access icon free Research on classification and influencing factors of metro commuting patterns by combining smart card data and household travel survey data

© The Institution of Engineering and Technology
Received 23/10/2018, Accepted 19/06/2019, Revised 11/04/2019, Published 21/06/2019

Smart card data (SCD) provide a new perspective for analysing the long-term spatiotemporal travel characteristics of public transit users. Understanding the commuting patterns provides useful insights for urban traffic management. This study attempts to identify and cluster commuting patterns and explore the influencing factors by combining SCD and traditional household travel survey data (HTSD) in Nanjing, China. First, the authors generate the commuting regularity rules using one-day HTSD. Then, the regular metro commuters are identified in four-week (20-weekday) SCD. Using the clustering method of the Gaussian mixture model, they classify metro commuters in SCD into three commuting pattern groups, namely, classic pattern, off-peak pattern, and long-distance pattern, based on their spatiotemporal characteristics. Next, they identify the corresponding metro commuters of these three groups in HTSD and apply a mixed logit regression model to determine the factors influencing metro commuting patterns from multiple dimensions. The results show that some socioeconomic attributes (e.g. gender, age, annual income, education, and occupation) as well as bus station density, metro lines, transfer mode, and transfer distance significantly impact commuting patterns. The findings can provide valuable information for planners and managers to put forward relevant transport guiding measures for alleviating traffic congestion and improving urban traffic management.

Inspec keywords: traffic engineering computing; Gaussian processes; smart cards; transportation; pattern clustering; regression analysis; pattern classification; road traffic

Other keywords: metro commuting patterns; corresponding metro commuters; traffic congestion; public transit users; transfer distance significantly impact commuting patterns; classic pattern; useful insights; regular metro commuters; off-peak pattern; commuting regularity rules; one-day HTSD; metro lines; long-term spatiotemporal travel characteristics; cluster commuting patterns; spatiotemporal characteristics; influencing factors; SCD; traditional household travel survey data; clustering method; smart card data; improving urban traffic management; mixed logit regression model; long-distance pattern; commuting pattern groups; Gaussian mixture model

Subjects: Other topics in statistics; Traffic engineering computing; Data handling techniques

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