Volume 41 Issue 1
Feb.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(1): 161-168
YAN Xiu, LU Yu, XIE Qian, LIU Qiang, XIE Xiaomin. A Method for Evaluating Operation Efficiency of Bus Lines Based on a Bootstrap-DEA-Gini Model[J]. Journal of Transport Information and Safety, 2023, 41(1): 161-168. doi: 10.3963/j.jssn.1674-4861.2023.01.017
Citation: YAN Xiu, LU Yu, XIE Qian, LIU Qiang, XIE Xiaomin. A Method for Evaluating Operation Efficiency of Bus Lines Based on a Bootstrap-DEA-Gini Model[J]. Journal of Transport Information and Safety, 2023, 41(1): 161-168. doi: 10.3963/j.jssn.1674-4861.2023.01.017
shu

A Method for Evaluating Operation Efficiency of Bus Lines Based on a Bootstrap-DEA-Gini Model

doi: 10.3963/j.jssn.1674-4861.2023.01.017
  • 1.

    School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, Guangdong, China

  • 2.

    Guangdong Provincial Marine Engineering Construction and Water Emergency Rescue Engineering Technology Center, Guangzhou 510006, China

  • 3.

    SYSU-GAC R&D Center Joint Laboratory of Intelligent Transportation and Artificial Intelligence, Guangzhou 510006, China

  • 4.

    Guangdong Marshell Electric Technology Co., Ltd, Zhaoqing 523268, Guangdong, China

  • Received Date: 2022-10-18
    Available Online: 2023-05-13
    Abstract
  • Scientific evaluation of operation efficiency of urban bus lines is the premise of optimizing bus network and improving its level of service. An evaluation method is developed with the following indicators as the input: number of lines, line length, average station distance, non-linear coefficient, departure interval, operating speed, and ratio of the average fare to the average income. The outputs of the evaluation include the ratio of the daily average passenger flow to the number of residents served and the revenue per kilometer. Then, a data envelopment analysis (DEA) model is developed to evaluate the operation efficiency of bus lines. In order to correct the bias from the random factors in the case study with a small sample, the Bootstrap sampling method is used to expand the number of samples to in order to reproduce the overall distribution. In order to reduce the risk associated with the high dimensionality of the input indicators, Gini impurity is introduced to determine the weights of each subset of indicators and a combined value is calculated, in order to jointly improve the ability of the DEA model to differentiate the operation efficiency of different bus lines. At the same time, as it is difficult to determine the impact of different indicators by DEA, a partial least squares regression method is used to quantify the impact of each indicator on the operation efficiency of bus lines. Based on this, a Bootstrap-DEA-Gini model is proposed to evaluate the operation efficiency of 457 bus lines in the City of Foshan, Guangdong Province, China. Study results show that the average station distance, line length, and non-linear coefficients have a significant impact on the operation efficiency of bus lines, of which the average station distance has the highest impact ratio of up to 0.98. It is also found that the main reasons for the low operation efficiency of bus lines in the City of Foshan are long average departure interval, large non-linear coefficient, and small number of passenger seats. The operation efficiencies calculated based on the proposed Bootstrap-DEA-Gini model are lower than the traditional DEA model, and bus lines with similar operating efficiency can be effectively distinguished, which indicates that proposed method is effective for evaluating the efficiency of real-world bus operation.

     

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