Public transportation multimodality in the city of Lisbon

Abstract

Mobility in major European capitals is not yet sustainable. The need to respond to the ongoing changes in public transportation demand, operationalize safety norms of social distancing, and reach carbon neutrality are prompting cities to reassess public transport systems. Cross-mode synergies in multimodal transport systems can be explored (including convenience, reliability, cost, speed and predictability) to foment public and active modes of mobility. In this context, multimodal traffic pattern analysis can unravel cross-mode vulnerabilities, a possibility that is finally rising with the sensorization of cities, integration of ticketing systems, and consolidation of traffic data sources and their situational context. This work introduces a methodology for the analysis of spatiotemporal indices of multimodality against available situational context, aiding specialists to find vulnerabilities on the public transportation network. Traffic generation poles, large-scale public events, and weather records are the considered sources of situational context. We discuss the role of context-aware multimodality indices to understand demand and its emerging changes, assess cross-modal transfers and preferences, and support cross-mode route and schedule planning. This work further discusses the relevance of multimodal pattern discovery to offer datacentric views ensuring: fully transparent decisions to the citizens; and an objective coordination between carriers, municipalities and authorities. Lisbon is further introduced in this work as a reference case study for context-aware multimodal mobility.