159 citations · 323 across the 11 of their papers we have counts for
13 papers
The Calibration Illusion in Traffic Microsimulation
Cameron Hickert, Maryam Samaei, Athena Wang +5
The transportation community seeks to use calibration methods for highway traffic microsimulation. This is a response to the time-consuming and subjective nature of traditional man…
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…
Interactive Car-Following: Matters but NOT Always
Chengyuan Zhang, Rui Chen, Jiacheng Zhu +3
Following a leading vehicle is a daily but challenging task because it requires adapting to various traffic conditions and the leading vehicle's behaviors. However, the question `D…
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…
Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression
Xinyu Chen, Chengyuan Zhang, Xiaoxu Chen +2
The problem of broad practical interest in spatiotemporal data analysis, i.e., discovering interpretable dynamic patterns from spatiotemporal data, is studied in this paper. Toward…
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…
Social Interactions for Autonomous Driving: A Review and Perspectives
Wenshuo Wang, Letian Wang, Chengyuan Zhang +2
No human drives a car in a vacuum; she/he must negotiate with other road users to achieve their goals in social traffic scenes. A rational human driver can interact with other road…
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…
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang +1
Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowled…
A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos
Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang +1
Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper de…