Transportation Engineering最新文献

Partial driving automation consists of several subsystems that drivers need to trust to an appropriate level and interact with in a secure manner. To investigate whether drivers adapt their trust functional specific, how they experience the subsystems, and what their preferences are, we conducted two field studies with two partially automated (SAE level 2) vehicles and involving a total of 132 drivers. In both studies, participants drove on a public highway with support from partial driving automation systems for longitudinal control (adaptive cruise control, ACC) and lateral control (lane-keeping assistance, LA). Participants in Study 2 were additionally able to use a lane change assistance (LC) system. The drivers' trust generally increased over time and reflected in how much a participant used the automation (Study 1). Hereby, in both studies drivers had functional specific trust: in Study 1 drivers trusted the ACC more than the LA. In Study 2 drivers in addition had lower trust in the LC than in the LA and the ACC. In their thinking-aloud statements, drivers were more positive about ACC and more critical about LA but reported more interaction difficulties with the ACC (Study 1). In Study 2 participants on average preferred the ACC over the LA and the LA over the LC. The interview responses suggest that the lower preference was due to the perceived reliability (LA) and usefulness (LC). In summary, our results indicate that drivers adapt their trust functional specific when using partial driving automation and have an overall preference for ACC that persists despite a higher number of interaction problems.

Running has become an increasingly popular activity in urban areas over the past few years, attracting people for both recreational and commuting purposes. The main purpose of this study is to highlight the main priorities that should be considered for streetscape design improvements, including running activities as a share of active mobility modes. The benefits of this social phenomenon can be identified in terms of safety, quality of service, aesthetics, and remarkable possibilities regarding the economy of cities. Real GPS track data from physical activity tracking apps were extracted and analysed. Streetscape rehabilitations were proposed and evaluated using the microsimulation model VISWALK. Results highlight the increase in the level of service of active mobility, considering safe and complete streets for all users. Finally, a conceptual design of a runnable city is proposed according to the results.

Adopting automated driving vehicles (AVs) promises to transform transportation systems, yet understanding individual preferences is essential for effective implementation. This study uses decision tree analysis to investigate the factors influencing preferences for partially automated vehicles (AV3) and fully automated vehicles (AV5). Surveys were conducted in the Duhok-Kurdistan Region of Iraq to explore attitudes toward AV technology. Respondents selected their preferred option from AV3 and AV5, with various factors considered in the analysis, including age, gender, frequency of trips, and mobility concerns. Results revealed a preference for AV3, with trip frequency emerging as a key determinant. Older respondents tended towards AV3. The number of trips was the most significant determinant in their decision-making process, with individuals who frequently traveled showing a preference for fully automated AVs. Furthermore, understanding these preferences can inform strategies for promoting AV adoption and integration into transportation systems, shaping future mobility. The study's contributions lie in its localized focus and the identification of key factors influencing AV preferences in a specific region. This insight can assist in developing tailored strategies to promote AV adoption and address mobility challenges.

Disasters can potentially disrupt transportation systems, resulting in complete or partial blockages of specific links, which can limit the available options for transportation. During a disaster, crucial decisions must be made, such as planning the safe evacuation of people from affected areas and strategizing to repair damaged transportation links. The primary objective of this research is to develop a multi-objective optimization framework that can enhance the effectiveness of transportation networks in natural disasters, such as floods. To achieve this, the proposed model incorporates fuzzy set theory since it is challenging to determine parameters like capacity precisely in these situations. The model assumes that some damaged links can be reconstructed within the available resources and budget. The optimization model focuses on minimizing the total evacuation time and reconstruction cost, and its effectiveness is validated using a case study of Cork City with 100 nodes and 141 links. The model is solved, and optimal solutions are generated using an exact method to handle various uncertainty scenarios. The results demonstrate that developing the fuzzy optimization approach as an analytical tool can be used to make critical decisions for evacuation planning and emergency management.

The pedelec 25 (pedal electric cycle) has established itself as a popular mode of transport, with sales rising. Unfortunately, this has also led to an increase in the number of accidents. Analyses of pedelec accidents have been carried out, but most studies are based on technical variables. Neither spatial, geographical factors, for example the terrain, nor socio-geographical aspects, such as whether an accident occurred in a tourist region, have been much addressed. However, geographical exposure can vary significantly. Therefore, we assume that spatial differences contribute to regional variations in numbers of accidents and resulting injuries associated with both bicycle types (pedelecs 25 and conventional). Thus, the aim of this paper is to determine geographical factors that influence severe accidents and to evaluate this influence. We have identified relevant geographical factors and proven their influence on two types of severity: risk of severe bicycle accidents per region (RS) and probability of severe injuries in case of accident (AS). Spatial analyses indicate that for both bicycle types, severity of accident increases in rural, mountainous or tourist regions. Regression analyses of RS provided further evidence: compactness of settlement structure (thus urbanity/rurality), share of tourists, altitude differences (only pedelecs), rate of registered motor vehicles and share of elderly population (only pedelecs) influence the risk of severe bicycle accidents per region. The influence of geographical variables tended to be greater for pedelecs. Regression analyses of AS reveal that geographical factors also have an influence on the probability of severe injuries in case of an accident.

Shared e-scooters were introduced in cities as a promise to provide accessible, affordable, flexible, and sustainable mobility. However, it is still unknown if this mode can contribute to reaching sustainability and whether it is perceived by the population as enabling or hampering such endeavors. Therefore, the present research aims to further understand people's perceptions of shared e-scooters around sustainability issues, to identify the main influential factors for using and not using this mode of transport, and how regulation could affect the demand for shared e-scooter trips. For this, a Revealed Preference (RP) survey was implemented in the city of Braga, Portugal to gather information on the above-mentioned matters, and to provide guidance for future policy and transport planning. The survey results show that most users of shared e-scooters consider this mode of transport to be sustainable (98 %). Still, the perception differs among different socioeconomic groups, especially when considering the gender of respondents (ρ<0.05). Regarding the causes for not using shared e-scooters, the preference to use other modes of transport (77 %), the lack of knowledge on how to ride an e-scooter (37.5 %), and the scarcity of road safety (34.4 %) can negatively affect e-scooter usage, mainly for women (ρ<0.05). In addition, regulation that improves road safety for shared e-scooters can affect greatly its usage, and this mode of transport is not perceived as a promoter of gender equity in urban mobility, which could affect the future implementation of more user-centric regulation and planning strategies.

Emergency vehicles getting stuck in traffic jams have always been a great concern in the cities. This would be more concern when the car is autonomous. So, evaluating the vehicle's maneuver in the presence of an emergency vehicle is required. This problem has been questioned in different ways. This paper attempts to deal with the performance of the vehicle in front of the emergency vehicle by considering the emergency vehicle's right-of-way. To evaluate the behavior of such a vehicle, we propose an algorithm that comprises three parts. In the first part, using a decision tree model, all feasible maneuvers that can be done by the front vehicle in the presence of an emergency vehicle are predicted. In the next part, the performed maneuver is detected using Hidden Markov Model. Finally, the best possible maneuver is compared with the performed maneuver. The proposed algorithm is implemented on a simulator, also developed in this research. The simulator generates different driving behaviors to train the models and evaluate the proposed algorithm.

There is big difference in road death rates among countries from as high as 81.56 in Congo to as low as 0.34 in Norway in terms of per 10 thousand vehicles registered. To find the reason behind this, we compared both graphically and statistically 15 interventions adopted by UN member countries. Comparison of the means of each intervention through Brown-Forsythe Variance Analysis was done. The study found and confirmed that stricter law and its enforcement in the following aspects associate lower road death rate: BAC restriction, speed limits on urban roads, rural roads and motorways, helmet law, seat-belt law, child restraint law, vehicle standard, audits or star rating of new road infrastructure projects, inspections/star ratings of existing road infrastructure projects, design standards for the safety of pedestrians and cyclists, investments to upgrade high risk locations, policies & investment in urban public transport, policies promoting walking and cycling, emergency medicine and trauma surgery. If all countries have road death rate as low as Norway's 0.34, the total global road fatality will be 67,205—or 5 % of the current fatality. Thus it is advisable that interventions adopted by countries with low death rates should be popularized to other countries with high road death rates so as to cut down global total number of road deaths.

Motorcycles are vehicles with the highest risk of involvement in crashes. Based on the integrated data of road crash fatalities reported by the police, hospitals, and insurance companies, motorcycle crashes account for more than 80% of all crashes in Thailand, and the number of fatalities from motorcycle crashes was as high as 84% of the total fatalities from road crashes in the country. This study conducted an in-depth motorcycle crash investigation to understand the collision patterns and characteristics of motorcycle crashes, and to identify the contributing factors affecting the severity of motorcycle crashes in Thailand. The types of opponent vehicles, crash areas, the time of the crash, riders’ age, possession of a motorcycle license, riders’ attention failure, types of human failure, collision avoidance, alcohol use, helmet use, headlight, and traveled speed were discovered to be significant factors affecting motorcycle crash severity. Based on the findings of this study, it has been suggested that to reduce the number of fatalities from motorcycle crashes, the related government agencies should seriously consider how to improve the safe riding skills of motorcycle users. The need for basic safety skills should be practiced by all riders, such as defensive driving skills, knowledge of alcohol risks, proper helmet use, and proper collision avoidance maneuvers. Proper rider training courses are necessary to provide basic knowledge for safe motorcycle operation in traffic, as well as safe traffic strategy and collision avoidance skills.

This study investigates the combination of audio and image data to classify road conditions, particularly focusing on loose gravel scenarios. The dataset underwent binary categorisation, comprising audio segments capturing gravel sounds and corresponding images. Early feature fusion, utilising a pre-trained Very Deep Convolutional Networks 19 (VGG19) and Principal component analysis (PCA), improved the accuracy of the Random Forest classifier, surpassing other models in accuracy, precision, recall, and F1-score. Late fusion, involving decision-level processing with logical disjunction and conjunction gates (AND and OR) in combination with individual classifiers for images and audio based on Densely Connected Convolutional Networks 121 (DenseNet121), demonstrated notable performance, especially with the OR gate, achieving 97 % accuracy. The late fusion method enhances adaptability by compensating for limitations in one modality with information from the other. Adapting maintenance based on identified road conditions minimises unnecessary environmental impact. This method can help to identify loose gravel on gravel roads, substantially improving road safety and implementing a precise maintenance strategy through a data-driven approach.