Exploring the Multi-modal Demand Dynamics During Transport System
  Disruptions
Ali Shateri Benam Angelo Furno Nour-Eddin El Faouzi
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.