Interactive Behavior Modeling for Vulnerable Road Users With Risk-Taking Styles in Urban Scenarios: A Heterogeneous Graph Learning Approach

The deep understanding of the behaviors of traffic participants is essential to guarantee the safety of automated vehicles (AV) in mixed traffic with vulnerable road users (VRUs). Precise trajectory prediction of traffic participants can provide reasonable solution space for motion planning of AV. Early works mainly focused on handcrafting...

Using taxi GPS data for macroscopic traffic monitoring in large scale urban networks: Calibration and MFD derivation

A two-Fluid Model (TFM) of urban traffic provides the macroscopic description of traffic state. The TFMs parameters are hard to calibrate, particularly for the dynamic traffic conditions. This leads to the TFM often being used to compare the quality of service through the plot of stopping time versus trip time of the vehicles in the network....