2023/07/03 by Ali Shateri Benam, Benam, Ali Shateri, Angelo Furno +3
Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Transportation Planning and Optimization #Urban Transport and Accessibility
paper · pdf · doi:10.48550/arxiv.2307.00877
openalex publication_date 2023/07/03 · openalex created_date 2023/07/05 · openalex updated_date 2026/07/28
Various forms of disruption in transport systems perturb urban mobility in different ways. Passengers respond heterogeneously to such disruptive events based on numerous factors. This study takes a data-driven approach to explore multi-modal demand dynamics under disruptions. We first develop a methodology to automatically detect anomalous instances through historical hourly travel demand data. Then we apply clustering to these anomalous hours to distinguish various forms of multi-modal demand dynamics occurring during disruptions. Our study provides a straightforward tool for categorising various passenger responses to disruptive events in terms of mode choice and paves the way for predictive analyses on estimating the scope of modal shift under distinct disruption scenarios.