| 基于Bootstrap-DEA-Gini模型的公交线路运行效率评价方法 |
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| 引用本文： | 严修,鲁誉,谢谦,刘强,解孝民.基于Bootstrap-DEA-Gini模型的公交线路运行效率评价方法[J].交通信息与安全,2023,41(1):161-168. |
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| 作者姓名： | 严修 鲁誉 谢谦 刘强 解孝民 |
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| 作者单位： | 1.中山大学智能工程学院 广东 深圳 518107 |
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| 基金项目： | 国家自然科学基金项目51675540广东省基础与应用基础研究基金项目2022A1515010692 |
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| 摘 要： | 科学评价城市常规公交线路运行效率是优化公交线网、提升服务水平的前提。构建以线路配车数、线路长度、平均站距、非直线系数、发车间隔、运行速度和票价收入比为投入指标，客流服务居民数量比和每公里收益为产出指标的评价指标体系，采用数据包络分析（data envelopment analysis，DEA）模型对公交线路运行效率进行评价。为了修正小样本情况下随机因素带来的偏差，采用Bootstrap抽样法扩大样本数量使之接近总体分布；为了降低效率受指标维数影响的风险性，引入基尼不纯度（Gini impurity）系数赋予各指标子集权重并求出综合效率值，以此联合提升传统DEA模型的区分能力。同时，针对DEA模型难以确定评价指标的影响程度的问题，采用偏最小二乘回归方法量化各评价指标对公交线路运行效率的影响程度。基于构建的Bootstrap-DEA-Gini模型来联合改进DEA模型，并以广东省佛山市457条公交线路为例对其运行效率进行评价。结果表明:平均站距、线路长度和非直线系数对公交线路的运行效率具有显著影响，其中平均站距影响最大，影响程度高达0.98。平均发车间隔偏长、非直线系数偏大和线路配客座总数偏少是造成佛山市部分线路运行效率低的主要原因。提出的Bootstrap-DEA-Gini模型得到的效率值比传统DEA模型低，且能有效区分运行效率相近的公交线路，与实际运营情况更相符。
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| 关 键 词： | 城市交通 公交线路 运行效率 数据包络分析 Bootstrap Gini |
| 收稿时间： | 2022-10-18 |
A Method for Evaluating Operation Efficiency of Bus Lines Based on a Bootstrap-DEA-Gini Model |
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| Affiliation: | 1.School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, Guangdong, China2.Guangdong Provincial Marine Engineering Construction and Water Emergency Rescue Engineering Technology Center, Guangzhou 510006, China3.SYSU-GAC R&D Center Joint Laboratory of Intelligent Transportation and Artificial Intelligence, Guangzhou 510006, China4.Guangdong Marshell Electric Technology Co., Ltd, Zhaoqing 523268, Guangdong, China |
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| 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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