3 citations · 5 across the 7 of their papers we have counts for
7 papers
A Strictly Proper Scoring-Rule Theory for Calibrating Stochastic Car-Following Models
Shirui Zhou, Shiteng Zheng, Junzhe Ding +2
Problem definition: Fixed parameters and inputs in a stochastic simulator induce a distribution over complete trajectories, not one trajectory. Calibration must assess this distrib…
A Structured Framework for Calibrating Stochastic Car-Following Models: Data Adequacy, Parameter Sensitivity, and Objective Selection
Shirui Zhou, Junzhe Ding, Junfang Tian +3
Calibrating a stochastic car-following model is harder than its deterministic counterpart: the loss itself becomes a random variable, so a favorable random realization can be mista…
Human adaptive variability stabilises collective traffic dynamics
Shirui Zhou, Ching Jin, Junfang Tian +5
Automated systems are often designed on the assumption that replacing human behavioural variability with precise, uniform algorithmic control improves collective performance. In au…
Twenty-Five Years of the Intelligent Driver Model: Foundations, Extensions, Applications, and Future Directions
Shirui Zhou, Shiteng Zheng, Junfang Tian +2
The Intelligent Driver Model (IDM), proposed in 2000, has become a foundational tool in traffic flow modeling, renowned for its simplicity, computational efficiency, and ability to…
Adaptive Kalman-based hybrid car following strategy using TD3 and CACC
Yuqi Zheng, Ruidong Yan, Bin Jia +5
In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative adaptive cruise control (CACC) can fully utilize the advantages of the two algorithms and…
On the calibration of stochastic car following models
Shirui Zhou, Shiteng Zheng, Martin Treiber +2
Recent experimental and empirical observations have demonstrated that stochasticity plays a critical role in car following (CF) dynamics. To reproduce the observations, quite a few…