In recent years, great efforts have been devoted to deep imitation learning for autonomous
driving control, where raw sensory inputs are directly mapped to control actions. However …

Collective movement simulations are challenging and important in many areas, including life
science, mathematics, physics, information science and public safety. In this survey, we …

As more and more trajectory data become available, their analysis creates unprecedented
opportunities for traffic flow investigations. However, observed physical quantities like speed …

Vehicle trajectory anomaly detection plays an essential role in the fields of traffic video
surveillance, autonomous driving navigation, and taxi fraud detection. Deep generative …

With the exponential growth of mobile users, ensuring high-quality network coverage has
become paramount. Large-scale mobile networks consist of numerous base stations (BSs) …

The smart road stud (SRS) system can improve the driver's overtaking behavior through light
guidance, which shows great potential in raising the traffic efficiency of two-way two-lane …

High-resolution trajectory data from the intersection shared space (ISS) is an ideal resource
for testing autonomous driving and studying microscopic traffic flow theory in urban road …

Realistic crowd simulation has been pursued for decades, but it still necessitates tedious
human labour and a lot of trial and error. The majority of currently used crowd modelling is …

A crowd simulator that generates realistic crowds with various movement patterns and
environmental adaptability is urgently desired but underdeveloped for the applications of …

Intelligent transportation modes such as autonomous robots and electric scooters with ride
assistance are gaining popularity, but their integration into public spaces poses challenges …