交通运输系统工程与信息

交通运输系统工程与信息 ›› 2023, Vol. 23 ›› Issue (3): 39-47.DOI: 10.16097/j.cnki.1009-6744.2023.03.005

• 交通运输业高质量发展策略 • 上一篇    下一篇

雾天场景下高速公路通行能力分析及提升策略

秦严严1，肖腾飞1，贺正冰*2   

  1. 1.重庆交通大学，交通运输学院，重庆400074；2.北京工业大学，交通工程北京市重点实验室，北京100124
  • 收稿日期:2023-02-08 修回日期:2023-03-18 接受日期:2023-03-28 出版日期:2023-06-25 发布日期:2023-06-22
  • 作者简介:秦严严(1989-)，男，江苏沛县人，副教授，博士
  • 基金资助:
    国家自然科学基金 (52002044)

Analysis and Improvement Strategy on Freeway Traffic Capacity in Foggy Weather

QIN Yan-yan1, XIAO Teng-fei1, HE Zheng-bing*2   

  1. 1. School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China; 2. Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
  • Received:2023-02-08 Revised:2023-03-18 Accepted:2023-03-28 Online:2023-06-25 Published:2023-06-22
  • Supported by:
    National Natural Science Foundation of China (52002044)

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摘要： 为研究雾天场景高速公路通行能力，本文提出基于车车通信(Vehicle-to-Vehicle,V2V)环境的雾天通行能力提升策略。首先，选取雾天场景标定的Gips模型描述雾天环境下的车辆跟驰行为，推导雾天跟驰行为车头间距一速度函数式，构建雾天高速公路通行能力分析方法，并从雾天浓度和车流限速两个角度分析通行能力的影响。然后，从雾天高速公路通行能力影响机理层面，对反应时间Tn、后车最大制动减速度bn,以及后车预计前车最大制动减速度bn-1,进行参数敏感性分析。最后，基于雾天V2V环境，考虑反应时间与制动减速度对通行能力的影响作用，提出雾天场景下高速公路通行能力提升的跟驰控制策略。研究结果表明：轻雾场景（能见度150m）和浓雾场景（能见度60m)分别在限速80kmh-1和100kmh-1时的通行能力最大，且均在限速60kmh-1时的通行能力最小。相比最低限速40km·h-1,轻雾场景和浓雾场景在限速80kmh-1和100kmh-1对应的最大通行能力分别提升21.83%和9.68%，在限速60km·h-1对应的最小通行能力分别降低15.88%和4.61%。通行能力与Tn呈负相关性，同时，当bn-1>bn时，有利于通行能力的提升。本文所提控制策略能够有效提升雾天高速公路通行能力，在置信水平为95%的情况下，通行能力提升效果显著，不同雾天浓度和限速条件下，通行能力平均提升幅度为44.22%。

关键词: 交通工程, 通行能力, 跟驰模型, 雾天场景, 提升策略

Abstract: This paper studies the freeway traffic capacity in foggy weather. An improvement strategy for freeway traffic capacity in foggy weather is proposed based on vehicle-to-vehicle (V2V) communications. Firstly, a Gipps model calibrated in foggy weather was selected to describe the car-following behavior, and its spacing-speed function was derived to construct the analysis method of freeway capacity. Secondly, the influence of traffic capacity was analyzed from the perspectives of different foggy scenes and speed limit conditions. Under the influence mechanism by foggy weather, we conducted sensitivity analyses on driver reaction time Tn, the maximum brake deceleration bn of the following vehicle, and the estimated maximum brake deceleration bn-1 of the front vehicle by the follower. Finally, considering the influence of the driver reaction time and the braking deceleration on traffic capacity, a car-following control strategy for improving freeway capacity was proposed based on foggy V2V conditions. The results show that speed limit values of 80 km · h-1 and 100 km · h-1 will lead to the maximum traffic capacity under light fog (visibility of 150 meters) and heavy fog (visibility of 60 meters) conditions, respectively. Both conditions of light fog and heavy fog have the minimum traffic capacity when 60 km · h-1 is selected as the speed limit value. Compared with the speed limit of 40 km · h-1, the maximum traffic capacities corresponding to the speed limit of 80 km · h-1 in light fog and 100 km · h-1 in heavy fog increase by 21.83% and 9.68%, respectively. Additionally, the minimum traffic capacities corresponding to the speed limit of 60 km · h- 1 are reduced by 15.88% and 4.61% under light fog and heavy fog conditions, respectively. Traffic capacity improves with the decrease of the driver reaction time and it is positive for capacity improvement when bn-1> bn. The proposed control strategy can effectively improve the freeway capacity in foggy weather, and the control strategy has a significant effect on the improvement of traffic capacity with a confidence level of 95%. The average improvement percentage is 44.22% under different foggy scenes and speed limit conditions.

Key words: traffic engineering, traffic capacity, car-following model, foggy weather, improvement strategy

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