基于动态清空距离的特殊车辆与CAVs混合车道控制

赵欣; 庞明宝

doi:10.3963/j.jssn.1674-4861.2022.03.012

基于动态清空距离的特殊车辆与CAVs混合车道控制

doi: 10.3963/j.jssn.1674-4861.2022.03.012

赵欣,
庞明宝^,

河北工业大学土木与交通学院天津 300400

基金项目:

国家自然科学基金项目 50478088

河北省自然科学基金项目 E2022202023

河北省高等学校科学技术研究项目 ZD2021028

详细信息

作者简介:
赵欣（1996—），硕士研究生. 研究方向：交通运输规划与管理. E-mail：zhaoxincsc@163.com

通讯作者:
庞明宝（1966—），博士，教授. 研究方向：交通管理与控制、智能交通运输系统、交通安全等. E-mail：pmbpgy@sina.com

中图分类号: U491
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出版历程
- 收稿日期: 2022-02-06
- 网络出版日期: 2022-07-25

A Control method of Dedicated Lanes for Mixed Use of Special Vehicles and CAVs Based on Dynamic Clear Distance

ZHAO Xin,
PANG Mingbao^,

School of Civil and Transportation, Hebei University of Technology, Tianjin 300401, China

摘要

摘要: 特殊车辆的优先通行是道路交通管理的一项重要工作，而目前相关控制措施存在实施难度较大、道路空间利用率低和道路通行能力下降等问题。为解决这些问题，结合智能网联汽车（CAVs）技术特点，提出考虑特殊车辆优先通行的CAVs专用车道控制方法，按应急车辆、一般优先级车辆和CAVs的优先通行顺序设计车辆通行规则。通过预测特殊车辆到达下游交叉口时的路口排队长度，建立“满足不同优先级特殊车辆通行需求”的动态清空距离模型，其中应急车辆以速度损失最小化为优化目标，一般优先级车辆以均衡车辆通行需求为优化目标。针对CAVs在专用道上可能成为其他车辆通行障碍的情况，考虑换道安全和不同换道动机，设计CAVs进入和离开专用道的规则，建立换道决策控制模型；在此基础上，提出适用于不同优先级车辆的专用车道通行控制策略。通过仿真实验对所提方法的控制效果予以分析验证。实验结果表明：与不考虑特殊车辆优先通行的控制方法相比，虽然该方法的车均出行时间和人均出行时间分别增加了3.9%和2.8%，但特殊车辆的车均延误时间减少了59.6%以上；与IBL控制方法相比，该方法的车均出行时间和人均出行时间分别减少16.7%和14.6%，特殊车辆的车均延误时间减少13.5%，专用车道利用率提高36.3%以上，并且在CAVs渗透率大于0.4时获得最佳控制效果。该控制方法在特殊车辆优先通行方面，减少了单一控制策略的局限性，为交通控制和管理提供理论支撑。
- 智能交通 /
- 动态清空距离 /
- 车道控制 /
- 特殊车辆优先 /
- 智能网联汽车
Abstract: Providing road priority to special vehicles is one of important tasks of traffic management and operation authorities. However, traditional control measures for providing road priority to special vehicle are difficult to implement, and they also tend to significantly reduce road capacity for other traffic. Therefore, a control method of dedicated lane for mixed use of special vehicles and connected automated vehicles(CAVs), is proposed to solve the above problems. First, the access rules for the dedicated lanes in the order of emergency vehicles, vehicles with secondary priority and CAVs are designed. By predicting the queue length when special vehicles arrive at a downstream intersection, the state of special vehicles at the intersection is obtained, and a dynamic clear distance model is developed to meet the demand of special vehicles with different priorities. In this model, the objective function is to minimize the speed reduction of emergency vehicles, and to balance the road capacity with traffic demand of the vehicles with secondary priority. The rules for CAVs to enter and leave the dedicated lanes are designed, and a lane-changing control model is established to solve the problem that CAVs may become obstacles to other vehicles at dedicated lanes. With the above, a dedicated lane control strategy suitable for different types of vehicles with different priority is proposed. The effectiveness of the model is validated through a set of simulated experiments. Study results show that, compared with the control methods without consideration of the priority of special vehicles, the average travel time and per capita travel time are increased by 3.9% and 2.8% respectively, but the average vehicle delay of special vehicles is reduced by more than 59.6%. Compared with the intermittent bus lane control method, the average vehicle travel time and per capita travel time are reduced by 16.7% and 14.6% respectively, and the average delay of special vehicles is reduced by 13.5% and the use rate of special lanes is increased by more than 36.3%. The best outcomes can be obtained when the CAVs penetration rate is greater than 40%. The proposed method removes some of the limitations of traditional lane control strategies when providing road priority to special vehicles, and therefore, provides"new"theoretical contribution for traffic control and management.
- intelligent transportation /
- dynamic clear distance /
- lane control /
- priority of special vehicles /
- Connected and Automated Vehicles

HTML全文

图 1 动态清空距离示意图

Figure 1. Dynamic clear distance diagram

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图 2 特殊车辆到达交叉口状态

Figure 2. Arrival status of special vehicles at intersection

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图 3 混合车道动态控制流程图

Figure 3. Dynamic control flow chart of hybrid lane

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图 4 实验区域示意图

Figure 4. Schematic of the experimental area

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图 5 特殊车辆车均延误时间

Figure 5. Average vehicle delay time of special vehicles

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图 6 整体车均出行时间

Figure 6. Average vehicle travel time

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图 7 整体人均出行时间

Figure 7. Per capita travel time

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图 8 专用车道交通量

Figure 8. Traffic flow of the dedicated lane

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图 9 普通车道交通量

Figure 9. Traffic flow of the normal road lane

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图 10 道路整体车均延误时间

Figure 10. The average vehicle delay time

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参考文献(27)

[1]	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.
[2]	赵韩涛, 马国胜, 毛宏燕. 基于元胞自动机模型的应急车辆行程时间[J]. 长安大学学报(自然科学), 2015, 35(2): 132-137+144. https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL201502020.htm ZHAO H T, MA G S, MAO H Y. Emergency vehicle travel time based on cellular automaton model[J]. Journal of Chang'an University(Natural Science Edition), 2015, 35 (2): 132-137+144. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XAGL201502020.htm
[3]	赵晨馨, 董红召, 郝伟娜. 公交时分复用车道设置条件及交通临界模型[J]. 浙江大学学报(工学版), 2021, 55(4): 704-712. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202104012.htm ZHAO C X, DONG H Z, HAO W N. Setting condition and traffic critical model of bus lane with time-division multiplexing[J]. Journal of Zhejiang University(Engineering Science), 2021, 55(4): 704-712. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC202104012.htm
[4]	BASSO L J, GUEVANA C A, GSCHWENDER A, et al. Congestion pricing, transit subsidies and dedicated bus lanes: Efficient and practical solutions to congestion[J]. Transport Policy, 2011, 18(5): 676-684. doi: 10.1016/j.tranpol.2011.01.002
[5]	TOY C, LEUNG K, ALVAREZ L, et al. Emergency vehicle maneuvers and control laws for automated highway systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2002, 3(2): 109-119. doi: 10.1109/TITS.2002.801422
[6]	杨阳, 刘强, 石英杰. 高速公路饱和路段动态应急车道开放决策模型研究[J/OL]. (2021-04-12)[2022-04-16]. http://kns.cnki.net/kcms/detail/43.1481.U.20210409.1620.008.html. YANG Y, LIU Q, SHI Y J. Study on decision model of dynamic hard shoulder running for highways with saturated traffic volume[J/OL]. (2021-04-12)[2022-04-16]. http://kns.cnki.net/kcms/detail/43.1481.U.20210409.1620.008.html. (in Chinese)
[7]	HUANG D, XING J, LIU Z, et al. A multi-stage stochastic optimization approach to the stop-skipping and bus lane reservation schemes[J]. Transportmetrica A: Transport Science, 2021, 17(4): 1272-1304. doi: 10.1080/23249935.2020.1858206
[8]	OSMAN R A, ZAKI A I, ABDELSALAM A K. Novel road traffic management strategy for rapid clarification of the emergency vehicle route based on V2V communications[J]. Sensors, 2021, 21(15): 5120. doi: 10.3390/s21155120
[9]	GHIASI A, HUSSAIN O, QIAN Z S, et al. A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method[J]. Transportation Research Part B: Methodological, 2017(106): 266-292.
[10]	XIE Z, JIN H, TENG J, et al. Design and management of multi-functional exclusive lane for the integrated service to various vehicles with priority[J]. KSCE Journal of Civil Engineering, 2022, 26(2): 882-892. doi: 10.1007/s12205-021-5786-8
[11]	LITMAN T. Autonomous vehicle implementation predictions[M]. Victoria, Canada: Victoria Transport Policy Institute, 2017.
[12]	庞明宝, 柴紫欣, 巩丹阳. 混合交通下智能网联车借道公交专用车道控制[J]. 交通运输系统工程与信息, 2021, 21(4): 118-124. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202104014.htm PANG M B, CHAI Z X, GONG D Y. Control of connected and automated vehicles driving on dedicated bus lane under mixed traffic[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(4): 118-124. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202104014.htm
[13]	赵鑫. 基于元胞自动机的联网车辆专用车道设置研究[D]. 哈尔滨: 哈尔滨工业大学, 2020. ZHAO X. Research on dedicated lane setting of internet of vehicles based on cellular automata[D]. Harbin: Harbin Institute of Technology, 2020. (in Chinese)
[14]	WINSOR M. Influence of networked and cooperative vehicles on virtual right-of-way performance in mixed traffic[D]. Munich: Technical University of Munich, 2020.
[15]	XIAO L, WANG M, AREM B. Traffic flow impacts of converting an HOV lane into a dedicated CACC lane on a freeway corridor[J]. IEEE Intelligent Transportation Systems Magazine, 2019, 12(1): 60-73.
[16]	刘悠冉. 自动驾驶汽车借道BRT车道行驶的干线协调效率研究[D]. 北京: 清华大学, 2019. LIU Y R. Research on the coordination control efficiency of autonomous vehicles driving on the BRT Lanes[D]. Beijing: Tsinghua University, 2019. (in Chinese)
[17]	LEVIN M W, KHANI A. Dynamic transit lanes for connected and autonomous vehicles[J]. Public Transport, 2018, 10 (3): 399-426. doi: 10.1007/s12469-018-0186-2
[18]	WU D, DENG W, SONG Y, et al. Evaluating operational effects of bus lane with intermittent priority under connected vehicle environments[J]. Discrete Dynamics in Nature and Society, 2017, 2017: 1659176.
[19]	ZYRYANOV V, MIRONCHUK A. Simulation study of intermittent bus lane and bus signal priority strategy[J]. Procedia-Social and Behavioral Sciences, 2012(48): 1464-1471.
[20]	WU W, HEAD L, YAN S, et al. Development and evaluation of bus lanes with intermittent and dynamic priority in connected vehicle environment[J]. Journal of Intelligent Transportation Systems, 2018, 22(4): 301-310. doi: 10.1080/15472450.2017.1313704
[21]	MA C, XU X D. Providing spatial-temporal priority control strategy for BRT lanes: A simulation approach[J]. Journal of Transportation Engineering Part A: Systems, 2020, 146(7): 04020060. doi: 10.1061/JTEPBS.0000385
[22]	RAU A, TIAN L, JAIN M, et al. Dynamic autonomous road transit (DART) for use-case capacity more than bus[J]. Transportation Research Procedia, 2019(41): 812-823.
[23]	XIE M, RAMANATHAN S, RAU A, et al. Design and evaluation of V2X-based dynamic bus lanes[J]. IEEE Access, 2021 (9): 136094-136104.
[24]	宋现敏, 张明业, 李振建, 等. 动态公交专用道的设置及其仿真分析评价[J]. 吉林大学学报(工学版), 2020, 50(5): 1677-1686. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202005016.htm SONG X M, ZHANG M Y, LI Z J, et al. Setting of dynamic bus lane and its simulation analysis and evaluation[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1677-1686. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202005016.htm
[25]	CHOWDHURY D, SANTEN L, SCHADSCHNEIDER A. Statistical physics of vehicular traffic and some related systems[J]. Physics Reports, 2000, 329(4-6): 199-329. doi: 10.1016/S0370-1573(99)00117-9
[26]	NIE J, ZHANG J, DING W, et al. Decentralized cooperative lane-changing decision-making for connected autonomous vehicles[J]. IEEE Access, 2016(4): 9413-9420.
[27]	WANG Z, ZHAO X, CHEN Z, et al. A dynamic cooperative lane-changing model for connected and autonomous vehicles with possible accelerations of a preceding vehicle[J]. Expert Systems with Applications, 2021(173): 114675.