Extracting Spatiotemporal Demand for Public Transit from Mobility Data

TL;DR

This paper introduces a Gaussian mixture model-based method to identify and analyze spatiotemporal demand patterns for public transit using empirical ridership data, enabling better transit planning and management.

Contribution

It presents a novel, simple approach to decompose transit ridership data into temporal demand profiles, capturing spatial demand clusters and improving demand forecasting accuracy.

Findings

Distinct demand profiles identified in London data

Weighted mixture of profiles accurately predicts station traffic

Method applicable to various urban transit systems

Abstract

With people constantly migrating to different urban areas, our mobility needs for work, services and leisure are transforming rapidly. The changing urban demographics pose several challenges for the efficient management of transit services. To forecast transit demand, planners often resort to sociological investigations or modelling that are either difficult to obtain, inaccurate or outdated. How can we then estimate the variegated demand for mobility? We propose a simple method to identify the spatiotemporal demand for public transit in a city. Using a Gaussian mixture model, we decompose empirical ridership data into a set of temporal demand profiles representative of ridership over any given day. A case of approximately 4.6 million daily transit traces from the Greater London region reveals distinct demand profiles. We find that a weighted mixture of these profiles can generate any station traffic remarkably well, uncovering spatially concentric clusters of mobility needs. Our method of analysing the spatiotemporal geography of a city can be extended to other urban regions with different modes of public transit.