| 引用本文: | 傅泽新,陈旭梅,王宇擎,张义鑫.智能网联环境下管理车道设置策略与影响因素分析[J].哈尔滨工业大学学报,2023,55(7):24.DOI:10.11918/202209011 |
| FU Zexin,CHEN Xumei,WANG Yuqing,ZHANG Yixin.Managed lane setting strategies and influence factor analysis in intelligent connected environment[J].Journal of Harbin Institute of Technology,2023,55(7):24.DOI:10.11918/202209011 |
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| 摘要: |
| 为探索人工驾驶车辆（human driven vehicle,HDV）与自动驾驶车辆（connected and autonomous vehicle,CAV）构成的新型混合交通流的运行规律,研究不同管理车道设置策略对城市快速路新型混合交通流产生的影响。首先,基于不同种类车辆间跟驰与专用道选择概率间的耦合关系,定量描述了不同管理车道设置策略条件下快速路通行能力演变机理。基于此,利用SUMO仿真平台分析了新型混合交通流条件下车辆延误的变化规律。最后,通过对车辆换道类型与换道间隙分析,研究了不同管理车道设置策略对交通流车辆间碰撞风险的影响。结果表明:CAV渗透率低于30%或大于80%,且只限制HDV在普通车道通行时,通行能力最大；CAV渗透率介于30%~80%之间,应考虑设置公交和CAV专用车道；设置公交和CAV专用车道但不限制其通行路权时,路段平均延误最小且几乎不受CAV渗透率的影响；当只为CAV或多乘员车辆（high-occupancy vehicle,HOV）设置管理车道时,会增加车辆碰撞风险。这说明CAV渗透率是建立合理的管理车道设置策略的重要参考因素,CAV渗透率对设置管理车道路段的通行能力有很大影响,而路段平均延误和交通流车辆间碰撞风险则更受管理车道设置策略的影响。 |
| 关键词: 智能交通 管理车道设置策略 SUMO仿真 新型混合交通流 通行能力 |
| DOI：10.11918/202209011 |
| 分类号:U491.2 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(71871013) |
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| Managed lane setting strategies and influence factor analysis in intelligent connected environment |
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FU Zexin,CHEN Xumei,WANG Yuqing,ZHANG Yixin
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（School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China）
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| Abstract: |
| In order to explore the operation rules of the mixed traffic flow consisting of human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), this paper studies the impact of different managed lane setting strategies on the traffic flow of urban expressways. First, on the basis of the coupling relationship between car-following and lane selection probability of different types of vehicles, the evolution mechanism of expressway capacity was quantitatively described under different managed lane setting strategies. Then, the variation pattern of vehicle delays was analyzed using the SUMO simulation platform under mixed traffic flow conditions. Finally, the influence of different managed lane setting strategies on the collision risk of vehicles was investigated by analyzing the vehicle lane changing types and gaps. Results showed that the capacity reached the maximum value when the CAV penetration rate was less than 30% or more than 80% and HDVs were restricted to general purpose lanes. Bus and CAV lanes should be considered when the CAV penetration rate was between 30% and 80%. The average delay on the roadway was the lowest and almost unrelated to the CAV penetration rate when bus and CAV lanes were designed but their right-of-way was not restricted. The risk of vehicle collisions increased when managed lanes were provided only for CAVs or high-occupancy vehicles (HOVs). These results indicate that CAV penetration rate is an important factor for development of a reasonable managed lane setting strategy, and has significant influence on the capacity of the road section with managed lanes. The average delay of road sections and the collision risk between vehicles in the traffic flow are mainly influenced by the managed lane setting strategy. |
| Key words: intelligent transportation managed lane setting strategy SUMO simulation mixed traffic flow capacity |
