Exploring the Multi-modal Demand Dynamics During Transport System Disruptions

TL;DR

This paper presents a data-driven methodology to detect and categorize multi-modal demand changes during transport disruptions, enabling better understanding and prediction of passenger responses to such events.

Contribution

It introduces an automatic anomaly detection and clustering approach to analyze demand dynamics during disruptions, advancing the understanding of passenger mode choices.

Findings

Developed a method to detect anomalous demand patterns

Categorized different demand responses during disruptions

Provided a tool for predicting modal shifts under disruptions

Abstract

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.