Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

Hamed Faroqi · Mahmoud Mesbah · Jiwon Kim

15 citations0/0 files storedMathematical Problems in Engineering

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Transportation Planning and Optimization Data Management and Algorithms Human Mobility and Location-Based Analysis

Abstract

Smart card datasets in the public transit network provide opportunities to analyse the behaviour of passengers as individuals or as groups. Studying passenger behaviour in both spatial and temporal space is important because it helps to find the pattern of mobility in the network. Also, clustering passengers based on their trips regarding both spatial and temporal similarity measures can improve group‐based transit services such as Demand‐Responsive Transit (DRT). Clustering passengers based on their trips can be carried out by different methods, which are investigated in this paper. This paper sheds light on differences between sequential and combined spatial and temporal clustering alternatives in the public transit network. Firstly, the spatial and temporal similarity measures between passengers are defined. Secondly, the passengers are clustered using a hierarchical agglomerative algorithm by three different methods including sequential two‐step spatial‐temporal (S‐T), sequential two‐step temporal‐spatial (T‐S), and combined one‐step spatiotemporal (ST) clustering. Thirdly, the characteristics of the resultant clusters are described and compared using maps, numerical and statistical values, cross correlation techniques, and temporal density plots. Furthermore, some passengers are selected to show how differently the three methods put the passengers in groups. Four days of smart card data comprising 80,000 passengers in Brisbane, Australia, are selected to compare these methods. The analyses show that while the sequential methods (S‐T and T‐S) discover more diverse spatial and temporal patterns in the network, the ST method entails more robust groups (higher spatial and temporal similarity values inside the groups).

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References

2011Smart card data use in public transit: A literature review 2015Planning, operation, and control of bus transport systems: A literature review 2013Mining smart card data for transit riders’ travel patterns 2014Temporal variability in transit-based accessibility to supermarkets 2016Public Transit Planning and Operation 2015Evaluating Public Transit Benefits and Costs 2006MINING PUBLIC TRANSPORT USER BEHAVIOUR FROM SMART CARD DATA 2017Analyzing year-to-year changes in public transport passenger behaviour using smart card data

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