Identification of Urban Spatial Form Based on Multimode Transit Travel Features

Abstract

The daily trips of urban residents in different transport modes reflect the urban mobility pattern and the latent spatial structure of the city, which is seldom addressed in the literature. A network-based cluster model is applied in this paper based on multimode transit big data. Thus, the urban spatial form can be observed at different levels according to the travel modes and distance, differing from traditional geographic structures. This paper will identify the urban mobility pattern and the corresponding spatial form by taking the Beijing metropolitan area as a study area. Origin–destination data at the traffic analysis zone level were collected, covering shared bike, bus, and metro. The results show that the metro trips reflect the single-center feature of Beijing. However, the multicenter development trend can be revealed by bus and shared-bike data. This paper finds that shared bike supports the establishment of subcenters as a new transport mode. The urban spatial form presents a complex feature of multiple levels and spatial diversity due to the comprehensive effect of multiple travel modes. The study results can provide a basis for the planning of transit network and urban equilibrium land use and development.