159 citations · 323 across the 11 of their papers we have counts for
6 papers · 1 filter
Active Simulation-Based Inference for Scalable Car-Following Model Calibration
Menglin Kong, Chengyuan Zhang, Lijun Sun
Credible microscopic traffic simulation requires car-following models that capture both the average response and the substantial variability observed across drivers and situations.…
When Context Is Not Enough: Modeling Unexplained Variability in Car-Following Behavior
Chengyuan Zhang, Zhengbing He, Cathy Wu +1
Modeling car-following behavior is fundamental to microscopic traffic simulation, yet traditional deterministic models often fail to capture the full extent of variability and unpr…
Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior
Chengyuan Zhang, Cathy Wu, Lijun Sun
Accurate and interpretable car-following models are essential for traffic simulation and autonomous vehicle development. However, classical models like the Intelligent Driver Model…
Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression
Chengyuan Zhang, Kehua Chen, Meixin Zhu +2
Learning and understanding car-following (CF) behaviors are crucial for microscopic traffic simulation. Traditional CF models, though simple, often lack generalization capabilities…
Calibrating Car-Following Models via Bayesian Dynamic Regression
Chengyuan Zhang, Wenshuo Wang, Lijun Sun
Car-following behavior modeling is critical for understanding traffic flow dynamics and developing high-fidelity microscopic simulation models. Most existing impulse-response car-f…
Bayesian Calibration of the Intelligent Driver Model
Chengyuan Zhang, Lijun Sun
Accurate calibration of car-following models is essential for understanding human driving behaviors and implementing high-fidelity microscopic simulations. This work proposes a mem…