Application of Driving Simulation Technology in Calibration of Traffic Simulation Parameters

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

Abstract

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

CLC Number:

TP391

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.

Figures/Tables 17

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Table 1

Fig. 9

Fig. 10

Fig. 11

Table 2

Fig. 12

Fig. 13

Fig. 14

Fig. 15

References 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]	Deng Mingjun, Hu Xinxia, Li Xiang, Xu Liping. Arterial Coordination Optimization Method Based on Vehicle Speed Guidance and Inductive Control [J]. Journal of System Simulation, 2024, 36(6): 1309-1321.
[2]	Qiu Jiandong, Tang Yi, Ji Yuxiong, Liu Heng, Luo Junshao. Research on Simulation Model of Double-layer Expansion Design of Expressway [J]. Journal of System Simulation, 2024, 36(5): 1072-1080.
[3]	Liu Bo, Lin Jianxin, Liu Yini, Zhang Dong. Optimization of Urban Traffic Microsimulation Model for Carbon Emission Reduction [J]. Journal of System Simulation, 2024, 36(4): 859-872.
[4]	Mingbao Pang, Zhen Liu. Simulation on Cooperative Control of Connected and Automated Vehicles at Interchange Based on Petri Net [J]. Journal of System Simulation, 2023, 35(3): 484-493.
[5]	Jianlei Liu, Xuejian Jiao, Huaiqian Wang. Development of Vehicle Dynamics Virtual Simulation System Based on CarSim [J]. Journal of System Simulation, 2022, 34(8): 1847-1854.
[6]	Qinglu Ma, Lin Zhang, Xinxin Yuan, Fengjie Liu. Continuous Simulation Technology for Multi-State Evolution of Urban Road Traffic [J]. Journal of System Simulation, 2022, 34(4): 847-855.
[7]	Geqi Qi, Sijin Liu, Yikang He, Meng Wang, Ailing Huang. Simulation-driven Based Utility Evaluation and Recommendation of Expressway Proactive Speed Limit [J]. Journal of System Simulation, 2022, 34(12): 2522-2534.
[8]	Yiting Zhu, Yun Yan, Zhaocheng He. Mesoscopic Modeling and Simulation of Mixed Traffic Flow of Buses and Vehicles [J]. Journal of System Simulation, 2022, 34(09): 2019-2027.
[9]	Hu Mingwei, Zhang Zhiming, Chen Xiangsheng. Research on Benefits of Mixed Traffic Flow of Intelligent Connected Vehicles [J]. Journal of System Simulation, 2021, 33(9): 2270-2278.
[10]	He Zhaocheng, Lin Xuanhua, Nie Peilin, Zhang Ronghui. Mesoscopic Traffic Simulation Model and Calibration Considering Stretching-segment Design [J]. Journal of System Simulation, 2021, 33(4): 781-791.
[11]	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.
[12]	Zhao Dan, Niu Xuejun, Zhang Shuhao, Wei Jiaxu. Study and Effect Evaluation on the Setting of Contraflow Left-Turn at Intersections [J]. Journal of System Simulation, 2021, 33(12): 2975-2982.
[13]	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.
[14]	Duan Yuzhou, Chai Tingting, Jian Jie. Travel Time Reliability of Inside Area of Roundabouts [J]. Journal of System Simulation, 2019, 31(3): 438-447.
[15]	Zhou Chenjing, Rong Jian, Kwok Lam For. Research on the Value Accessing Method for Calibrating Micro Traffic Simulation Model Parameters [J]. Journal of System Simulation, 2019, 31(12): 2802-2809.