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浙江大学学报(工学版)
浙江大学学报(工学版)  2025, Vol. 59 Issue (7): 1373-1384    DOI: 10.3785/j.issn.1008-973X.2025.07.005
土木与交通工程     
城市网约车与巡游出租车动态博弈及碳排放效应研究
矫金池1(),孙健2,*(),倪训友3
1. 长安大学 运输工程学院，陕西 西安 710064
2. 长安大学 未来交通学院，陕西 西安 710064
3. 华东交通大学 交通运输工程学院，江西 南昌 330013
Dynamic game and carbon emission effects between urban ride-sourcing and cruise taxis
Jinchi JIAO1(),Jian SUN2,*(),Xunyou NI3
1. School of Transportation Engineering, Chang’an University, Xi’an 710064, China
2. School of Future Transportation, Chang’an University, Xi’an 710064, China
3. School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China
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摘要：

为了研究城市网约车和巡游出租车的动态竞争关系及其对交通碳排放的影响，引入赫芬达尔-赫希曼指数（HHI）度量城市出租车的竞争程度. 构建包括政府和企业在内的四方博弈模型，考虑2种决策顺序，提出16种博弈场景，运用斯坦克尔伯格模型计算均衡点，筛选得到8种稳定博弈情景. 基于博弈结果，将COPERT模型与改进的Michaelis-Menten（T-M-M）方程整合，计算碳排放量. 以西安市为例，构建车辆数量（含网约车和巡游出租车）与行驶里程，车辆保有量与碳排放间的关系，计算得出不同博弈策略下的理论最优碳排放量. 结果显示，西安市网约车与巡游出租车最优比例控制在29∶35到1∶1能够维持市场平衡和低碳环保. 政府可通过限行、限号政策使巡游出租车数量略多于网约车，出台鼓励政策推动巡游出租车电气化进程.
关键词： 城市交通;  共享出行;  出租汽车;  竞争;  博弈理论;  碳排放    
Abstract:

To study the dynamic competitive relationship between ride-sourcing and cruise taxis in urban environments and their impact on traffic carbon emissions, the Herfindahl-Hirschman index (HHI) was introduced to quantify the competitiveness of urban taxis. A four-player game model encompassing the government and enterprises was constructed. Considering two distinct decision-making sequences, 16 potential game scenarios were proposed. Equilibrium points were computed using the Stackelberg model, leading to the identification of 8 stable game scenarios. Based on the game outcomes, carbon emissions were calculated by integrating the COPERT model and a modified Michaelis-Menten (T-M-M) equation. Taking Xi’an as a case study, relationships between vehicle numbers (including both ride-sourcing vehicles and cruise taxis) and vehicle kilometers traveled, as well as between vehicle fleet size and carbon emissions, were established. The theoretical optimal carbon emissions under the proposed game scenarios were calculated. Results show that maintaining an optimal ratio of ride-sourcing vehicles and cruise taxis within the range of 29∶35 to 1∶1 in Xi’an can effectively balance market stability and low-carbon objectives. The government can implement traffic restriction policies (e.g., driving bans based on license plates) to maintain a slightly larger fleet of cruise taxis relative to ride-sourcing vehicles, alongside introducing incentive policies to accelerate the electrification of the cruise taxi fleet.
Key words: urban transportation    shared mobility    taxis    competition    game theory    carbon emissions
收稿日期: 2024-05-19 出版日期: 2025-07-25
CLC:  U 491.1+2  
基金资助: 国家自然科学基金资助项目（52172319，71971138，72161012）.
通讯作者: 孙健     E-mail: 2022234048@chd.edu.cn;jiansun@chd.edu.cn
作者简介: 矫金池（2000—），男，硕士生，从事城市交通竞合关系研究. orcid.org/0009-0005-4919-4111. E-mail：2022234048@chd.edu.cn
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引用本文:

矫金池,孙健,倪训友. 城市网约车与巡游出租车动态博弈及碳排放效应研究[J]. 浙江大学学报(工学版), 2025, 59(7): 1373-1384.

Jinchi JIAO,Jian SUN,Xunyou NI. Dynamic game and carbon emission effects between urban ride-sourcing and cruise taxis. Journal of ZheJiang University (Engineering Science), 2025, 59(7): 1373-1384.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.07.005    或    https://www.zjujournals.com/eng/CN/Y2025/V59/I7/1373

图 1  四方博弈模型的逻辑关系
博弈场景$ {P}_{\mathrm{R}} $$ {P}_{\mathrm{T}} $
场景1. 严-监-合-合$ {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景2. 严-不-合-合$ {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景3. 宽-监-合-合$ {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $?$ {C}_{no} $
场景4. 宽-不-合-合$ {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景5. 严-监-合-违$ {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $+$ \Delta {R}_{n} $?$ {F}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景6. 严-不-合-违$ {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $+$ \Delta {R}_{n} $?$ {F}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景7. 宽-监-合-违$ {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $+$ \Delta {R}_{n} $?$ \dfrac{1}{2}F_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景8. 宽-不-合-违$ {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $+$ \Delta {R}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景9. 严-监-违-合$ {R}_{z} $+?$ {R}_{z} $?$ {F}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景10. 严-不-违-合$ {R}_{z} $+$ \Delta {R}_{z} $?$ {F}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景11. 宽-监-违-合$ {R}_{z} $+$ \Delta {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $?$ \dfrac{1}{2}F_{z} $$ {R}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景12. 宽-不-违-合$ {R}_{z} $+$ \Delta {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景13. 严-监-违-违$ {R}_{z} $+$ \Delta {R}_{z} $?$ {F}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $+$ \Delta {R}_{n} $?$ {F}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景14. 严-不-违-违$ {R}_{z} $+$ \Delta {R}_{z} $?$ {F}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n}+\Delta {R}_{n} $?$ {F}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
场景15. 宽-监-违-违$ {R}_{z} $+$ \Delta {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $?$ \dfrac{1}{2}F_{z} $$ {R}_{n} $+$ \Delta {R}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $?$ \dfrac{1}{2}F_{n} $
场景16. 宽-不-违-违$ {R}_{z} $+$ \Delta {R}_{z} $?$ {C}_{\mathrm{z}\mathrm{o}} $$ {R}_{n} $+$ \Delta {R}_{n} $?$ {C}_{\mathrm{n}\mathrm{o}} $
表 1  不同博弈场景网约车和巡游出租车净收益
图 2  巡游出租车、网约车的日订单量
图 3  巡游出租车、网约车小时订单率分布
图 4  西安市主城区栅格划分情况
FIDRTQHHI
20016136 6407 2530.845
20021 0893 7624 8510.652
20031 6696 1037 7720.663
20045164499650.502
200544821260.545
200687331200.601
2007161381540.814
20084041.000
表 2  西安市主城区内赫芬达尔-赫希曼指数统计（摘录）
竞争状态编号状态描述HHI变化范围网约车数量变化范围巡游出租车数量变化范围
1.0完全竞争0.500≤HHI≤0.501[16 715, 15 285][16 715, 15 285]
2.1网约车轻微优势0.501＜HHIrs≤0.520(16 715, 19 200][12 800, 15 285)
2.2网约车优势竞争0.520＜HHIrs≤0.625(19 200, 24 000][8 000, 12 800)
2.3网约车基本垄断HHIrs＞0.625(24 000, 32 000][0, 8 000)
3.1巡游出租车轻微优势0.501＜HHIct≤0.520[12 800, 15 285)(16 715, 19 200]
3.2巡游出租车优势竞争0.520＜HHIct≤0.625[8 000, 12 800)(19 200, 24 000]
3.3巡游出租车基本垄断HHIct＞0.625[0, 8 000)(24 000, 32 000]
表 3  网约车、巡游出租车竞争程度标度
图 5  西安市主城区网约车与巡游出租车竞争程度分布
参数赋值参数赋值
政府宽松监管成本$ {C}_{{\mathrm{xl}}} $/
万元		10政府严格监管成本$ {C}_{{\mathrm{xs}}} $/万元15
网约车罚金$ {F}_{z} $/万元7网约车运营成本$ {C}_{{\mathrm{zo}}} $/万元6
巡游出租车罚金$ {F}_{n} $/万元7巡游出租车运营成本$ {C}_{{\mathrm{no}}} $/
万元		6
乘客监督奖励$ {B}_{y} $/万元4乘客监督成本$ {C}_{y} $/万元2
网约车合规收益$ {R}_{z} $/万元9巡游出租车合规收益$ {R}_{n} $/
万元		9
网约车违规额外收益$ \Delta {R}_{z} $/万元3巡游出租车违规额外收益$ \Delta {R}_{n} $/万元3
政府严格监管概率$ x $/%20网约车合规经营概率$ z $/%80
乘客参与监督概率$ y $/%20巡游出租车合规经营概率$ n $/%80
表 4  博弈模型参数及赋值
博弈场景$ {u}_{1} $$ {v}_{1} $$ {u}_{2} $$ {v}_{2} $$ {q}_{1} $$ {q}_{2} $
场景196961.50.75
场景296961.50.75
场景396961.50.75
场景496961.50.75
场景5121396?2.52.75
场景6121396?2.52.75
场景7129.59611
场景8126964.5?0.75
场景99612133.5?2.25
场景109612133.5?2.25
场景1196129.51.750.375
场景129612603
场景1312131213?0.5?0.25
场景1412131213?0.5?0.25
场景15129.5129.51.250.625
场景1612612631.5
表 5  不同博弈场景下参数赋值纳什均衡结果（网约车优先实施策略）
博弈场景$ K $
策略1策略2
场景11/22
场景21/22
场景31/22
场景41/22
场景7114/3
场景113/141
场景151/22
场景161/22
表 6  博弈场景的纳什均衡结果
删除场景q1q2说明
场景53.5?2.25q2为负值，表示市场无法有效运作
场景63.5?2.25q2为负值，表示市场无法有效运作
场景803q1=0，表示巡游出租车垄断市场
场景9?2.52.75q1为负值，表示市场无法有效运作
场景10?2.52.75q1为负值，表示市场无法有效运作
场景124.5?0.75q2为负值，表示市场无法有效运作
场景13?0.5?0.25q1、q2均为负值，表示市场完全失控
场景14?0.5?0.25q1、q2均为负值，表示市场完全失控
表 7  不稳定博弈场景的删除说明
图 6  总碳排放与网约车数量的关系
图 7  特定情形下的总碳排放比较
图 8  特定情形下碳排放随网约车数量的变化
图 9  模型关键参数对碳排放的影响
图 10  模型关键参数的期望-方差
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