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.
Research on classification and influencing factors of metro commuting patterns by combining smart card data and household travel survey data
IET Intelligent Transport Systems ; 13 , 10 ; 1525-1532
2019-10-01
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
long‐distance pattern , traffic congestion , metro lines , long‐term spatiotemporal travel characteristics , mixed logit regression model , road traffic , commuting pattern groups , classic pattern , traffic engineering computing , corresponding metro commuters , SCD , pattern clustering , smart cards , transfer distance significantly impact commuting patterns , traditional household travel survey data , transportation , smart card data , spatiotemporal characteristics , regular metro commuters , off‐peak pattern , influencing factors , regression analysis , pattern classification , one‐day HTSD , useful insights , clustering method , improving urban traffic management , Gaussian mixture model , commuting regularity rules , cluster commuting patterns , Gaussian processes , public transit users , metro commuting patterns
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