6 papers
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…
Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization
Xinyu Chen, Chengyuan Zhang, Xi-Le Zhao +2
Movement speed data from urban road networks, computed from ridesharing vehicles or taxi trajectories, is often high-dimensional, sparse, and nonstationary (e.g., exhibiting season…
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…
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…