应急车路径与专用道协同优化的双层规划方法

doi:10.16097/j.cnki.1009-6744.2025.01.019

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 202-211.DOI: 10.16097/j.cnki.1009-6744.2025.01.019

应急车路径与专用道协同优化的双层规划方法

龙科军^1a,1b，邹道兴^1b，刘洋^*2，马昌喜²，马璐³

1. 长沙理工大学，a.智能道路与车路协同湖南省重点实验室，b.交通运输工程学院，长沙410114；2. 兰州交通大学，交通运输学院，兰州730070；3.招商局重庆交通科研设计院有限公司，重庆400067

收稿日期:2024-08-27 修回日期:2024-11-12 接受日期:2024-11-28 出版日期:2025-02-25 发布日期:2025-02-24
作者简介:龙科军(1974—)，男，湖南双峰人，教授，博士。
基金资助:
国家自然科学基金(52172313，52062027)；新疆自治区重点研发计划项目(2023B03004-3)。

Bi-level Programming Method for Coordinated Optimization of Emergency Vehicle Route and Dedicated Lanes

LONG Kejun^1a,1b, ZOU Daoxing^1b, LIU Yang^*2, MA Changxi², MA Lu³

1a. Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-infrastructure Systems, 1b. School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China; 2. School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China; 3. China Merchants Chongqing Communications Technology Research & Design Institute Co Ltd, Chongqing 400067, China

Received:2024-08-27 Revised:2024-11-12 Accepted:2024-11-28 Online:2025-02-25 Published:2025-02-24
Supported by:
National Natural Science Foundation of China (52172313，52062027)；Key Research and Development Plan of Xinjiang Autonomous Region (2023B03004-3)。

摘要/Abstract

摘要： 已有应急车辆路径规划的研究中，专用车道布设方案通常被视为已知条件，因此，本文提出一种同时优化应急车路径及专用车道的双层规划模型。在规划应急车路径时，将是否设置应急专用车道定义为模型的决策变量，并引入前景理论来衡量设置专用车道对交通流的影响。上层模型目标函数包含应急车行程时间和顺畅通行前景值两部分，其中前景值作为部署应急专用车道的决策依据；下层模型基于Wardrop均衡原理进行交通分配。结合遗传算法和禁忌搜索算法，提出GA-TS(GeneticAlgorithm-Tabu Search)算法求解模型。通过在Nguyen-Dupuis仿真网络上进行数值实验，验证了模型和算法的有效性。实验结果表明：与不部署应急专用车道相比，在不增加路径交通饱和度的情况下，本文模型能将应急车辆的行程时间缩短10.69%。敏感性分析结果表明，在不同交通需求下，本文模型均能有效缩短应急车辆行程时间，并且随着交通需求增大，应急车辆行程时间缩短越明显。此外，相比于暴力搜索算法，本文设计的算法在求解模型时的平均耗时降低了87.02%，显著提高了模型的求解效率。

关键词: 城市交通, 应急车辆优先, 遗传算法, 路径优化, 前景理论

Abstract: Existing research on emergency vehicle route planning typically treating the deployment of dedicated lanes as a predetermined condition. This study proposes a bi-level programming model that concurrently optimizes emergency vehicle route and dedicated lane deployment. In this model, the deployment of dedicated lanes on each road segment is defined as a decision variable in route planning. Prospect theory is introduced to evaluate the impact of these dedicated lanes on traffic saturation. The objective function of the upper-level model comprises two components: emergency vehicle travel time and the prospect value of emergency vehicles travel smoothly, with the latter serving as the decision criterion for dedicated lane deployment. The lower-level model performs traffic assignment based on Wardrop's Equilibrium Principle. An innovative GA TS algorithm, combining tabu search and genetic algorithm, is proposed to solve the model. Numerical experiments on the Nguyen-Dupuis simulation network verify the effectiveness of the model and algorithm. Results show that this model reduces emergency vehicle travel time by 10.69% without increasing traffic saturation. Multiple experiments under different traffic demands further confirm that the proposed model can effectively shorten the travel time of emergency vehicles with insignificant impact on traffic saturation, and this reduction becomes more pronounced with increasing traffic demand. Additionally, the GA-TS algorithm improves solving efficiency by reducing the average solution time by 87.02%.

Key words: urban traffic, emergency vehicle priority, genetic algorithm, route optimization, prospect theory

中图分类号:

U491

龙科军, 邹道兴, 刘洋, 马昌喜, 马璐. 应急车路径与专用道协同优化的双层规划方法[J]. 交通运输系统工程与信息, 2025, 25(1): 202-211.

LONG Kejun, ZOU Daoxing, LIU Yang, MA Changxi, MA Lu. Bi-level Programming Method for Coordinated Optimization of Emergency Vehicle Route and Dedicated Lanes[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 202-211.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2025.01.019

http://www.tseit.org.cn/CN/Y2025/V25/I1/202

参考文献

[1]LI J, PENG L, HOU K, et al. Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review[J]. Digital Transportation and Safety, 2023, 2(2): 89-111.

[2]SU H R, ZHONG Y D, CHOW J Y, et al. EMVLight: A multi-agent reinforcement learning framework for an emergency vehicle decentralized routing and traffic signal control system[J]. Transportation Research Part C: Emerging Technologies, 2023, 146: 103955.

[3]龙科军,张仲根,刘洋,等.城市道路应急车辆借道通行与信号协同优化模型[J].交通运输系统工程与信息, 2023, 23(5): 194-201. [LONG K J, ZHANG Z G, LIU Y, et al. Cooperative optimization model for emergency vehicles using reverse left-turn lanes and traffic signals[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(5): 194-201.]

[4]KARMAKAR G, CHOWDHURY A, KAMRUZZ J, et al. A smart priority-based traffic control system for emergency vehicles[J]. IEEE Sensors Journal, 2020, 21 (14): 15849-15858.

[5]WANG Z, ZLATANOVA S. Safe route determination for first responders in the presence of moving obstacles[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(3): 1044-1053.

[6]LIU Q X, XU P, WU YH, et al. A two-stage algorithm for vehicle routing problem with charging relief in post disaster[J]. IET Intelligent Transport Systems, 2023, 17 (8): 1525-1543.

[7]CHOI S, KIM J, YU H, et al. Real-time prediction of arterial vehicle trajectories: An application to predictive route guidance for an emergency vehicle[C]. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand: IEEE, 2019.

[8]宋现敏,马林,李丽丽.公交与右转混合型专用道仿真分析及效益评价[J].中国公路学报,2019,32(5): 142-152. [SONG X M, MA L, LI L L, et al. Simulation analysis and benefit evaluation of bus and right turn exclusive lanes[J]. China Journal of Highway and Transport, 2019, 32(5): 142-152.]

[9]董红召,杨嘉炜,全程.车联网下公交专用道复用的动态清空控制方法[J].交通运输系统工程与信息,2024,24(3): 12-20. [DONG H Z, YANG J W, QUAN C. Dynamic clearance control method for reusing bus lanes under vehicular networking[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(3): 12-20.]

[10] NIKOO N, BABAEI M, MOHAYMANY A S. Emergency transportation network design problem: Identification and evaluation of disaster response routes[J]. International Journal of Disaster Risk Reduction, 2017, 27: 7-20.

[11] ZENG Z L, YI W, WANG S A, et al. Emergency vehicle routing in urban road networks with multistakeholder cooperation[J]. Journal of Transportation Engineering Part A: Systems, 2021, 147(10): 04021064.

[12] WU J, KULCSáR B, AHN S, et al. Emergency vehicle lane pre-clearing: From microscopic cooperation to routing decision making[J]. Transportation Research Part B: Methodological, 2020, 141: 223-239.

[13] GUO H L, CAO Z G, SESHADRI M, et al. Routing multiple vehicles cooperatively: Minimizing road network breakdown probability[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2017, 1 (2): 112-124.

[14] HE J, YANG H, TANG T Q, et al. Optimal deployment of wireless charging lanes considering their adverse effect on road capacity[J]. Transportation Research Part C: Emerging Technologies, 2020, 111: 171-184.

[15] 杨云, 张小强,徐新昊.基于累积前景理论的城际列车分时定价研究[J].交通运输系统工程与信息,2022,22(4): 23-29. [YANG Y, ZHANG X Q, XU X H. Intercity train time-sharing pricing based on cumulative prospect theory[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(4): 23-29.]

应急车路径与专用道协同优化的双层规划方法

Bi-level Programming Method for Coordinated Optimization of Emergency Vehicle Route and Dedicated Lanes

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	晋泽倩, 俞诚成, 叶昕. 混合效用和后悔规则下的洪涝交通疏散决策行为研究[J]. 交通运输系统工程与信息, 2025, 25(2): 16-25.
[2]	周雨阳, 赵聪颖, 李京昆, 陈艳艳, 柳堤, 王书灵. 多类城市职住及通勤状态对出行方式选择影响比较[J]. 交通运输系统工程与信息, 2025, 25(2): 26-35.
[3]	彭其渊, 刘思源, 江山, 冯涛, 陈垚, 张永祥. 贯通运营下市域铁路与地铁列车开行方案协同优化[J]. 交通运输系统工程与信息, 2025, 25(2): 36-47.
[4]	蒋阳升, 叶肖甫, 徐尉耀, 刘洪汛, 胡路. 考虑转运与拼车的共享自动驾驶电动汽车动态运营[J]. 交通运输系统工程与信息, 2025, 25(2): 58-68.
[5]	赵雪亭, 胡立伟, 周君. 城市交通拥塞风险场级联失效及韧性评估研究[J]. 交通运输系统工程与信息, 2025, 25(2): 146-156.
[6]	武慧荣, 王玉莹, 盛椿婷. 考虑动态需求的城市货运公交线路规划模型研究[J]. 交通运输系统工程与信息, 2025, 25(2): 214-226.
[7]	徐伟汉, 李欣, 王天奇. 基于生态驾驶的常规和需求响应公交混合调度方法[J]. 交通运输系统工程与信息, 2025, 25(2): 227-240.
[8]	李昊, 陈绍宽, 徐彬, 石梦彤, 肖迪, 陈梓琦. 考虑值乘能力差异的地铁跨线乘务轮班计划优化方法[J]. 交通运输系统工程与信息, 2025, 25(2): 253-260.
[9]	薛锋, 肖恩, 杨颖, 金成, 罗建. 基于多旅行商问题建模的地铁乘务排班计划优化[J]. 交通运输系统工程与信息, 2025, 25(2): 261-272.
[10]	张鹏, 吴照杰, 孙超, 李文权. 路口混合放行下的干线红波带宽最小优化模型[J]. 交通运输系统工程与信息, 2025, 25(2): 293-303.
[11]	高攀, 黄柳森, 赵旭. 考虑转港调度的内河港口群多泊位联合配置策略[J]. 交通运输系统工程与信息, 2025, 25(2): 328-337.
[12]	许奇, 秦贝宁, 任澎, 陈越, 赖瑾璇. 建成环境对公交接驳地铁客流的异质性影响[J]. 交通运输系统工程与信息, 2025, 25(2): 352-363.
[13]	姚明, 王宇航, 曹淑超, 马露涵. 手机分心条件下行人运动特性研究[J]. 交通运输系统工程与信息, 2025, 25(2): 364-372.
[14]	翁剑成, 周慧缘, 张梦媛, 于江波. 考虑城市与群体异质的新能源车激励策略有效性研究[J]. 交通运输系统工程与信息, 2025, 25(1): 2-14.
[15]	万民, 匡海波, 贾鹏, 余方平, 马千里, 张益阁, 赵素娥. 考虑损耗的粮食装载方式与运输路径联合优化[J]. 交通运输系统工程与信息, 2025, 25(1): 15-23.