Analyzing year-to-year changes in public transport passenger behaviour using smart card data |
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Affiliation: | 1. Department of Transport & Planning, Delft University of Technology, The Netherlands;2. HTM Personenvervoer N.V., The Netherlands;3. Goudappel Coffeng B.V., The Netherlands;1. School of Civil Engineering, The University of Queensland, Australia;2. Department of Civil and Environmental Engineering, Amirkabir University of Technology, Iran;1. Central for Advanced Spatial Analysis, University College London, UK;2. Future Cities Laboratory, Architecture Department, ETH Zurich, Switzerland;3. Institute of 4D Technologies, FHNW, Switzerland |
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Abstract: | In recent years, there has been increased interest in using completely anonymized data from smart card collection systems to better understand the behavioural habits of public transport passengers. Such an understanding can benefit urban transport planners as well as urban modelling by providing simulation models with realistic mobility patterns of transit networks. In particular, the study of temporal activities has elicited substantial interest. In this regard, a number of methods have been developed in the literature for this type of analysis, most using clustering approaches. This paper presents a two-level generative model that applies the Gaussian mixture model to regroup passengers based on their temporal habits in their public transportation usage. The strength of the proposed methodology is that it can model a continuous representation of time instead of having to employ discrete time bins. For each cluster, the approach provides typical temporal patterns that enable easy interpretation. The experiments are performed on five years of data collected by the Société de transport de l’Outaouais. The results demonstrate the efficiency of the proposed approach in identifying a reduced set of passenger clusters linked to their fare types. A five-year longitudinal analysis also shows the relative stability of public transport usage. |
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Keywords: | Smart card Passenger clustering Mixture model Public transit Longitudinal analysis |
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