Journal of Advanced Transportation

In response to the issue of young truck drivers’ weaker perception of potential risks, which makes them more prone to engaging in risky driving behaviors, the direct influence of risk perception on behavior was innovatively considered. An improved theory of planned behavior (TPB) model was developed and a study on risky driving behavior among young truck drivers was conducted. Valid questionnaire data from 330 young truck drivers in China were collected, and the improved TPB model was validated and analyzed through structural equation modeling. The results indicate that the improved TPB model can effectively explain the risky driving behavior among young truck drivers. Specifically, attitudes toward behavior, subjective norms, and perceived behavioral control have significant positive effects on behavioral intention, while behavioral intention and perceived behavioral control have significant positive effects on behavior. In addition, risk perception has a significant negative effect on behavioral intention and behavior. Furthermore, a comparison with the traditional TPB model reveals that the improved TPB model performs better in terms of fit and explanatory power. Fit indices CMIN/DF, RMSEA, and AGFI were optimized by 16%, 18%, and 1.5%, respectively, and there was a 5% increase in explanatory power for behavior variance, validating the rationality and effectiveness of the improved TPB model. This provides decision support for the development of intervention measures for risky driving behavior among young truck drivers in the future.

The connected and automated car-following model can provide a model reference for the queue control algorithm of connected and automated driving and has become a hot research topic in the field of connected vehicles and intelligent transportation. A queue of fast-moving vehicles on urban roads can cause traffic congestion when forced to slow down and, in serious cases, can cause rear-impact accidents. Therefore, this paper introduces information on the time delay of information reception and processing, a collision risk quantification factor reflecting the speed characteristics of the front vehicle, and the speed limit and proposes an improved intelligent driver collision quantification model that considers drastic changes in the speed of the front vehicle. Additionally, the model parameters are calibrated using real vehicle data from urban roads combined with an improved salp swarm algorithm. Finally, the evolution rule of disturbance in the traffic flow under different states is analyzed using a time-space diagram, and the DIDM-CSCL model is compared with the classical IDM. The results show that the improved IDM can better describe the following behavior at the microscopic level, which provides a basis for research related to connected and automated driving.

Accurate vehicle acceleration prediction is useful for developing reliable Advanced Driving Assistance Systems (ADAS) and improving road safety. The existence of driver heterogeneity magnifies the variations in acceleration data, leading to consequential impacts on the precision of vehicle acceleration prediction. However, few studies have fully considered the driver heterogeneity when predicting vehicle acceleration. To model the characteristics of individual drivers, this study first identifies the driving behavior semantics which is defined as the underlying patterns of driving behaviors. The analysis results from the coupled hidden Markov model (CHMM) are used to evaluate the driving behavior differences between different drivers by Wasserstein distance. Then the convolutional neural network (CNN) and long short-term memory (LSTM) network are applied to predict vehicle acceleration. To validate the accuracy of the proposed prediction framework, vehicle acceleration data in car-following conditions is extracted from the safety pilot model deployment (SPMD) dataset. The segmentation results indicate that the CHMM possesses a robust capacity for modeling driving behavior. The prediction results demonstrate that the proposed framework, which incorporates driver clustering before prediction, significantly improves the accuracy of predictions. And the CNN-LSTM outperforms the LSTM in predicting vehicle acceleration during car-following scenarios. The findings from this study can enhance the development of personalized functionalities within ADAS to promote its deployment, thereby improving its acceptance and safety.

Precrash scenario analysis for autonomous vehicles (AVs) is critical for improving the safety of autonomous driving, yet the scenario differences between different driving modes are unexplored. Using the precrash scenario typology of the USDOT, this study classified 484 AV crash reports from the California DMV from 2018 to 2022, revealing the differences in the scenario proportions of the three modes of autonomous driving, driving takeover, and conventional driving in 34 types of scenarios. The results showed that there were significant differences in the proportion of six scenarios such as “Lead AV stopped” and “Lead AV decelerating” among different driving modes . To analyze the relative risk of different driving modes in specific scenarios, an evaluation model of the risk level of AV precrash scenarios was established using the analytic hierarchy process (AHP). The findings indicated that ​ autonomous driving has the highest risk rating and poses the greatest danger in Scenario 1, while conventional driving is associated with Scenario 2b, and driving takeover corresponds to Scenario 3, respectively. In-depth analysis of the crash characteristics and causes of these three typical scenarios was conducted, and suggestions were made from the perspectives of autonomous driving system (ADS) and drivers to reduce the severity of crashes. This study compared precrash scenarios of AV by different driving modes, providing references for the optimization of ADS and the safety of human-machine codriving.

Recently, smart intersections have emerged as a novel intelligent transportation system (ITS) solution that integrates traffic monitoring, optimal signal control, and even traffic safety. Although smart intersections have been prevalent in many cities, there are a few drawbacks in their practical operations. First, there are inevitable delays in transmitting and processing the video data. Second, there is still a need to develop a real-time signal control method leveraging the acquired data from smart intersections. Thus, this study aims to construct edge AI-based smart intersections and to provide their application for traffic signal coordination. To this end, we install smart intersections on three consecutive intersections of Route 45 in Pyeongtaek city, South Korea. The real-time traffic data are collected by an edge AI video analysis model which is compressed and optimized for its operation in on-site edge devices. The optimized model maintains a similar level of accuracy (93.64%), even if the size is reduced by 97.8% compared to the original. Next, we utilize the LT2 model to treat the coordination failure problem in nonpeak hours occurring unnecessary delays of the side-streets with relatively high demands. We complement some constraint conditions in order to consider the compatibility with the current legacy system. The experiment is conducted on a virtual environment of which geometry and traffic demand are configured based on the features of the study site. The numerical results conclude that the optimal offsets calculated by the LT2 model effectively manage bandwidths for multidirectional flows based on the real-time traffic demands collected from the edge AI-based smart intersections. This study contributes to serve high-resolution real-time traffic data using edge AI on smart intersections and to provide a case study for signal coordination.

To reduce the risk of traffic conflicts in merging area, driver’s behavior pattern was analyzed to provide a theoretical basis for traffic control and conflict risk warning. The unmanned aerial vehicle (UAV) was used to collect the videos in two different types of merging zones: freeway interchange and service area. A vehicle tracking detection model based on YOLOv5 (the fifth version of You Only Look Once) and Deep SORT was constructed to extract traffic flow, speed, vehicle type, and driving trajectory. Acceleration/deceleration distribution and vehicle lane-changing behavior were analyzed. The influence of different vehicle models on vehicle speed and lane-changing behavior was summarized. Based on this data, the mean and standard deviation of velocity, acceleration, and variable acceleration were selected as the characteristic variables for driving style clustering. To avoid redundant information between features, principal component dimensionality reduction was performed, and the dimensionality reduction data was used for K-means and K-means++ clustering to obtain three driving styles. The results show that there are obvious differences in the driving behaviors of vehicles in different types of merging areas, and the characteristics of different areas should be fully considered when conducting traffic conflict warnings.