Volume 41 Issue 3
Jun.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(3): 147-156
HU Song, YANG Bei, WENG Jiancheng, ZHOU Wei. A Cause Analysis of Residents' Dependence on Public Transportation Based on Association Rules[J]. Journal of Transport Information and Safety, 2023, 41(3): 147-156. doi: 10.3963/j.jssn.1674-4861.2023.03.016
Citation: HU Song, YANG Bei, WENG Jiancheng, ZHOU Wei. A Cause Analysis of Residents' Dependence on Public Transportation Based on Association Rules[J]. Journal of Transport Information and Safety, 2023, 41(3): 147-156. doi: 10.3963/j.jssn.1674-4861.2023.03.016
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A Cause Analysis of Residents' Dependence on Public Transportation Based on Association Rules

doi: 10.3963/j.jssn.1674-4861.2023.03.016
  • 1.

    Research Institute of Highway Ministry of Transport, Beijing 100088, China

  • 2.

    Zhonglu Gongke (Beijing) Consulting Co., Ltd., Beijing 100088, China

  • 3.

    Faculty of Urban Construction, Beijing University of Technology, Beijing 100124, China

  • 4.

    Ministry of Transport of the People's Republic of China, Beijing 100736, China

  • Received Date: 2022-11-29
    Available Online: 2023-09-16
    Abstract
  • Identifying the magnitude of travelers' dependence on public transit (PT) and analyzing the differences in its underlying causes can contribute to targeted improvements in the level of PT services from the perspectives of planning, design and policy making. In this study, an online revealed preference (RP) survey for residents' travel is designed and carried out. The data quality is examined, based on which the correlation matching technique is adopted to extract individual PT-trip chains by integrating travel survey data and PT transaction data. Measurement indicators and key causation indicators of PT dependence are proposed, and an AGNES-Apriori model is developed to classify travelers' PT dependence and strong association rules for different groups. Further, a two-stage framework and a set of travel incentive strategies to enhance travelers' PT dependence levels are proposed. The results show that ①residents'PT dependence can be classified into four categories (low, relatively low, relatively high, and high dependences), and significant differences are found among the different categories regarding the strong association rules; ②the number of indicators contained in association rules is negatively correlated with three parameters, and the probability of strong association rules with high dependence level is 2.1 times higher than that with low dependence level; ③objective factors such as total distance from home and destination to the PT stations, income, and car availability are identified as key indicators affecting residents' PT dependence, and the low freedom for traveling by PT is an important reason for the reduction of travelers' dependence on PT; ④the low values of the objective factors usually cause the travelers to form a relatively high PT dependence; ⑤the low availability of cars mainly related to the strong association rules corresponding to the low and high PT dependence groups, while the high dependence group may show the tendency of reducing PT dependence with increased car availability.

     

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