驾驶模拟技术在交通仿真参数标定中的应用研究

doi:10.16182/j.issn1004731x.joss.23-1549

系统仿真学报 ›› 2024, Vol. 36 ›› Issue (6): 1359-1368.doi: 10.16182/j.issn1004731x.joss.23-1549

驾驶模拟技术在交通仿真参数标定中的应用研究

刘诗昆¹^,²(), 唐易³, 刘永红¹^,²()

^1.中山大学智能工程学院, 广东深圳 518107
^2.广东省智能交通系统重点实验室, 广东深圳 518107
^3.深圳市城市交通规划设计研究中心股份有限公司, 广东深圳 518057

收稿日期:2023-12-20 修回日期:2024-01-03 出版日期:2024-06-28 发布日期:2024-06-19
通讯作者: 刘永红 E-mail:shikunliucsu@163.com;liuyh3@mail.sysu.edu.cn
第一作者简介:刘诗昆(1997-)，女，博士生，研究方向为交通仿真、交通安全、低碳交通。E-mail：shikunliucsu@163.com
基金资助:
国家自然科学基金(41975165);广东省科技计划(2023B1212060029)

Application of Driving Simulation Technology in Calibration of Traffic Simulation Parameters

Liu Shikun¹^,²(), Tang Yi³, Liu Yonghong¹^,²()

^1.School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
^2.Guangdong Provincial Key Laboratory of Intelligent Transportation System, Shenzhen 518107, China
^3.Shenzhen Urban Transport Planning Center Co. , Ltd, Shenzhen 518057, China

Received:2023-12-20 Revised:2024-01-03 Online:2024-06-28 Published:2024-06-19
Contact: Liu Yonghong E-mail:shikunliucsu@163.com;liuyh3@mail.sysu.edu.cn

摘要/Abstract

摘要：

为解决交通仿真建模过程因缺乏深层次考虑复杂驾驶行为而导致精度不足的问题，提出了基于驾驶模拟技术的交通仿真参数标定方法。以深圳市机荷高速改扩建工程作为研究案例，基于VISSIM构建案例全线交通仿真模型，应用UC-winRoad道路三维建模软件构建高逼真驾驶模拟场景，开展驾驶模拟实验提取复杂场景的典型驾驶行为特征，采用车头间距、行驶速度等跟车行为指标建立仿真参数标定函数，针对标定算法效能问题，通过改进的遗传粒子群算法寻优Wiedemann99跟驰模型的标定参数，以期提升微观交通仿真模型的分析精度与应用效果。结果表明：相比于正交试验与基于交通感知数据迭代寻优的标定方法，该标定方法求解参数标定值误差分别降低47.2%和9.1%，更加符合项目本地化驾驶行为特征，可显著提升交通仿真模型的可信度；相比常规遗传算法、粒子群算法，遗传粒子群算法不仅能找到全局最优解，而且收敛速度比遗传算法快17轮，能同时满足算法有效性与高效率的要求。

关键词: 交通仿真, 驾驶模拟, 参数标定, 遗传粒子群, 跟车行为

Abstract:

To address the insufficient accuracy in traffic simulation modeling due to the lack of in-depth consideration of complex driving behaviors, a calibration method for traffic simulation parameters based on driving simulation technology is proposed. The reconstruction and expansion project of Shenzhen Bao'an International Airport Expressway is selected as the case. Using VISSIM simulation software, a comprehensive traffic simulation model of the entire route is constructed, and UC-winRoad software is employed to create the highly realistic driving simulation scenarios. Driving simulation experiments are conducted to extract the typical driving behavior characteristics in complex scenarios. Calibration functions for simulation parameters are established by using the car-following behavior indicators such as headway and driving speed. To address the efficiency of the calibration algorithm, an improved genetic particle swarm optimization algorithm is employed to optimize the calibration parameters of Wiedemann99 car-following model to enhance the analytical precision and application effectiveness of the microscopic traffic simulation model. The results show that, compared to the orthogonal experiments and iterative optimization based on traffic perception data, the proposed calibration method based on driving simulation technology reduces the error in parameter calibration values by 47.2% and 9.1%, respectively, and aligns more closely with the localized driving behaviors, which significantly enhances the credibility of the traffic simulation model. Compared to the conventional genetic algorithms and particle swarm algorithms, the proposed genetic particle swarm algorithm can not only obtain the global optimum but also converge faster by 17 rounds, which can meet the effectiveness and efficiency requirements for the algorithm.

Key words: traffic simulation, driving simulation, parameter calibration, genetic & particle swarm, car following behavior

中图分类号:

TP391

刘诗昆,唐易,刘永红 . 驾驶模拟技术在交通仿真参数标定中的应用研究[J]. 系统仿真学报, 2024, 36(6): 1359-1368.

Liu Shikun,Tang Yi,Liu Yonghong . Application of Driving Simulation Technology in Calibration of Traffic Simulation Parameters[J]. Journal of System Simulation, 2024, 36(6): 1359-1368.

图/表 17

图1

图2

图3

图4

图5

图6

图7

图8

表1

图9

图10

图11

表2

图12

图13

图14

图15

参考文献 17

1	伍朝辉, 郭瑜, 王辉, 等. 虚拟现实交通运输应用研究综述[J]. 系统仿真学报, 2016, 28(10): 2289-2297, 2303.
	Wu Zhaohui, Guo Yu, Wang Hui, et al. Virtual Reality in Transportation: A Survey[J]. Journal of System Simulation, 2016, 28(10): 2289-2297, 2303.
2	Vision PTV. PTV VISSIM 9 User Manual[M]. Germany: Planung Transport Verkeher AG, 2016.
3	Company Caliper. Trans Modeler User's Guide 6.0[M]. [S.l.]: Caliper Company, 2021.
4	周晨静, 高亚聪, 荣建. 微观交通仿真模型参数标定方法改善研究[J]. 系统仿真学报, 2020, 32(11): 2112-2120.
	Zhou Chenjing, Gao Yacong, Rong Jian. Research on Improvement of Parameters Calibration Method of Microscopic Traffic Simulation Model[J]. Journal of System Simulation, 2020, 32(11): 2112-2120.
5	马文欣, 李瑞敏. 检测数据驱动的交通仿真参数标定方法研究[J]. 系统仿真学报, 2021, 33(12): 2808-2819.
	Ma Wenxin, Li Ruimin. Research on Detection Data Driven Calibration Method of Traffic Simulation Parameters[J]. Journal of System Simulation, 2021, 33(12): 2808-2819.
6	何兆成, 林炫华, 聂佩林, 等. 考虑展宽设计的中观交通仿真模型及其标定[J]. 系统仿真学报, 2021, 33(4): 781-791.
	He Zhaocheng, Lin Xuanhua, Nie Peilin, et al. Mesoscopic Traffic Simulation Model and Calibration Considering Stretching-segment Design[J]. Journal of System Simulation, 2021, 33(4): 781-791.
7	李振龙, 王保菊, 金雪, 等. 针对主辅路的Vissim仿真模型参数标定方法[J]. 交通信息与安全, 2015, 33(2): 45-50.
	Li Zhenlong, Wang Baoju, Jin Xue, et al. Parameter Calibration of Vissim Simulation Models with a Focus on Main and Side Roads[J]. Journal of Transport Information and Safety, 2015, 33(2): 45-50.
8	杨艳芳, 秦勇, 努尔兰·木汉. 基于SOGA的VISSIM仿真模型参数标定方法[J]. 交通运输系统工程与信息, 2017, 17(3): 91-97.
	Yang Yanfang, Qin Yong, Nuerlan Muhan. VISSIM Model Calibration Based on SOGA[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(3): 91-97.
9	张彦宁, 郭忠印, 孙智. 驾驶模拟环境中激进驾驶行为的影响因素分析[J]. 中国公路学报, 2020, 33(6): 129-136.
	Zhang Yanning, Guo Zhongyin, Sun Zhi. Influencing Factors of Aggressive Driving Behavior in Driving Simulation Environment[J]. China Journal of Highway and Transport, 2020, 33(6): 129-136.
10	张静, 谢练, 梁新, 等. 驾驶模拟条件下高速公路线形指标对驾驶绩效影响特征分析[J]. 交通信息与安全, 2015, 33(6): 102-107.
	Zhang Jing, Xie Lian, Liang Xin, et al. Analysis of Effects of Highway Alignments on Driver′s Performance Based on Simulated Driving Experiments[J]. Journal of Transport Information and Safety, 2015, 33(6): 102-107.
11	张驰, 魏东东, 兰富安, 等. 基于驾驶模拟技术的道路行车安全性研究综述[J]. 交通信息与安全, 2022, 40(4): 1-12, 25.
	Zhang Chi, Wei Dongdong, Lan Fuan, et al. A Review on Road Driving Safety Based on Driving Simulation Technologies[J]. Journal of Transport Information and Safety, 2022, 40(4): 1-12, 25.
12	深圳市交通运输委员会, 深圳市城市交通规划设计研究中心股份有限公司. 深圳市高快速路网优化及地下快速路布局规划[R]. 深圳: 深圳市交通运输委员会, 深圳市城市交通规划设计研究中心股份有限公司, 2018: 125-127.
	Transport Commission of Shenzhen Municipality, Shenzhen Urban Transport Planning Center Co., Ltd. Optimization of Expressway Network and Layout Planning of Underground Expressway in Shenzhen[R]. Shenzhen: Transport Commission of Shenzhen Municipality, Shenzhen Urban Transport Planning Center Co., Ltd, 2018: 125-127.
13	Sajjadi S, Kondyli A. Macroscopic and Microscopic Analyses of Managed Lanes on Freeway Facilities in South Florida[J]. Journal of Traffic and Transportation Engineering(English Edition), 2017, 4(1): 61-70.
14	Sergeeva Marina, Delahaye Daniel, Mancel Catherine, et al. Dynamic Airspace Configuration by Genetic Algorithm[J]. Journal of Traffic and Transportation Engineering(English Edition), 2017, 4(3): 300-314.
15	Yuan Qing, Zhai Shihong, Wu Li, et al. Blasting Vibration Velocity Prediction Based on Least Squares Support Vector Machine with Particle Swarm Optimization Algorithm[J]. Geosystem Engineering, 2019, 22(5): 279-288.
16	邢广成, 石磊. 基于改进粒子群算法的单交叉口信号配时仿真[J]. 计算机仿真, 2012, 29(5): 348-351.
	Xing Guangcheng, Shi Lei. Simulation of Signal Timing for Single Intersection Based on Improved-PSO Algorithm[J]. Computer Simulation, 2012, 29(5): 348-351.
17	Li Li, Chen Xiqun, Zhang Lei. A Global Optimization Algorithm for Trajectory Data Based Car-following Model Calibration[J]. Transportation Research Part C: Emerging Technologies, 2016, 68: 311-332.

序号	参数	名称单位	默认值	范围
1	CC0	平均停车间距/m	1.5	0.5 ~ 2.5
2	CC1	期望车头时距/s	0.9	0.5 ~ 2.0
3	CC2	跟车随机振荡距离/m	4	2 ~ 8
4	CC3	进入跟车状态阈值/s	-8	-10 ~ -2
5	CC4	消极跟车状态的阈值/(m/s)	-0.35	-1.05 ~ -0.05
6	CC5	积极跟车状态的阈值/(m/s)	0.35	0.05 ~ 1.05
7	CC6	车速振动/(1/m·s^-1)	11.44	0 ~ 20
8	CC7	振动加速度/(m/s²)	0.25	0 ~ 2
9	CC8	停车加速度/(m/s²)	3.5	2.0 ~ 3.5
10	CC9	80 km/h时加速度/(m/s²)	1.5	0.5 ~ 2.0

序号	参数	默认值	标定值
1	CC0	1.50	1.50
2	CC1	0.90	1.95
3	CC3	-8.00	-9.60
4	CC4	-0.35	-0.45
5	CC7	0.25	0.65

[1]	邓明君, 胡辛瑕, 李响, 徐丽萍. 基于车速引导和感应控制的干线协调优化方法[J]. 系统仿真学报, 2024, 36(6): 1309-1321.
[2]	丘建栋, 唐易, 暨育雄, 刘恒, 罗钧韶. 高速公路立体复合扩容设计的仿真模型研究[J]. 系统仿真学报, 2024, 36(5): 1072-1080.
[3]	刘博, 林建新, 刘依妮, 张栋. 面向碳减排的城市交通微观仿真模型优化研究[J]. 系统仿真学报, 2024, 36(4): 859-872.
[4]	庞明宝, 刘震. 基于Petri网立交桥智能网联车协作控制仿真[J]. 系统仿真学报, 2023, 35(3): 484-493.
[5]	马庆禄, 张琳, 袁新新, 刘丰杰. 城市道路交通多状态演化下的连续仿真技术研究[J]. 系统仿真学报, 2022, 34(4): 847-855.
[6]	奇格奇, 刘思劲, 何一康, 王猛, 黄爱玲. 基于仿真驱动的高速公路主动限速效用评价与推荐[J]. 系统仿真学报, 2022, 34(12): 2522-2534.
[7]	朱依婷, 闫云, 何兆成. 公交与社会车辆混合的中观交通建模与仿真[J]. 系统仿真学报, 2022, 34(09): 2019-2027.
[8]	胡明伟, 张芷铭, 陈湘生. 智能网联车辆混行交通流效益研究[J]. 系统仿真学报, 2021, 33(9): 2270-2278.
[9]	何兆成, 林炫华, 聂佩林, 张荣辉. 考虑展宽设计的中观交通仿真模型及其标定[J]. 系统仿真学报, 2021, 33(4): 781-791.
[10]	马文欣, 李瑞敏. 检测数据驱动的交通仿真参数标定方法研究[J]. 系统仿真学报, 2021, 33(12): 2808-2819.
[11]	赵丹, 牛学军, 张书豪, 魏佳旭. 平面交叉口借道左转设置研究与效果评估[J]. 系统仿真学报, 2021, 33(12): 2975-2982.
[12]	周晨静, 高亚聪, 荣建. 微观交通仿真模型参数标定方法改善研究[J]. 系统仿真学报, 2020, 32(11): 2112-2120.
[13]	段宇洲, 柴婷婷, 简洁. 环形交叉口内部运行时间可靠度研究[J]. 系统仿真学报, 2019, 31(3): 438-447.
[14]	杜怡曼, 苏红帆, 陈宇, 刘龙飞, 吴建平. 南宁市滨湖路绿波方案仿真评估[J]. 系统仿真学报, 2019, 31(3): 585-591.
[15]	杜怡曼, 苏红帆, 吴建平, 陈宇, 黄琳, 刘龙飞. 南宁市中心区域交通动态调控技术[J]. 系统仿真学报, 2019, 31(2): 324-331.

驾驶模拟技术在交通仿真参数标定中的应用研究

Application of Driving Simulation Technology in Calibration of Traffic Simulation Parameters

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 17

相关文章 15

编辑推荐

Metrics

本文评价