Spatiotemporal trip profiles in public transportation reveal city modular structure

Understanding urban mobility patterns within public transportation (PT) systems is key for cities to improve services and promote sustainable mobility. Exploring daily PT riders' traffic flows using anonymized big data is a first step to analyze when and to where people travel. Nevertheless, assessing the degree to which PT usage patterns correlate with the spatial distribution of points of interest (POI) and subsequently affect the city structure is less attempted. Classic approaches use questionnaires and survey data that are costly and often yielding limited statistical significance. The purpose of this research is to understand associations between travel patterns of urban commuters and the functional organization of a city. To this end, we propose a network-constrained temporal distance measure for modeling PT rider travel patterns from smart card data; and further introduce a fully autonomous approach to describe the span of services available at catchment areas around metro stations. The end result is a detailed analytical prescription of spatiotemporal commuting patterns in the city of Lisbon as well as an analytical contextual information that enables us to understand the functional modular structure of urban facilities. Using the city of Lisbon as the guiding case study, the gathered results confirm the hypothesis that PT rider's daily flows along the PT network of metro stations reveal a city modular structure.

Automated summary

Understanding urban mobility patterns within public transportation systems is crucial for improving services and promoting sustainable transportation. This research explores daily travel patterns of public transportation riders using big data, aiming to analyze when and where people travel. The study also examines the correlation between public transportation usage patterns and the spatial distribution of points of interest, and their impact on the structure of a city. By proposing a new measurement method and conducting a case study, this research confirms the modular structure of public transportation networks.