the effect of a non-mandatory VSL system on traffic flow performances on a 14-km portion of the Padua–Mestre motorway in Italy. Statistical analysis is conducted on the observed speeds, collected for almost one year, to identify any significant differences provided by VSL activation. The changes in global motorway performances induced by the VSL in typical traffic patterns under recurring congestion are assessed using both statistical tests and two specific clustering algorithms, namely K-means and DBSCAN. The results indicate that the VSL system effectively affects the observed speeds and alleviates congested conditions: the observed reduction in mean travel time ranges is around 4% with the VSL system active across various lanes; the standard deviation of vehicular speeds witnessed a decrease of 12% to 20% in the most congested segments, while no notable distinction is observed in traffic flows.

Abstract: Despite the growing adoption of Mobility as a Service (MaaS) in urban transportation systems, standard monitoring methods for evaluating its impact and effectiveness still need to be developed. This study proposes a quantitative state of MaaS analysis based on mobility market indicators and macroeconomic metrics to generate a MaaS Status Index (MSI). The intention is to introduce a standardised quantitative methodology for systematically assessing and comparing the state of MaaS in urban mobility systems. The MSI aims to quantitatively capture the economic, social, technological, and infrastructural conditions relevant to MaaS implementation. The methodology includes four steps: identifying relevant mobility markets, defining mobility market metrics, integrating macroeconomic metrics, and deriving the MSI formula. We apply the MSI methodology to the Austrian mobility market as a case study, demonstrating its practicality in assessing MaaS readiness and highlighting specific challenges and opportunities within the Austrian mobility system. The analysis covers the present (2017–2022) and the projected future (2023–2028). The findings indicate that the proposed MSI is an effective tool for evaluating the readiness of MaaS implementation.

Abstract: 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.

Abstract: Urban transport has evolved based on three main visions: automobility, multimodality, and accessibility. The first dominates North American cities; the second, European; the third, significantly discussed in the literature, is still in the early stages of practical development, with a few limited examples. Each of the first two visions has an aligned planning paradigm to support aspirational goals and future directions. But implementation has been disappointing, owing to the appearance of anomalies; that is, unanticipated and unexplained mismatches between the vision and the paradigms that refuse to be resolved. The attempts are self-defeating, and result, for example, in congestion and road accidents. A review of the literature with some new insights can shed light on the problems and the anomalies of these two visions. For the third vision, a new paradigm has been proposed based on accessibility and polycentric and multi-timed cities, promising new insights and breakthroughs in the way of thinking about transport and cities. Some practical examples of accessibility cities are presented with a focus on people, places, land use changes, telecommunications, transportation demand management (TDM), and public and non-motorized transport. Some minor anomalies are discussed. In conclusion, enhancing accessibility in cities is crucial for creating more inclusive and sustainable urban environments that are less dependent on cars, but this vision and this paradigm still require further development to be accepted and implemented.

Abstract: The autonomous vehicle is an innovative field for the application of machine learning algorithms. Controlling an agent designed to drive safely in traffic is very complex as human behavior is difficult to predict. An individual’s actions depend on a large number of factors that cannot be acquired directly by visualization. The size of the vehicle, its vulnerability, its perception of the environment and weather conditions, among others, are all parameters that profoundly modify the actions that the optimized model should take. The agent must therefore have a great capacity for adaptation and anticipation in order to drive while ensuring the safety of users, especially pedestrians, who remain the most vulnerable users on the road. Deep reinforcement learning (DRL), a sub-field that is supported by the community for its real-time learning capability and the long-term temporal aspect of its objectives looks promising for AV control. In a previous article, we were able to show the strong capabilities of a DRL model with a continuous action space to manage the speed of a vehicle when approaching a pedestrian crossing. One of the points that remains to be addressed is the notion of discrete decision-making intrinsically linked to speed control. In this paper, we will present the problems of AV control during a pedestrian crossing, starting with a modelization and a DRL model with hybrid action space adapted to the scalability of a vehicle-to-pedestrian (V2P) encounter. We will also present the difficulties raised by the scalability and the curriculum-based method.

Abstract: In Yaoundé, Cameroon, where walking dominates transport modes, pedestrian safety remains an issue as pedestrians account for a fair share of road traffic casualties, partly due to the lack of walking policies and pedestrian facilities safety data, hindering targeted intervention. This study used a pedestrian safety index (PSI) and the Global Walkability Index (GWI) to investigate 12 road segments frequented by diverse pedestrian groups. Indexes were graded from E—lowest to A—highest and analyzed using description and rank correlation. Main safety issues included lack of adequate and accessible sidewalks, bollards, pedestrian crossings, signage, shade, and street lighting. Only one segment (R7) achieved grade C, while the remainder scored D or E, indicating poor pedestrian safety conditions and an unpleasant walking experience. The correlation coefficient (0.69) between the PSI and GWI at a 99% significance level validated the safety assessment, providing confidence in the results. A seven-year (2024–2030) safety strategy is proposed to improve all roads to grade B. This strategy contains several interventions, including engineering improvement, which have been proven effective. This study offers evidence for city officials to improve pedestrian safety and informs walking policies and the implementation of upcoming projects. Future research should quantify the recommendations’ benefits and validate indexes with crash or conflict data.

Abstract: 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.

Abstract: The swap-body vehicle routing problem (SBVRP) represents a specialized extension of the traditional vehicle routing problem (VRP), incorporating additional practical complexities. Effective fuel consumption management and the scheduling of multiple vehicle trips are pivotal strategies for reducing costs and ensuring the sustainability of distribution systems. In response to the acceleration of urbanization, the rising demand for logistics, and the deteriorating living environment, we introduce an SBVRP considering fuel consumption and multiple trips to enable greener, cheaper, and more efficient delivery methods. To tackle the SBVRP, we propose a hybrid multi-population genetic algorithm enhanced with local search techniques to explore various areas of the search space. Computational experiments demonstrate the efficiency of the proposed method and the effectiveness of its components. The algorithm developed in this study provides an optimized solution to the VRP, focusing on achieving environmentally friendly, sustainable, and cost-effective transportation by reducing energy consumption and promoting the rational use of resources.