Identifying the spatial distribution of public transportation trips by node and community characteristics
place - asia, place - urban, mode - bus, mode - subway/metro, ridership - behaviour, ridership - commuting, planning - methods, technology - passenger information
Public transportation, spatial distribution, smart card data, transit network, PageRank algorithm, community detection, case study
Identifying the spatial distribution of travel activities can help public transportation managers optimize the allocation of resources. In this paper, transit networks are constructed based on traffic flow data rather than network topologies. The PageRank algorithm and community detection method are combined to identify the spatial distribution of public transportation trips. The structural centrality and PageRank values are compared to identify hub stations; the community detection method is applied to reveal the community structures. A case study in Guangzhou, China is presented. It is found that the bus network has a community structure, significant weekday commuting and small-world characteristics. The metro network is tightly connected, highly loaded, and has no obvious community structure. Hub stations show distinct differences in terms of volume and weekend/weekday usage. The results imply that the proposed method can be used to identify the spatial distribution of urban public transportation and provide a new study perspective.
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Li, J., Zheng, P., & Zhang, W. (2020). Identifying the spatial distribution of public transportation trips by node and community characteristics. Transportation Planning and Technology, Vol. 43(3), pp. 325-340.