Latent classes and attribute bounds in public transport route choice adaptations to unplanned metro disruptions

Abstract

Designing public transport systems resilient to unplanned disruptions requires understanding mode and route choice preferences during such events. This study, for the first time, uses Automatic Fare Collection data to analyse multimodal public transport route choice adaptations to unplanned metro disruptions. Methodologies are developed for identifying commuters and their usual commute route, approximating home/work locations, and identifying disrupted trips and onward post-disruption routes. These methodologies are operationalised in a case study of Lyon, France, where 4569 post-disruption routes are identified, including routes taking bridging buses (bus replacement services). A novel Latent Class Conjunctive Bounded Path Size (LC-CBPS) model is employed to model the route choices, inaugurally exploring both a) whether there are any latent classes of choice preferences, and b) whether travellers impose bounds upon different route attributes in their route consideration. Goodness-of-fit of the LC-CBPS model is benchmarked against other models, and estimated attribute preferences and bounds are analysed.