3 papers
stat.AP2026
Quantifying the Causal Operational Determinants of Service Reliability in Urban Rail Transit: Evidence from Panel Double/Debiased Machine Learning
Ying Yao, Nan Zhang, Daniel J. Graham
Urban rail transit reliability is a critical measure of system performance, yet its causal determinants remain poorly quantified due to high-dimensional and interdependent influenc…
cs.LG2024
MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction
Shuang Li, Ziyuan Pu, Nan Zhang +4
Traffic crashes profoundly impede traffic efficiency and pose economic challenges. Accurate prediction of post-crash traffic status provides essential information for evaluating tr…
stat.AP2023
Causal resilience curves: A data-driven framework for quantifying the spatiotemporal impacts of metro service disruptions
Nan Zhang, Daniel Hörcher, Prateek Bansal +1
Urban metro systems move vast numbers of passengers with a high level of efficiency in resource use, but frequently experience disruptions that result in delays, crowding, and dete…