基于多模式运营网络平衡的出租车票价结构优化模型

doi:10.13229/j.cnki.jdxbgxb.20211030

摘要/Abstract

摘要：

为优化出租车的出行费用，以巡游车模式和网约车模式票价结构为研究对象，通过对巡游行为的分析确定了不同模式下出租车巡游方向选择和巡游匹配结果的概率表达，以供需平衡和服务时间平衡为基础，建立了多模式运营网络平衡条件，使用调整阈值将平衡条件转化为不等式条件并作为约束，构建了不定需求下多目标票价结构优化模型；采用了非支配排序遗传算法（NSGA-Ⅱ），将平衡条件在内的约束条件转化为外罚函数的同时设置平衡调整阈值随迭代状态减小的自适应机制。通过对A市进行案例分析发现，相对于现有票价结构，优化后巡游车模式和网约车模式单均票价分别上涨了18.15%和9.75%，相应运输量均下降了约3%~4%，空驶率均下降了约6%~7%，同时乘客平均等待时间分别减少了4.73%和6.41%，构建的出租车司机劳动价值综合指标分别提升10.86%和4.15%，模型的有效性得到验证。与此同时，案例分析结果也表明，在当前票价结构中，巡游车基本费率（包括起步价、起步里程和里程价）和返空费部分存在较大的优化空间，网约车票价结构整体优化空间相对较小。

关键词: 交通运输经济, 出租车票价结构, 多模式, 运营网络, 网络平衡, 巡游

Abstract:

In order to optimize the taxi travel fare， the fare structure of cruise taxi mode and online car-hailing mode was taken as the research object. Based on the analysis of the cruising behavior， the probability statement of taxi cruising direction selection and cruising matching results under different modes was determined， and the equilibrium conditions of multi-mode taxi service network was established based on the balance of supply and demand and the balance of service time. The control threshold was used to transform the equilibrium condition into inequality condition as constraint， and a multi-objective fare structure optimization model under uncertain demand was constructed. The non-dominated sorting genetic algorithm （NSGA-II） was introduced to transform the constraint conditions including the equilibrium conditions into the external penalty function and the adaptive mechanism of the balance control threshold decreasing with the iteration state was set. Through the case analysis of A city， after the optimization of the existing fare structure， per-unit fares for cruise mode and online car-hailing mode increase by 18.15% and 9.75% respectively， the corresponding traffic volume decreased by about 3%~4%. The empty rate decreased by about 6%~7%， the average of passenger waiting time decreased by 4.73% and 6.41 %， and the comprehensive index of taxi drivers' labor value increased by 10.86% and 4.15%， respectively. The validity of the model is verified. The analysis found that in the current fare structure， there is a large optimization space for the basic rate of cruise taxi （including base fare， starting mileage and distance fare ） and the no-occupancy surcharge， and the overall optimization space of online ride-hailing fare structure is relatively small.

Key words: transportation economy, taxi fare structure, multi-mode, operation network, network equalization, cruise

中图分类号:

F570.5

贾洪飞,叶飞,杨丽丽. 基于多模式运营网络平衡的出租车票价结构优化模型[J]. 吉林大学学报(工学版), 2023, 53(7): 1970-1980.

Hong-fei JIA,Fei YE,Li-li YANG. Optimization model of taxi fare structure under multi⁃mode taxi service network equilibrium[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 1970-1980.

图/表 12

图1

图2

图3

图4

图5

图6

图7

表3

票价结构对比表"

运营模式	数据来源	$p s u$	$p l u$	$d l u$	$p b u$	$d b u$	$p t u$
巡游车	现状	10	2	3	1	8	0.4
巡游车	最优解	12	2.53	4	1.89	14	0.35
网约车	现状	11	2.71	3	1.82	15	0.9
网约车	最优解	12	2.56	3	1.85	14	0.93

表3

表4

多模式网络运营参数表"

指标	现状参数		最优解
指标	巡游车	网约车	巡游车	网约车
单均票价/元	14.6	17.25	16.82	18.46
运输量/单	5404	7957	5217	7654
空驶率/%	63.12	57.15	58.85	52.16
有效里程占比/%	36.88	42.85	41.15	47.84
乘客平均等待时间/ $m i n$	4.44	4.06	4.23	3.87
劳动价值综合指标	19.61	21.74	21.23	22.11

表4

图8

图9

表5

优化前后出租车空驶时间分布变化表(%)"

空驶时间区间 / $m i n$	巡游车			网约车
空驶时间区间 / $m i n$	现状	最优解	变化率	现状	最优解	变化率
0~5	26.15	29.32	3.17	41.38	45.39	4.01
5~10	24.17	22.35	-1.82	25.49	22.63	-2.86
10~15	21.71	18.97	-2.74	13.61	12.40	-1.21
15~20	17.88	17.67	-0.21	11.25	11.09	-0.16
20~25	7.69	8.39	0.70	5.96	6.42	0.46
25~30	2.17	2.37	0.20	1.96	1.61	-0.35
30~35	0.18	0.85	0.67	0.27	0.36	0.09
35~40	0.05	0.08	0.03	0.08	0.11	0.03

表5

参考文献 13

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