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浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (6): 1019-1026    DOI: 10.3785/j.issn.1008-973X.2021.06.001
交通工程、土木工程     
智能网联混行环境下交叉口时空资源配置优化
钱国敏1(),凡俊生2,何春光1,3,章立辉1,*(),王殿海1
1. 浙江大学 建筑工程学院，浙江 杭州 310058
2. 浙江大学 工程师学院，浙江 杭州 310015
3. 新疆农业大学 交通与物流工程学院，新疆 乌鲁木齐 830052
Optimization of spatial-temporal resources at intersections under environment of mixed traffic flow with connected and autonomous vehicles and human-driven vehicles
Guo-min QIAN1(),Jun-sheng FAN2,Chun-guang HE1,3,Li-hui ZHANG1,*(),Dian-hai WANG1
1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2. Polytechnic Institute, Zhejiang University, Hangzhou 310015, China
3. School of Transportation and Logistics Engineering, Xinjiang Agricultural University, Urumqi 830052, China
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摘要：

在网联自动车辆（CAVs）与人工驾驶车辆混行环境下，构建混合整数线性规划（MILP）模型，以优化交叉口时空资源配置. 该模型以交叉口通行能力最大化为目标，约束条件主要包括车道渠化、流量分配和信号配时等相关约束. 以典型四车道十字交叉口为例，在网联自动车不同驾驶行为和不同渗透比例的条件下，优化交叉口渠化方案和信号配时方案. 结果表明，随着网联自动车占比和跟驰行为的改变，交叉口最优渠化方案和信号配时方案须相应调整. 网联自动车占比增大和跟车时距减小，均有利于提高交叉口的通行能力，且当网联自动车跟车时距不受前车类型的影响时，交叉口通行能力提高更多.
关键词： 网联自动车;  混行车流;  交叉口通行能力;  车道渠化;  信号配时    
Abstract:

A mixed integer linear programming (MILP) model was proposed to optimize the spatial-temporal resources at intersections under the environment of mixed traffic flow with connected and autonomous vehicles (CAVs) and human-driven vehicles. The objective of the model is to maximize the intersection capacity, and the constraints mainly include those regarding lane channelization, flow distribution and signal timing settings. The lane channelization and signal timing scheme at intersections were optimized with different CAV driving behavior settings and different CAV penetration rates by taking a typical four-lane intersection as an example. Results show that the optimal channelization and signal timing scheme need to be adjusted with the change of CAV penetration rate and CAV car following behavior. The increase of the CAV penetration rate and the decrease of CAV headway are both beneficial to the improvement of the intersection capacity. The increase in the intersection capacity is slightly larger when the headway of CAV is not affected by the type of vehicles ahead.
Key words: connected and autonomous vehicle    mixed traffic flow    intersection capacity    lane channelization    signal timing
收稿日期: 2021-01-10 出版日期: 2021-07-30
CLC:  U 9  
基金资助: 国家重点研发计划资助项目（2018YFB1600500）
通讯作者: 章立辉     E-mail: gmqian@zju.edu.cn;lihuizhang@zju.edu.cn
作者简介: 钱国敏（1991—），男，博士生，从事交通控制和交通规划的研究. orcid.org/0000-0002-8617-6749. E-mail： gmqian@zju.edu.cn
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钱国敏
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引用本文:

钱国敏,凡俊生,何春光,章立辉,王殿海. 智能网联混行环境下交叉口时空资源配置优化[J]. 浙江大学学报(工学版), 2021, 55(6): 1019-1026.

Guo-min QIAN,Jun-sheng FAN,Chun-guang HE,Li-hui ZHANG,Dian-hai WANG. Optimization of spatial-temporal resources at intersections under environment of mixed traffic flow with connected and autonomous vehicles and human-driven vehicles. Journal of ZheJiang University (Engineering Science), 2021, 55(6): 1019-1026.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.06.001    或    https://www.zjujournals.com/eng/CN/Y2021/V55/I6/1019

图 1  混行车流中4种可能的车头时距
图 2  典型交叉口示例
i Qi,j
j=1 j=2 j=3 j=4
1 ? 400 700 300
2 600 ? 200 600
3 600 200 ? 400
4 400 800 300 ?
表 1  算例中的交通需求
图 3  网联自动车跟驰受前车类型影响时的最优渠化方案
$r$ i=1, j=2 i=1, j=3 i=1, j=4 i=2, j=1 i=2, j=3 i=2, j=4
$\theta $ $\phi $ $\theta $ $\phi $ $\theta $ $\phi $ $\theta $ $\phi $ $\theta $ $\phi $ $\theta $ $\phi $
0 0.706 0.200 0.706 0.200 0.000 0.456 0.225 0.500 0.483 0.100 0.506 0.150
0.1 0.000 0.232 0.561 0.179 0.000 0.238 0.000 0.500 0.790 0.136 0.790 0.136
0.2 0.000 0.214 0.000 0.214 0.000 0.214 0.000 0.500 0.781 0.136 0.781 0.136
0.5 0.755 0.200 0.755 0.200 0.000 0.237 0.205 0.500 0.287 0.136 0.287 0.136
0.9 0.000 0.232 0.019 0.179 0.000 0.238 0.000 0.500 0.552 0.136 0.552 0.136
1 0.425 0.232 0.425 0.232 0.200 0.238 0.425 0.500 0.000 0.100 0.000 0.150
$r$ i=3, j=1 i=3, j=2 i=3, j=4 i=4, j=1 i=4, j=2 i=4, j=3
$\theta $ $\phi $ $\theta $ $\phi $ $\theta $ $\phi $ $\theta $ $\phi $ $\theta $ $\phi $ $\theta $ $\phi $
0 0.000 0.175 0.000 0.175 0.000 0.175 0.225 0.232 0.225 0.208 0.000 0.238
0.1 0.561 0.179 0.561 0.150 0.000 0.232 0.282 0.229 0.282 0.229 0.282 0.229
0.2 0.550 0.181 0.534 0.150 0.550 0.181 0.264 0.220 0.264 0.220 0.264 0.238
0.5 0.005 0.150 0.000 0.150 0.005 0.232 0.473 0.232 0.473 0.208 0.005 0.237
0.9 0.738 0.150 0.552 0.150 0.738 0.232 0.282 0.220 0.282 0.220 0.249 0.238
1 0.200 0.175 0.200 0.175 0.200 0.175 0.706 0.220 0.706 0.220 0.000 0.375
表 2  部分网联自动车占比下的最优信号方案
图 4  网联自动车跟驰受前车类型影响时的交叉口通行能力
图 5  网联自动车跟驰不受前车类型影响时的部分最优渠化方案
图 6  网联自动车跟驰不受前车类型影响时的交叉口通行能力
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