Abstract
The ongoing COVID-19 pandemic is creating disruptive changes in urban mobility that may compromise the sustainability of the public transportation system. As a result, worldwide cities face the need to integrate data from different transportation modes to dynamically respond to changing conditions. This article combines statistical views with machine learning advances to comprehensively explore changing urban mobility dynamics within multimodal public transportation systems from user trip records. In particular, we retrieve discriminative traffic patterns with order-preserving coherence to model disruptions to demand expectations across geographies and show their utility to describe changing mobility dynamics with strict guarantees of statistical significance, interpretability and actionability. This methodology is applied to comprehensively trace the changes to the urban mobility patterns in the Lisbon city brought by the current COVID-19 pandemic. To this end, we consider passenger trip data gathered from the three major public transportation modes: subway, bus, and tramways. The gathered results comprehensively reveal novel travel patterns within the city, such as imbalanced demand distribution towards the city peripheries, going far beyond simplistic localized changes to the magnitude of traffic demand. This work offers a novel methodological contribution with a solid statistical ground for the spatiotemporal assessment of actionable mobility changes and provides essential insights for other cities and public transport operators facing mobility challenges alike.
Highlights
Worldwide, cities face pressures to dynamically adapt their public transportation systems to better respond to the increasingly complex changes in urban mobility
An individual trip record is issued with the passenger identifier, timestamp, boarding or alighting location and, for validations inside vehicles, additional details pertaining to the vehicle and route
Three major structural representations of trip record data can be found: georeferenced time series of traffic demand at different locations and routes; end-to-end origin-destination (OD) data mapped from paired entry-and-exit card validations of users along the public transport network; and raw trip/event data
Summary
Cities face pressures to dynamically adapt their public transportation systems to better respond to the increasingly complex changes in urban mobility. An individual trip record is issued with the passenger identifier, timestamp, boarding or alighting location and, for validations inside vehicles, additional details pertaining to the vehicle and route. In cities, such as Lisbon, the ticketing systems of public carriers are consolidated, offering the possibility to trace multimodal user movements along the public transportation network. Three major structural representations of trip record data can be found: georeferenced time series of traffic demand at different locations and routes; end-to-end origin-destination (OD) data mapped from paired entry-and-exit card validations of users along the public transport network; and raw trip/event data. Raw trip data analysis resorts to the introduced approaches for demand/OD traffic series under specific spatiotemporal aggregation criteria
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