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.

翻译：交通系统的各类中断以不同方式扰动城市出行。乘客会根据多种因素对这些中断事件做出异质性响应。本研究采用数据驱动方法，探索中断期间的多模式需求动态。我们首先开发了一种方法，通过历史小时级出行需求数据自动检测异常事件。随后，我们对这些异常时段进行聚类分析，以区分中断期间发生的多种多模式需求动态。本研究提供了一种直观工具，用于从出行方式选择角度分类乘客对中断事件的不同响应，并为预测分析奠定基础，以评估不同中断场景下出行方式转移的规模。