PROGRESS IN GEOGRAPHY ›› 2021, Vol. 40 ›› Issue (4): 671-680.doi: 10.18306/dlkxjz.2021.04.011

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Impact of the built environment on residents’ car commuting based on trip chain

ZHANG Xue1,2(), ZHOU Suhong1,2,*(), CHEN Fei3   

  1. 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    2. Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
    3. China Academy of Urban Planning & Design, Shenzhen 518040, Guangdong, China
  • Received:2020-06-17 Revised:2020-10-19 Online:2021-04-28 Published:2021-06-28
  • Contact: ZHOU Suhong;
  • Supported by:
    National Natural Science Foundation of China(71961137003);National Natural Science Foundation of China(41871148);Key Research and Development Program of Guangdong Province(2020B0202010002)


The impact of urban built environment on residents' travel behavior, especially car commuting, is a basic topic of traffic demand forecasting, which has received considerable research attention. Existing studies mainly focused on single trips and paid less attention to the collaborative decision making under the influence of trip chains. By using the logistic model and detailed activity diary data collected in Guangzhou City in 2017, this study investigated the relationship between urban built environment and car commuting on weekdays from the trip chain perspective. Empirical results indicate that there is an interactive relationship between non-commuting trip in the trip chain and the built environment of the workplace and residence, which synergistically affects residents' commuting modes. On the one hand, the non-commuting trip time (before or after the commuter's trip) make difference in the relationship between the built environment and commuting mode choice. On the other hand, the mutual influence of several built environment factors—residential area, work location, and non-commuting activity—determines the commuting behavior. Therefore, the extension of the results may provide new thoughts for predicting individual traffic behavior and traffic demand based on individual models.

Key words: built environment, car commuting trip, trip chain, interaction