Vehicle platooning, a beneficial feature of automated driving environments, is likely to affect the lane change behaviour of manually driven vehicles (MVs) because identifying proper gaps in vehicle platoons in the target lane will be more difficult than in the current non‐platooning environment. These interactions between MVs and automated vehicles (AVs) could lead to a higher potential for unstable traffic flow, which is closely associated with traffic safety issues. The objective of this study was to investigate the lane change characteristics of MVs in AV platooning environments using driving simulator experiments. This research found that MV drivers tend to show more radical driving behaviour, as indicated by greater steering magnitude and steering velocity when they change lanes into the platooning vehicles’ lane. Therefore, novel traffic operations strategies to manage MVs and AVs effectively are required to ensure traffic safety, which will be conducted by a future study.

Vehicle platooning service through wireless communication and automated driving technology has become a reality. Vehicle platooning means that several vehicles travel like a train on the road with a minimum safety distance, which leads to the enhancement of safety, mobility, and energy savings. This study proposed a framework for exploring traffic mobility and safety performance due to the market penetration rate (MPR) of truck platoons based on microscopic traffic simulations. A platoon formation algorithm was developed and run on the VISSIM platform to simulate automated truck maneuvering. As a result of the mobility analysis, it was found that the difference in network mobility performance was not significant up to MPR 80%. Regarding the mobility performance of the truck-designated lane, it was found that the average speed was lower than in other lanes. In the truck-designated lane of the on-ramp section, the average speed was identified to be approximately 33% lower. From the viewpoint of network safety, increasing the MPR of the truck platoon has a positive effect on longitudinal safety but has a negative effect on lateral safety. The safety analysis of the truck-designated lane indicated that the speed difference by lane of MPR 100% is 2.5 times higher than that of MPR 0%. This study is meaningful in that it explores traffic flow performance on mobility and safety in the process of platoon formation. The outcomes of this study are expected to be utilized as fundamentals to support the novel traffic operation strategy in platooning environments.

Background: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the wellness of a driver, which reflects the personal characteristics of the driver. Methods: In-depth interviews with taxi drivers are conducted to collect wellness data. The priorities of factors affecting the severity of accidents are derived through a random forest model. In addition, based on the derived priority of variables, various combinations of inputs are set as scenarios and optimal artificial neural network models are derived for each scenario. Finally, the model with the best performance for predicting high-risk taxi drivers is selected based on three criteria. Results: A model with variables up to the 16th priority as inputs is selected as the best model; this has a classification accuracy of 86% and an F1-score of 0.77. Conclusions: The wellness-based model for predicting high-risk taxi drivers presented in this study can be used for developing a taxi driver management system. In addition, it is expected to be useful when establishing customized traffic safety improvement measures for commercial vehicle drivers.