Podium Session 10: Uncovering Physics-Regularized Data Generation Processes for Individual Human Mobility: A Multi-Task Gaussian Process Approach Based on Multiple Kernel Learning

Title: Uncovering Physics-Regularized Data Generation Processes for Individual Human Mobility: A Multi-Task Gaussian Process Approach Based on Multiple Kernel Learning
Authors: Ekin Uğurel, Shuai Huang, Cynthia Chen
Abstract: Passively-generated mobile data has grown increasingly popular in the travel behavior (or human mobility) literature. A relatively untapped potential for passively-generated mobile data is synthetic population generation, which is the basis for any large-scale simulations for purposes ranging from state monitoring, policy evaluation, and digital tw...
Keywords: Synthetic mobile data; Gaussian process; Multiple kernel Learning; Physics-regularization; Travel behavior

By |2024-07-31T01:03:21-04:00June 18, 2024||0 Comments
Read More

Podium Session 10: Calibrating Car-Following Models via Bayesian Dynamic Regression

Title: Calibrating Car-Following Models via Bayesian Dynamic Regression
Authors: Chengyuan Zhang, Wenshuo Wang, Lijun Sun
Abstract: Car-following behavior modeling is critical for understanding traffic flow dynamics and developing high-fidelity microscopic simulation models. Most existing impulse-response car-following models prioritize computational efficiency and interpretability by using a parsimonious nonlinear function based on immediate preceding state observations. Howev...
Keywords: car-following models; dynamic regression; Bayesian inference; microscopic traffic simulation

By |2024-07-31T01:01:39-04:00June 17, 2024||0 Comments
Read More

Podium Session 10: Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach

Title: Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach
Authors: Qiqing Wang, Kaidi Yang
Abstract: This paper proposes a privacy-preserving data fusion method for traffic state estimation (TSE). Unlike existing works that assume all data sources to be accessible by a single trusted party, we explicitly address data privacy concerns that arise in the collaboration and data sharing between multiple data owners, such as municipal authorities (MAs) ...
Keywords: Data Fusion; Federated Learning; Data Privacy; Traffic State Estimation; Traffic Flow Theory

By |2024-07-30T15:48:57-04:00June 17, 2024||0 Comments
Read More

Podium Session 10: Implications of Stop-and-Go Traffic on Training Learning-Based Car-Following Control

Title: Implications of Stop-and-Go Traffic on Training Learning-Based Car-Following Control
Authors: Anye Zhou, Srinivas Peeta, Hao Zhou, Jorge Laval, Zejiang Wang, Adian Cook
Abstract: Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving t...
Keywords: Car-following control; System identification; Behavior cloning; Deep reinforcement learning; Generalizability

By |2024-07-30T15:48:58-04:00June 17, 2024||0 Comments
Read More
Go to Top