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<p>This study was conducted to investigate the travel behavior of residents in a medium-sized Chinese city, with the goal of exploring travel characteristics and identifying the key factors influencing urban travel mode choices. While traditional discrete choice models are known for their strong interpretability, their predictive accuracy remains limited. In contrast, machine learning models are recognized for offering higher predictive accuracy but are frequently criticized for their lack of interpretability. To address this issue, a CART-Apriori predictive model was constructed through the integration of the Classification and Regression Tree (CART) model and the Apriori algorithm. Accuracy, the Kappa coefficient, and the Macro-F1 score were utilized as performance metrics for the quantitative comparison of the CART-Apriori model with various alternative models. Additionally, the RuleFit model was employed to extract nonlinear relationships generated by the CART-Apriori model. These relationships were subsequently converted into rule-based features and incorporated into a multinomial Logit linear model to identify the most influential travel rules for each travel mode. The results demonstrated that an average overall prediction accuracy of 82.77% was attained by the CART-Apriori model. Using the Apriori association rule algorithm, the most critical factors influencing urban residents’ travel mode choices were ranked in descending order of importance as travel distance, travel purpose, car ownership, and the number of transfers. When walking was chosen as the travel mode, travel distance played a dominant role. When shared electric vehicles or private cars were chosen, travelers were primarily motivated by the intention to reach their destinations directly. Shared bicycles were predominantly chosen by commuters traveling 1–3&#xa0;km. For bus users, travel distance and the number of transfers were the most influential factors. Ride-hailing users were primarily commuters traveling 1–3&#xa0;km who required multiple transfers on public transportation.</p>

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Study on urban residents’ travel mode choice based on the CART-Apriori method

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月27日

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  • Hui Song,
  • Xinxin Wang,
  • Wen Tian,
  • Lina Shi,
  • Shiqi Li

摘要

This study was conducted to investigate the travel behavior of residents in a medium-sized Chinese city, with the goal of exploring travel characteristics and identifying the key factors influencing urban travel mode choices. While traditional discrete choice models are known for their strong interpretability, their predictive accuracy remains limited. In contrast, machine learning models are recognized for offering higher predictive accuracy but are frequently criticized for their lack of interpretability. To address this issue, a CART-Apriori predictive model was constructed through the integration of the Classification and Regression Tree (CART) model and the Apriori algorithm. Accuracy, the Kappa coefficient, and the Macro-F1 score were utilized as performance metrics for the quantitative comparison of the CART-Apriori model with various alternative models. Additionally, the RuleFit model was employed to extract nonlinear relationships generated by the CART-Apriori model. These relationships were subsequently converted into rule-based features and incorporated into a multinomial Logit linear model to identify the most influential travel rules for each travel mode. The results demonstrated that an average overall prediction accuracy of 82.77% was attained by the CART-Apriori model. Using the Apriori association rule algorithm, the most critical factors influencing urban residents’ travel mode choices were ranked in descending order of importance as travel distance, travel purpose, car ownership, and the number of transfers. When walking was chosen as the travel mode, travel distance played a dominant role. When shared electric vehicles or private cars were chosen, travelers were primarily motivated by the intention to reach their destinations directly. Shared bicycles were predominantly chosen by commuters traveling 1–3 km. For bus users, travel distance and the number of transfers were the most influential factors. Ride-hailing users were primarily commuters traveling 1–3 km who required multiple transfers on public transportation.