The construction of purpose-specific OD matrices using public transport smart card data

Around the world, more and more public transport operators opt for automated fare collection systems by means of smart cards. The data registered by such a system contain valuable information for transport planning. This research aims at increasing the usability of this data for the description of public transport travel demand. Travel demand is generally described by OD matrices, specified by purpose, mode and time-of-day. Accurate stop-based matrices by mode and time-of-day can be derived from the transactions registered by the smart card system. However, smart card data lack information of activity locations (Origins and Destinations), as access and egress legs are not available. Moreover, the travel purpose is unknown, limiting the interpretability of the data. In order to add this information, this research investigates the enrichment of smart card data with information from survey data. Travel survey data do contain all this information, which allows for the estimation of relations between the information to be added and available trip characteristics and land-use characteristics. The relations are presented by three specific (logit) enrichment models for (1) the origin zone allocation (2) the destination zone allocation and (3) the travel purpose inference. These models are projected onto smart card data for the construction of purpose-specific OD matrices. We developed two approaches, based on different representations of travel: a trip-based and a tour-based approach. The resulting OD matrices are compared with the observed matrix of the travel survey, at different levels of spatial resolution, in order to assess the accuracy increase at each level. The results indicate that access and egress legs have a substantial effect on the representation of travel demand in spatial resolutions commonly used in regional and urban transport models. The tour-based approach results in the most accurate construction of purpose-specific OD matrices. This report presents recommendations for model developers regarding further development of this method of data enrichment. Moreover, the report includes recommendations for transport planners and public transport operators regarding the applications of the results.

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