To adapt to human-driving habits, this study develops a personalised car-following model
via a memory-based deep reinforcement learning approach. Specifically, Twin Delayed …

Autonomous systems are becoming increasingly relevant in our everyday life. The
transportation field is no exception and the smart cities concept raises new tasks and …

The transportation system, especially the road network, is the backbone of any modern
economy. However, with rapid urbanization, the congestion level has surged drastically …

Autonomous vehicles (AVs) have moved from hype to reality as the penetration and
acceptance rate continues to increase. As they are slowly integrated into traffic with human …

Traffic dynamics on freeways are stochastic in nature because of errors in perception and
operation of drivers as well as the heterogeneity between and within drivers. This …

Driving behaviour in mixed traffic conditions is characterized by vehicle heterogeneity and
lane-less movement. In such traffic conditions, the following response of a vehicle may be …

Reaction delay is an indispensable factor in the operation and control process of drivers in a
car-following scenario. Utilizing trajectory data obtained from an instrumented vehicle, this …

Effective classification of autonomous vehicle (AV) driving behavior emerges as a critical
area for diagnosing AV operation faults, enhancing autonomous driving algorithms, and …

The intelligent transportation system (ITS) can effectively utilize the existing transportation
facilities and improve traffic safety and efficiency. To study and evaluate the dynamic …

Distracted driving significantly affects the efficiency and safety of traffic flow. Modeling
distracted driving behavior in microscopic traffic flow simulation is essential for …