Exploring multimodal mobility patterns with big data in the city of Lisbon

Abstract

Worldwide and most European cities such as Lisbon in Portugal are establishing efforts to collect urban traffic data and their situational context for gaining more comprehensive views of the ongoing mobility changes and support decisions accordingly. Hence, cities are becoming sensorized and heterogeneous sources data are being consolidated for monitoring multimodal traffic patterns. Multimodal traffic patterns encompass all major transportation modes (road, railway, inland waterway, and active transport modes such as walking and cycling including other shared schemes). The research reported in this paper aims at bridging the existing literature gap on the integrative analysis of multimodal traffic data and its situational urban context. This work is anchored in the pioneer research and innovation project “Integrative Learning from Urban Data and Situational Context for City Mobility Optimization”(ILU), in the field of artificial intelligence applied to urban mobility that joins the Lisbon city Council and two research institutes. The manuscript is focused on the analysis of spatiotemporal indices of multimodality in passengers’ public transport, offering three major contributions. First, it provides a structured view on the scientific and technical opportunities and challenges for data-centric multimodal mobility decisions to support mobility planning decisions. Second, it outlines key principles for the discovery of multimodal patterns from heterogeneous sources of urban data. Finally, a case study is presented on the spatiotemporal analysis of multimodality indices from available urban data, followed by a discussion on the relevance of cross-modal pattern analysis for the cooperation of public transport operators along with its contribution to enable align supply with passengers’ demand to fit the self-actualizing city dynamics.