A Methodology to Attain Public Transit Origin–Destination Mobility Patterns Using Multi-Layered Mesoscopic Analysis

Abstract

Knowledge about mobility patterns has become increasingly important to urban development. In this work, public transit origin-destination (OD) mobility patterns are undergoing meso-level analysis in using the advantages of big data and for the creation of a new planning and decision-based tool. An ensemble clustering method is proposed to abstract the common OD pairs by fully considering link-based information, and the nonnegative tensor factorization model is adopted to effectively extract and visualize quantitatively the mobility patterns of OD pairs. This is attained by using multi-layered analysis such as of traffic demand, traffic accessibility and traffic congestion to enable different visual and quantitative mobility patterns. In the case study of Beijing, these patterns were analyzed and discussed by temporal and spatial factors. The results of the various patterns show explicitly when and where to provide remedies to traffic problems, by time and space. This is analogue, to some extent, to detecting and treating black spots of road accidents. The new developed multi-layered mesoscopic analysis could, therefore, be an important tool for improving urban planning, public transit planning, traffic management, and emergency intervention.

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