An in-depth analysis of the behaviour patterns and characteristics of public transport travelers from the perspective of travel behaviour is of great significance for targeted improvement of public transport service levels. The individual characteristics of travelers were obtained by the RP survey, and the commuting travel chain of public transport is extracted by the association matching method based on the analysis of massive smart card transaction data. The 9 indicators including travel days, daily travel frequency, travel integrity and individual socio-economic attributes are selected to describe commuters’ public transport usage behaviour based on exploratory factor analysis. Based on the discretization of continuous variables, the DBSCAN clustering algorithm is used to construct the category identification model of commuters’ public transport usage behaviour. The results show that the constructed category identification algorithm can effectively identify the categories of public transport usage behaviour. Public transport commuters are divided into three categories: high, medium and low usage groups of public transport, accounting for 54.2%, 33.7% and 12.1% respectively. The low usage group of public transport is regarded as the group with the greatest potential to improve public transport behaviour, it is necessary to improve the usage bahaviour of such public transport commuters from two dimensions of traffic restriction policy and service level. The research can help traffic management departments to accurately improve the quality and attractiveness of public transport services.
Category identification of urban public transport commuter groups based on the DBSCAN clustering algorithm
International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2024) ; 2024 ; Lanzhou, China
Proc. SPIE ; 13645 ; 136450G
16.06.2025
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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