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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (8): 1659-1670    DOI: 10.3785/j.issn.1008-973X.2024.08.013
    
Empty-load charging strategy for autonomous vehicle parking based on multi-agent system
Wenhao LI1,2(),Yanjie JI1,*(),Hao WU3,Yewen JIA1,Shuichao ZHANG4
1. School of Transportation, Southeast University, Nanjing 211189, China
2. Department of Civil and Environmental Engineering, Nagoya University, Nagoya 464-8603, Japan
3. Key Laboratory of Traffic Information and Safety, Anhui Sanlian University, Hefei 230601, China
4. School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China
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Abstract  

A multi-agent parking simulation framework was constructed in order to formulate autonomous vehicle (AV) parking demand management strategies. Two charging strategies for empty-load driving were proposed: a static charge based on driving distance and a dynamic charge based on road congestion levels. Rate calculation method was analyzed. Cost functions for parking lots, residential parking, and continuous empty cruising were established under these charging policies. A logit model was used to describe the choice behavior under different parking modes. The simulation of urban mobility (SUMO) was used to conduct a large-scale road network simulation experiment in Nanning’s main urban area. AV parking behavior and road network operation under both strategies were analyzed. The simulation results showed that the empty-load driving mileage of AVs decreased by 20.16% and 10.85% under the static and dynamic charging strategies, respectively. Total vehicle delay decreased by 39.80% and 43.52%, respectively. The dynamic charging strategy was adjustable in real-time based on road conditions, and operational efficiency of the road network was significantly enhanced.

Key words： empty-load parking charge      autonomous vehicle      multi-agent simulation      SUMO     
Received: 21 September 2023      Published: 23 July 2024
CLC:  U 491  
Fund:  中央高校基本科研业务费专项资金资助项目（2242020K40063）；江苏省研究生科研与实践创新计划资助项目（KYCX20_0137）；浙江省自然科学基金资助项目（LTGG23E080005）.
Corresponding Authors: Yanjie JI     E-mail: liwenhao@seu.edu.cn;jiyanjie@seu.edu.cn
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Wenhao LI
Yanjie JI
Hao WU
Yewen JIA
Shuichao ZHANG
Cite this article:

Wenhao LI,Yanjie JI,Hao WU,Yewen JIA,Shuichao ZHANG. Empty-load charging strategy for autonomous vehicle parking based on multi-agent system. Journal of ZheJiang University (Engineering Science), 2024, 58(8): 1659-1670.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.08.013     OR     https://www.zjujournals.com/eng/Y2024/V58/I8/1659


基于多智体的自动驾驶汽车停车空载收费策略

为了制订自动驾驶车辆(AV)停车需求管理方案，搭建多智能体停车模拟框架，提出2种空载行驶收费策略：基于行驶距离的静态收费和基于道路拥堵水平的动态收费，研究费率计算方法. 建立空载行驶收费策略下停车场停车、居住地停车及持续空载巡航3种停车模式的成本函数，使用logit模型描述不同停车模式下的选择行为. 利用Simulation of urban mobility (SUMO)，以南宁市主城区为例开展大规模路网下的仿真实验，研究2种策略下的AV停车行为及路网运行状态变化. 仿真结果表明，静态收费策略和动态收费策略下的AV空载行驶里程分别减少了20.16%和10.85%，车辆总延误分别降低了39.80%和43.52%；动态收费策略能够灵活地根据路况变化进行实时调整，路网运行效率提升更显著.


关键词： 停车空载收费,  自动驾驶汽车,  多智能体模拟,  SUMO 
Fig.1 Simulation logic for AV parking empty-load charging
智能体种类功能属性
AV-出行者实现车辆微观行驶、停车模式选择、路径选择速度、加速度、车辆类型、活动时间、路径选择规则、停车模式决策规则
停车系统动态更新停车资源，完成停车位预订和分配停车场容量、车位预定信息、停车位价格、空闲停车位数量、停车位位置
RSU实现道路状态识别，控制车辆巡航静态信息（限速、车道数、长度）、动态信息（车辆数、车速、行程时间）
TMC更新和整合道路资源、停车资源，完成空载收费发布，
实现停车诱导		车辆信息、道路状态信息、停车场信息、空载收费策略
Tab.1 Characteristic of different type of agent in AV parking simulation model
Fig.2 Segmental growth function of dynamic empty-load charging strategy
Fig.3 Schematic of Nanning’s main urban area
Fig.4 Traffic demand generation in traffic subareas of study area (cars)
场景编号收费策略收费区域$ {\tau }_{G} $/（元·km?1）
1无收费（基础场景）——
2静态收费策略区域$ {G}_{1} $$ {\tau }_{{G}_{1}} $：1.98
区域$ {G}_{2} $$ {\tau }_{{G}_{2}} $：0.61
3动态收费策略区域$ {G}_{1} $最小值：0
最高值$ {3\tau }_{{G}_{1}} $：5.94
区域$ {G}_{2} $最小值：0
最高值$ {3\tau }_{{G}_{2}} $：1.83
Tab.2 Simulation scenario of AV parking behavior simulation model
参数数值
HVAV
加速度$ a $/（m·s?2）2.63.8
减速度$ {a}_{\mathrm{d}\mathrm{e}\mathrm{c}} $/（m·s?2）3.54.5
紧急情况下减速度$ {a}_{\mathrm{e}\mathrm{m}} $/（m·s?2）9.09
驾驶容差$ {T}_{\mathrm{t}\mathrm{o}\mathrm{l}} $0.50
最小跟车间隙$ {d}_{\mathrm{m}\mathrm{i}\mathrm{n}} $/m2.51.5
期望车头时距$ {t}_{\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{d}} $/s1.00.6
Tab.3 Parameter of car-following and lane-changing model
区域类别分段费率/（元·h?1）每日最高限价/元
一类区域前3小时：4
3小时后：6		50
二类区域330
三类区域215
Tab.4 Parking lot fee
活动目的ρres
$ {\lambda }_{\mathrm{s}\mathrm{u}\mathrm{b}\mathrm{s}}=0 $$ {\lambda }_{\mathrm{s}\mathrm{u}\mathrm{b}\mathrm{s}}=1 $
通勤30%70%
商务50%50%
生活50%50%
通学100%0
其他100%0
Tab.5 Reservation pick-up ratio
场景停车模式$ {r_{{\text{choice}}}} $/%$ {\bar C_{{\text{total}}}} $/元$ {\bar T_{{\text{activity}}}} $/h$ {\bar C_{{\text{per hour}}}} $/（元·h?1）$ {\bar D_{{\text{travel}}}} $/km
场景1停车场停车45.6922.225.923.7515.99
住宅停车54.0117.039.191.858.62
巡航替代停车0.3010.910.4027.2810.20
均值—19.387.672.5311.99
场景2停车场停车62.0025.616.703.8214.69
住宅停车37.8019.729.302.127.54
巡航代替停车0.2010.600.3728.6517.32
均值—23.357.673.0411.99
场景3停车场停车59.2724.296.613.6815.01
住宅停车40.5518.979.262.057.55
巡航代替停车0.1810.630.3827.9717.43
均值—22.117.672.8811.99
Tab.6 Characteristics of AV parking mode in different scenarios
Fig.5 Variation of AV empty-load driving mileage for different scenario
Fig.6 Variation of AV empty-load travel time for different scenario
Fig.7 Variation in traffic volume compared to no-charge scenario (scenario 1)
Fig.8 Comparison of relative speeds during morning peak across different scenario
道路类型场景2场景3
γmγvγdγmγvγd
高速公路?2.690.02?0.41?1.760.01?0.39
快速路?13.673.38?56.53?11.783.87?53.99
主干道?7.343.48?43.52?6.554.25?44.22
次干道?9.053.59?36.64?6.073.71?43.21
三级道路?5.132.41?27.62?4.393.06?32.81
居住区道路?6.462.44?31.44?6.271.45?43.15
Tab.7 Change in miles traveled, speed, and delay on different road types compared to no-charge scenario (scenario 1) %
Fig.9 Distribution of parking fees
Fig.10 Distribution of empty-load charging for AV parking
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