混驾环境下考虑异质速度与路径选择行为的交通配流模型

韩飞; 王建; 李岩; 张锐; 孙超; 孔毅恒

doi:10.3963/j.jssn.1674-4861.2025.06.015

混驾环境下考虑异质速度与路径选择行为的交通配流模型

doi: 10.3963/j.jssn.1674-4861.2025.06.015

韩飞^1,,
王建^2, ,,
李岩¹,
张锐¹,
孙超³,
孔毅恒¹

1.
长安大学运输工程学院西安 710064
2.
东南大学交通学院南京 211189
3.
江苏大学汽车与交通工程学院江苏镇江 212016

基金项目:

国家自然科学基金项目 52472340

国家自然科学基金项目 52272316

陕西省重点研发计划项目 2023-YBGY-138

陕西省自然科学基金项目 2020JQ-370

中央高校基本科研业务费专项资金项目 300102344603

详细信息

作者简介:
韩飞（1986—），博士，讲师. 研究方向：交通系统建模优化. E-mail：hanfei@chd.edu.cn

通讯作者:
王建（1988—），博士，研究员. 研究方向：交通网络规划与管理、自动驾驶控制. E-mail：jianw@seu.edu.cn

中图分类号: U491.1
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出版历程
- 收稿日期: 2025-05-22
- 网络出版日期: 2026-03-13

A Traffic Assignment Model Considering Heterogeneous Speed and Route Choice Behaviors under Mixed Driving Environments

HAN Fei^1
,,
WANG Jian^{2
, ,},
LI Yan¹,
ZHANG Rui¹,
SUN Chao³,
KONG Yiheng¹

1.
School of Transportation Engineering, Chang'an University, Xi'an 710064, China
2.
School of Transportation, Southeast University, Nanjing 211189, China
3.
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212016, Jiangsu, China

摘要

摘要: 为了定量评估混驾环境下网联自驾车（connected autonomous vehicle，CAV）与人驾车（human-driven vehicle，HDV）的行为异质性对路网交通系统性能的影响，构建了考虑CAV、HDV速度与路径联合选择行为异质性的交通均衡配流模型。通过考虑行驶速度与感知碰撞风险、出行时间，以及超速罚单风险三者之间的定量关系，建立基于效用理论的速度选择行为模型，解析CAV、HDV最优速度选择和限速遵从决策的行为异质性。引入路径时间剩余的概念描述出行者权衡路径时间和费用的非补偿决策，采用路径时间剩余最大化准则下的用户均衡、Logit随机用户均衡条件分别描述CAV、HDV的异质性路径选择行为，速度和路径选择行为通过路径时间剩余相互耦合。基于变分不等式理论构建等价的路网交通均衡配流模型，并设计1种启发式的双层循环迭代算法求解模型。采用Nguyen-Dupuis网络和淮南市路网对模型和算法进行验证，并对比分析不同交通管控条件下的路网总出行时间和事故风险。结果表明：随着CAV市场渗透率由20%提高至80%，路网总时间在40、50 km/h这2种限速情景下呈现增大趋势，而路网事故风险则在所有限速情景下，均呈现先增大后减小趋势；在高限速（80 km/h）下，路网总时间随CAV交通折算系数下降（0.9~0.1）而减小，但路网事故风险会增大，在低限速（40 km/h）下，CAV交通折算系数的影响则较微弱；随着路网限速值由40 km/h提高至80 km/h，路网总时间和事故风险均先减小而后稳定或增大，说明合适的限速值可以同时降低路网总时间和事故风险。
- 交通工程 /
- 交通配流模型 /
- 变分不等式 /
- 混驾环境 /
- 速度-路径选择行为 /
- 行为异质性
Abstract: This study aims at quantitatively evaluating the impacts of travelers'heterogeneous behaviors of connected autonomous vehicle (CAV) and human-driven vehicle (HDV) on road network performance. A traffic equilibrium assignment model is proposed under mixed driving environments, which considers the heterogeneity in joint speed-route choice behaviors of CAV and HDV. Specifically, the quantitative relationship is established between driving speed and perceived crash risk, travel time and speeding ticket risk. By using the quantitative relationship, a speed choice behavior model is then formulated based on utility theory. The model could analytically characterize the behavior heterogeneity in optimal speed selection and speed limit (SL) obeying decisions of CAV and HDV. The concept of path time surplus (PTS) is introduced to characterize travelers'non-compensatory decision rules when weighing path travel time and monetary cost. The user equilibrium (UE) and Logit-based stochastic user equilibrium (SUE) principles with PTS maximization are used to describe the heterogeneous route choice behaviors of CAV and HDV, respectively. The speed choice behavior would intertwine with the path choice behavior via the PTS. By utilizing variational inequality (VI) theory, an equivalent mixed traffic equilibrium assignment model is finally proposed. A heuristic double-layer loop iterative algorithm is also developed to solve the model. Nguyen-Dupuis network and Huainan road network are used to validate the proposed model and algorithm. The total travel time (TTT) and total accident risk (TAR) are compared and analyzed in a road network under different traffic regulation conditions. The results indicate that with the market penetration rate (MPR) of CAVs increasing from 20% to 80%, the TTT shows an increasing trend in both 40 km/h and 50 km/h SL scenarios. Meanwhile, the TAR would increase first and then decrease across all SL scenarios. Under a high SL scheme (80 km/h), the TTT would decrease as the CAV traffic conversion factor reduces from 0.9 to 0.1, while the TAR exhibits a rising trend. Under a low SL scheme (40 km/h), however, the influences of CAV traffic conversion factor would become very insignificant. As the SL value increases from 40 km/h to 80 km/h, both the TTT and TAR decrease initially, and then stabilize or increase. The numerical results demonstrate that an appropriate speed limit scheme can reduce both TTT and TAR simultaneously.
- traffic engineering /
- traffic assignment model /
- variational inequality /
- mixed driving environments /
- speed-route choice behaviors /
- behavior heterogeneity

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图 1 算法流程图

Figure 1. Flow chart of the algorithm

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图 2 Nguyen-Dupuis网络

Figure 2. Nguyen-Dupuis network

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图 3 算法求解迭代过程

Figure 3. Iteration process of the solution algorithm

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图 4 不同限速值和CAV市场渗透率下的路网总时间

Figure 4. Total travel time under different speed limits and CAV penetration rates

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图 5 不同CAV市场渗透率和折算系数下的路网总时间

Figure 5. Total travel time under different CAV penetration ratesand conversion factors

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图 6 3个参数不同取值组合下的路网总时间

Figure 6. Total travel time under different value combinations of the three parameters

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图 7 不同限速值和CAV市场渗透率下的路网事故风险成本

Figure 7. Total accident risk under different speed limits and CAV penetration rates

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图 8 不同CAV市场渗透率和折算系数下的路网事故风险成本

Figure 8. Total accident risk under different CAV penetration ratesand conversion factors

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图 9 3个参数不同取值组合下的路网事故风险成本

Figure 9. Total accident risk under different value combinations of the three parameters

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图 10 淮南市路网图

Figure 10. Huainan road network

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表 1 Nguyen-Dupuis网络输入数据

Table 1. Input data of Nguyen-Dupuis network

路段编号	自由旅行时间/h	路段容量/（veh/h）	路段收费/元	路段长度/km	限速值/（km/h）	路段编号	自由旅行时间/h	路段容量/（veh/h）	路段收费/元	路段长度/km	限速值/（km/h）
1	0.12	900	1.33	8.40	40	11	0.17	700	0.83	12.00	40
2	0.13	700	1.17	9.60	40	12	0.17	700	0.83	12.00	40
3	0.15	700	1.00	10.80	40	13	0.15	600	1.00	10.80	40
4	0.23	900	0.17	16.80	40	14	0.13	700	1.17	9.60	40
5	0.08	800	1.67	6.00	40	15	0.15	700	1.00	10.80	40
6	0.15	600	1.00	10.80	40	16	0.13	700	1.17	9.60	40
7	0.08	900	1.67	6.00	40	17	0.12	300	1.33	8.40	40
8	0.22	500	0.33	15.60	40	18	0.25	700	0.00	18.00	40
9	0.08	300	1.67	6.00	40	19	0.18	700	0.67	13.20	40
10	0.15	400	1.00	10.80	40

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表 2 模型参数值

Table 2. The model parameter values

用户类别	α_m	β_m	γ_m	ξ_m¹	ξ_m²	ε_m	η_m
CAV用户	2×10^-5	10	1×10^-4	2.5	2.5	2	2
HDV用户	4×10^-5	30	2×10^-4	2.5	2.5	2	2

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表 3 均衡状态的路段流量和速度

Table 3. The link flows and speed at equilibrium state

路段编号	路段均衡流量/（veh/h）	路段可行速度/（km/h）	CAV最优速度/（km/h）	HDV最优速度/（km/h）	CAV实际速度/（km/h）	HDV实际速度/（km/h）	路段平均速度/（km/h）
1	773.24	67.72	48.90	52.09	40.00	52.09	44.58
2	426.76	70.84	49.12	52.34	40.00	52.34	45.40
3	243.91	71.84	50.85	54.43	40.00	54.43	54.43
4	556.09	70.92	47.99	51.10	40.00	51.10	41.52
5	335.98	71.67	50.85	54.43	40.00	54.43	54.43
6	681.17	60.71	48.58	51.73	40.00	51.73	43.47
7	295.32	71.88	50.85	54.43	40.00	54.43	54.43
8	206.48	71.69	50.85	54.43	40.00	54.43	54.43
9	87.85	71.92	50.85	54.43	40.00	54.43	54.43
10	207.47	71.23	50.85	54.43	40.00	54.43	54.43
11	348.79	71.50	48.64	51.80	40.00	51.80	43.68
12	555.63	68.86	48.80	51.97	40.00	51.97	44.22
13	681.63	61.39	47.93	51.03	40.00	51.03	41.32
14	762.11	61.35	49.44	52.74	40.00	52.74	46.72
15	651.21	66.09	49.15	52.38	40.00	52.38	45.54
16	318.37	71.54	50.85	54.43	40.00	54.43	54.43
17	165.82	71.01	50.85	54.43	40.00	54.43	54.43
18	260.94	71.86	47.79	50.88	40.00	50.88	40.87
19	681.63	65.86	47.93	51.03	40.00	51.03	41.32

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表 4 不同限速值下的计算结果

Table 4. The results under different speed limit values

限速情景/（km/h）	算法外层迭代次数	最小路段平均速度（/ km/h）	对应路段编号	最大路段平均速度（/ km/h）	对应路段编号	路网总时间/h	路网事故风险成本/（×10⁶元）
40	5	5.87	114	54.43	67	41 147.49	1 557.16
45	15	6.09	114	56.84	182	39 171.75	656.13
50	13	6.20	11	59.39	182	37 462.48	201.56
55	8	6.20	11	62.06	137	36 162.99	54.25
60	13	6.20	11	64.86	66	35 560.14	9.92

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