Future Transp., Volume 4, Issue 1 (March 2024) – 16 articles

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. Full article

This paper summarizes the research on countermeasures against driver fatigue based on a comprehensive systematic literature review. Driver fatigue, induced by task monotony during conditional automated driving (CAD, SAE Level 3), can increase the risk of road accidents. There are several measures that counteract driver fatigue and aim to reduce the risk caused by a fatigued driver in the context of CAD. Twelve selected articles focusing on driver fatigue countermeasures in CAD were analyzed. The findings and conclusions are presented, focusing on the countermeasures themselves and their implementation. The countermeasures were critically discussed, especially regarding effectiveness and applicability. They seem to be effective in counteracting driver fatigue. However, the measures are not easily compared because they were studied in various experimental settings and various driver fatigue measurements were used. Different countermeasures have proven to be effective in reducing fatigue during CAD. For this reason, further investigation is needed to gain further insights into their applications, advantages, and disadvantages. Further studies will be conducted to verify the best solution regarding their effectiveness and applicability. Full article

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. Full article

This paper proposes a new method for network screening on rural low-volume roads. These roads are important as they provide critical access to agricultural land and tourist attractions. Most low-volume roads belong to the lowest functional class (local rural roads) and thus are built to lower design standards. Conventional hot spot network screening techniques may not be appropriate for low-volume roads due to the sporadic nature of crashes occurring on these roads. Conversely, sophisticated network screening approaches require extensive roadway and traffic data that are often unavailable to local agencies due to a lack of resources, and/or a lack of technical expertise. This research attempts to address these obstacles to low-volume road network screening which aims to identify candidate sites for safety improvements. The research used an extensive low-volume road sample from the state of Oregon and Empirical Bayes expected number of crashes in developing the proposed models for network screening. The proposed models do not require exact measurements of roadway geometric features as all geometric variables were classified into categories that are easy to compile by local agencies. Further, the method could be used with and without traffic data, without compromising the effectiveness of the network screening process. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

As an emerging, alternative mode of transportation, an in-depth understanding of autonomous shuttle (AS) experiences among all age groups, with and without disabilities, may impact acceptance and adoption of the AS, shape industry guidelines, and impact public policy. Therefore, this study analyzed qualitative data from older (n = 104), younger, and middle-aged (n = 106) adults and people with disabilities (n = 42). The data were obtained by asking participants four open-ended questions from an Autonomous Vehicle User Perception Survey. The result revealed seven themes (Safety, Ease of Use, Cost, Availability, Aging, AS Information, and Experience with AS) for older, younger, and middle-aged adults and six themes (all of the previously mentioned except for Aging) for people with disabilities. Frequency counts indicated priority attention, among all groups, to Safety and Ease of Use. This study provides valuable information pertaining to the experiences, concerns, and motivations of all potential users across age groups and disabilities—and may inform policymakers and industry partners to address their needs more adequately. These findings may contribute to improving and enhancing AS programming, design, and deployment in a safer, accessible, affordable, and tailored way. Full article

Governments and manufacturers anticipate that connected vehicle deployments will reduce accidents, optimize traffic flow, and enhance the driving experience. Although extensive studies focus on the technical aspects of connected vehicles, a gap exists in systematically analyzing the invention trends shaping the field. Insights into these trends is a strategic imperative for policymakers, researchers, and investors alike. This study presents a systematic patent review (SPR) as a robust and adaptable methodological framework for patent analysis. Adapted from the established systematic literature review (SLR), the SPR offers detailed insights into both the thematic and temporal trajectories of innovation in any technology field. The SPR identifies 220 U.S. patents from 2018 to 2022 and classifies them into specific objective categories such as computing resources, cybersecurity, and driving safety, among others. The study notes an increasing focus on driving safety and secure wireless communications, which aligns with broader goals of enhancing safety and situational awareness in transportation. Both the methodological framework and findings address an existing gap in the literature, guide future research, policymaking, and investment in the field of connected vehicles. Full article

Delivery systems are ubiquitous in today’s economy. However, those systems usually operate through purpose-built vehicles, which are inefficient, expensive, and highly harmful to the environment. We propose an optimal route crowd-shipping (OR-CS) system, a delivery service based on crowd transportation. The OR-CS system utilizes service points (SPs) and occasional couriers (OCs) to transfer deliveries. Senders drop packages at SPs, while receivers collect them from different SPs. The system is based on a new algorithm that assigns to each package an optimal route. The route is chosen individually for the package and is personally tailored according to the sender’s preferences and to the predefined routes of the OCs. To assess the real-life feasibility of the system, we developed a general simulator that emulates a city environment with authentic service points specifically selected based on desired attributes. The routes of OCs and the origins and destinations of packages were generated using a random process that differed between simulations. The results indicate that the system can be implemented and utilized. In addition, it yields positive results when the number of OCs surpasses a minimum threshold, which is feasible in most cities, given existing traffic loads. By adopting OR-CS, we can expect lowered delivery costs, reduced traffic congestion, and enhanced environmental sustainability. Full article