Abstract
The increasing availability of public transit smart card data has enabled several studies to focus on identifying passengers with similar spatial and/or temporal trip characteristics. However, this paper goes one step further by investigating the relationship between passengers’ spatial and temporal characteristics. For the first time, this paper investigates the correlation of the spatial similarity with the temporal similarity between public transit passengers by developing spatial similarity and temporal similarity measures for the public transit network with a novel passenger-based perspective. The perspective considers the passengers as agents who can make multiple trips in the network. The spatial similarity measure takes into account direction as well as the distance between the trips of the passengers. The temporal similarity measure considers both the boarding and alighting time in a continuous linear space. The spatial-temporal similarity correlation between passengers is analysed using histograms, Pearson correlation coefficients, and hexagonal binning. Also, relations between the spatial and temporal similarity values with the trip time and length are examined. The proposed methodology is implemented for four-day smart card data including 80,000 passengers in Brisbane, Australia. The results show a nonlinear spatial-temporal similarity correlation among the passengers.
Highlights
Analysing passengers’ movements in a public transit network is important in understanding passengers’ travel behaviours and designing more customised public transit services
The spatial and temporal similarity measures are developed for the public transit network
The used smart card dataset is from TransLink, the public transport authority of South East Queensland (SEQ), Australia
Summary
Analysing passengers’ movements in a public transit network is important in understanding passengers’ travel behaviours and designing more customised public transit services. By identifying passengers with similar spatial and/or temporal trip patterns and understanding their characteristics, transit operators could design transit services that better meet different needs of different passenger groups and develop strategies to influence travellers to use the existing transit network more efficiently. With the availability of transit smart card data that provide information on boarding and alighting locations and times for each passenger trip, it is possible to analyse spatial and temporal movement patterns for each passenger and compare them across passengers, thereby allowing a deeper understanding of individual passengers and their relationships. Transit authorities have developed automated fare collection (AFC) systems around the world since two decades ago These systems aim to gather fares and they turn valuable datasets out of trips as a by-product. Smart card datasets provide opportunities to explore travel behaviours of public transit passengers in large and detailed scales
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