Spatial-temporal identification of commuters using trip chain data from non-motorized mode incentive program and public transportation

Linchang Shi, Jiayu Yang, Jaeyoung Jay Lee, Jun Bai, Ingon Ryu and Keechoo Choi

Journal of Transport Geography, 2024, vol. 117, issue C

Abstract: Distinguishing commuters from non-commuters is important for transportation planning and traffic demand management. A framework to identify commuters using public transit is proposed based on a spatial-temporal clustering algorithm. The framework extracts commute trips by mining spatial-temporal travel patterns depending on whether the travelers' trip chains are complete. The commuting features in terms of travel frequency, time regularity, and spatial regularity are utilized to compare the difference in travel behavior between commuters and non-commuters. The framework is applied to trip data collected from an incentive program implemented by the Metropolitan Transport Commission of Korea. The data records spatial and temporal information on travelers' boarding and alighting points, as well as actual origins and destinations. One trip record constitutes a complete trip chain for travelers who joined the incentive program. The results show that commuters exhibit a higher travel frequency and more regular spatial-temporal travel patterns than non-commuters. It is found that not all commuters travel during morning and evening peaks; some commuters leave work after evening peak. The proposed identification framework based on the spatial-temporal clustering approach is capable of exploiting the inherent spatial-temporal travel patterns of travelers to achieve a reliable identification of commuters.

Keywords: Public transportation; Commuter identification; Commuting behavior; Spatial-temporal travel patterns; Complete trip chain data (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:117:y:2024:i:c:s0966692324000772

DOI: 10.1016/j.jtrangeo.2024.103868

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