This study proposes a stability index to quantitatively evaluate the psychological stability of drivers during truck platooning. Truck platooning is a technique in which a manually driven lead truck is followed by automated trucks using V2X communication and onboard sensors. While significant technical advances have been made in truck platooning, research on drivers’ psychological comfort remains limited. Due to the shorter intervehicle time gaps compared to conventional trucking, truck platooning raises concerns regarding drivers’ psychological stability. The proposed index quantifies this aspect and is validated using a driving simulator. Both the stability index calculations and the survey consistently indicate that when the time gap decreases to 0.6 s (approximately 15 m) or less, drivers’ psychological stability deteriorates. However, increasing the time gap beyond 0.6 s does not significantly improve it. Furthermore, this study investigates the effect of the see-through functionality, which provides real-time front-view footage of the preceding vehicle to the following driver. The results confirm that this functionality enhances psychological stability, even under a short time gap. The stability index should serve as a practical indicator for designing truck platooning systems that consider drivers’ psychological stability and may be extended to various advanced driving technologies, including general vehicle platooning.

This study aims to identify the factors that influence the occurrence of traffic accidents to improve motorway traffic safety. Various data, including the frequency of traffic accidents, traffic volume, geometric structure, and congestion level, were collected from individual sections of motorways in South Korea. Using the collected data, a traffic accident frequency prediction model was developed by applying an explainable artificial intelligence (AI)-based approach. The developed deep neural network model was combined with Shapley Additive Explanations to identify the variables that significantly affect the frequency of traffic accidents. The analysis identified five significant factors: segment length, total traffic volume, the proportion of truck traffic, the number of dangerous driving behaviors, and the duration of congestion. The results demonstrate the potential of using explainable AI in predicting traffic accident frequency. By identifying the factors that influence traffic accidents using this model, we can pinpoint areas for improvement, which may ultimately help reduce highway traffic accidents.

This study develops a methodology based on benefit-cost analysis for exclusive lane (i.e., high-occupancy vehicle lane) operation, in consideration of the advantages and disadvantages of the exclusive lane operation of zero emission vehicles, in order to estimate the feasibility of the zero emission vehicle exclusive lane operation and ultimately provide a recommendation to policymakers. While the major benefit of zero emission vehicles on an exclusive lane is travel time savings to all types of vehicles and the major cost of that is the increases of congestion and accident on an exclusive lane, the results show that the benefit in travel time savings was more significant compared to the cost in congestion and accident. This implies that the operation of zero emission vehicles exclusive lane is advantageous and can be an effective strategy for promoting eco-friendly vehicles to address environmental issues.