基于Bootstrap-DEA-Gini模型的公交线路运行效率评价方法

严修; 鲁誉; 谢谦; 刘强; 解孝民

doi:10.3963/j.jssn.1674-4861.2023.01.017

基于Bootstrap-DEA-Gini模型的公交线路运行效率评价方法

doi: 10.3963/j.jssn.1674-4861.2023.01.017

严修^1,,
鲁誉^{1, 2},
谢谦¹,
刘强^{1, 3, ,},
解孝民⁴

1.
中山大学智能工程学院广东深圳 518107
2.
广东省海洋工程施工与水上应急救援工程技术中心广州 510006
3.
中山大学-广汽研究院智慧交通与人工智能联合实验室广州 510006
4.
广东玛西尔电动科技有限公司广东肇庆 523268

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

详细信息

作者简介:
严修（1998—），硕士研究生. 研究方向：城市公交管理与规划. E-mail：yanx29@mail2.sysu.edu.cn

通讯作者:
刘强（1981—），博士，教授. 研究方向：交通安全. E-mail：liuq32@mail.sysu.edu.cn

中图分类号: U491.1+7
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出版历程
- 收稿日期: 2022-10-18
- 网络出版日期: 2023-05-13

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

YAN Xiu^1
,,
LU Yu^{1, 2},
XIE Qian¹,
LIU Qiang^{1, 3
, ,},
XIE Xiaomin⁴

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

摘要

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。
- 城市交通 /
- 公交线路 /
- 运行效率 /
- 数据包络分析 /
- Bootstrap /
- Gini
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.
- public transit /
- bus line /
- operation efficiency /
- DEA /
- Bootstrap /
- Gini

HTML全文

图 1 评价指标体系

Figure 1. Evaluation index system

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图 2 Bootstrap-DEA-Gini模型

Figure 2. Bootstrap-DEA-Gini model

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图 3 各投入指标对效率影响程度

Figure 3. Degree of impact of each input indicator on efficiency

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图 4 2种方法评价结果

Figure 4. Results of the evaluation of the two methods

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图 5 各低效线路差异明显指标

Figure 5. Obvious different indicators of each inefficient line

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表 1 部分线路各项指标值

Table 1. Values of various indicators for some lines

指标	线路名称
指标	100	101	102	103	05	106
A₁₁线路配客座总数/个	18	30	748	324	33	402
A₁₂线路长度/km	18.8	13.85	13.85	19.56	16.75	15.4
A₁₃平均站距/km	0.783 3	0.494 6	0.446 8	0.652 0	0.598 2	0.570 4
A₁₄非直线系数	1.60	2.20	1.31	1.68	2.46	2.16
A₂₁票价收入比	0.382 0	0.390 2	0.365 3	0.390 7	0.393 3	0.359 8
A₂₂运行速度（/ km/h）	16.35	11.54	11.38	14.31	13.58	17.9
A₂₃平均发车间隔/min	13.25	9.50	7.75	13.75	9.50	8.50
A₃₁每公里收益/元	2.071	3.138	4.155	1.500	3.409	2.326
A₃₂客流服务居民比	0.045 6	0.100 6	0.182 8	0.039 3	0.091 9	0.079 3

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表 2 2种模型评价结果对比

Table 2. Comparison of evaluation results between the two methods

方法	最大值	最小值	平均值	标准差	离散系数
DEA	1	0.026 95	0.376 6	0.183 4	0.487 1
Bootstrap-DEA-Gini	0.990 9	0.020 13	0.305 9	0.154 5	0.505 1

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表 3 企业评价结果对比

Table 3. Comparison of enterprise evaluation results

低效线路	效率值		排名
低效线路	DEA	Bootstrap-DEA-Gini	DEA	Bootstrap-DEA-Gini
江03B	0.113 4	0.081 52	15	9
398B	0.150 1	0.089 54	36	18
965	0.164 4	0.082 14	41	17
里06B	0.195 1	0.093 90	58	23
樵09（支线）	0.206 6	0.096 94	72	30
938	0.234 4	0.108 0	118	35
平均	0.177 3	0.092 01	56.67	22

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表 4 低效线路各指标平均值与所有线路平均值对比

Table 4. Comparison of the average of each index of inefficient lines and all lines

各项指标	低效线路	所有线路	差值	差率/%
线路配客座总数/个	139.1	228.9	-89.72	-39.20
线路长度/km	16.89	19.61	-2.729 -	13.91
平均站距/km	0.863 8	0.690 2	0.17 36 2	5.15
非直线系数	2.691	1.981	0.710 1 3	5.85
票价收入	0.420 8	0.446 5	-0.025 6 -	5.515
运行速度（/km/h）	20.90	19.10	2.645 1	3.85
平均发车间隔/min	43.22	23.28	19.95 8	5.73
每公里收益/元	0.732 0	2.141	-1.475 -	68.88
客流服务居民比	0.005 787	0.031 33	-0.025 54-	81.53

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