Construction and Application of Cellular Automaton Model of Traffic Flow in Freeway Diverging and Merging Areas

doi:10.12141/j.issn.1000-565X.220008

Abstract

Abstract:

The operation and management optimization schemes of freeway always need to be formulated according to the traffic capacity of bottleneck sections. Due to the complexity of the traffic flow characteristics of the bottleneck sections of freeway, the current general calculation formulas of the traffic capacity are limited to many assumptions, and the accuracy is not high and the error is large. This paper attempted to construct a calculation method of traffic capacity in freeway bottleneck sections and verify it by actual measurement. Firstly, UAV technology and software Tracker were used for aerial video recording and dynamic identification and 680 groups of traffic flow data for car-following and lane-changing on freeway was obtained, including position, speed, acceleration, headway and other parameters. Then, the probability models and rule models of lane-changing considering driver characteristics were established according to the obtained traffic flow parameters, and the classical car-following model GHR was calibrated. Finally, a simulation application model of freeway traffic flow based on cellular automaton theory was constructed by the partitioning method, the acceleration requirements of following vehicles were characterized with GHR model, and the effectiveness of the model was verified by the indicators of vehicle lane-changing times and hourly traffic volume with error rates of 12.06% and 3.19%, respectively. The results show that this model can effectively calculate the capacity of freeway diverging and merging areas and design the road parameters such as the length of speed-change lane. In this case, a traffic flow simulation test was conducted on Cencun interchange sections of Guangzhou Northring Freeway, in which the road geometry characteristics, vehicle arrival conditions and traffic flow operation mechanism were simulated. The capacity of the diverging and merging areas is 5 456 and 5 253 pcu/h, respectively, and the optimization design values for the length of speed-change lanes in the diverging and merging areas are 125 and 200 m, respectively. The cellular automaton model of traffic flow constructed in this paper provides scientific basis for microscopic traffic flow simulation and road parameters design of freeway diverging and merging areas, and helps to improve the level of service and operational quality.

Key words: freeway, diverging and merging areas, cellular model, traffic flow simulation, capacity

CLC Number:

U491.4

QI Weiwei, MA Siwei, ZHOU Nanjie . Construction and Application of Cellular Automaton Model of Traffic Flow in Freeway Diverging and Merging Areas[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(10): 11-18.

Figures/Tables 7

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References 18

1	KWON J W， CHWA D ．Adaptive bidirectional platoon control using a coupled sliding mode control method ［J］．IEEE Transactions on Intelligent Transportation Systems，2014，15（5）：2040-2048.
2	ALI Y， ZHENG Z， HAQUE M M，et al ．A game theory-based approach for modelling mandatory lane-changing behaviour in a connected environment ［J］．Transportation Research Part C：Emerging Technologies，2019，106：220-242.
3	刘伟铭，邓如丰，张阳，等．高速公路出匝分流区超车道车辆车道变换模型［J］．公路交通科技，2012，29（8）：106-111.
	LIU Wei-ming， DENG Ru-feng， ZHANG Yang，et al ．Vehicle lane-changing model for overtaking lane in freeway off-ramp diverging area ［J］．Journal of Highway and Transportation Research and Development，2012，29（8）：106-111.
4	许伦辉，胡三根，罗强，等．基于驾驶员类型的车辆换道模型［J］．华南理工大学学报（自然科学版），2014，42（8）：104-111.
	XU Lun-hui， HU San-gen， LUO Qiang，et al ．Lane-changing model based on different type of drivers ［J］．Journal of South China University of Technology（Natural Science Edition），2014，42（8）：104-111.
5	钟异莹，陈坚，邵毅明，等．强制性换道的空间特征对分流区交通流的影响［J］．交通运输系统工程与信息，2020，20（5）：114-120.
	ZHONG Yi-ying， CHEN Jian， SHAO Yi-ming，et al ．Spatial feature of mandatory lane changing and its impact on traffic flow at diverging area ［J］．Journal of Transportation Systems Engineering and Information Technology，2020，20（5）：114-120.
6	贾洪飞，谭云龙，李强，等．考虑驾驶员特征的快速路合流区间隙接受模型构建［J］．吉林大学学报（工学版），2015，45（1）：55-61.
	JIA Hong-fei， TAN Yun-long， LI Qiang，et al ．Gap acceptance model of expressway weaving area based on driver characteristics ［J］．Journal of Jilin University（Engineering and Technology Edition），2015，45（1）：55-61.
7	ZHOU B， WANG Y， YU G，et al ．A lane-change trajectory model from drivers’vision view ［J］．Transportation Research Part C：Emerging Technologies．2017，85：609-627.
8	CHONG L， ABBAS M M， MEDINA FLINTSCH A，et al ．A rule-based neural network approach to model driver naturalistic behavior in traffic ［J］．Transportation Research Part C：Emerging Technologies，2013，32：207-223.
9	杨晓芳，郭倩，付强．基于车车通信合流影响区外侧车辆决策模型［J］．系统仿真学报，2015，27（5）：1112-1119.
	YANG Xiao-fang， GUO Qian， FU Qiang ．Decision strategy models of merge influence area for outside vehicles based on vehicle-vehicle communication［J］．Journal of System Simulation，2015，27（5）：1112-1119.
10	胡笳，安连华，李欣．面向新型混合交通流的快速路合流区通行能力建模［J］．交通信息与安全，2021，39（1）：137-144.
	HU Jia， AN Lian-hua， LI Xin ．A capacity model of freeway merging areas with partially connected automated traffic ［J］．Journal of Transport Information and Safety，2021，39（1）：137-144.
11	PAN S， WANG Y， WANG K ．A game theory-based model predictive controller for mandatory lane change of multiple vehicles ［C］∥Proceedings of the 2020 4th International Conference on Vehicular Control and Intelligence（CVCI）．Hangzhou：Institute of Electrical and Electronics Engineers Inc.，2020：731-736.
12	CHEN T， WONG Y D， SHI X，et al ．A data-driven feature learning approach based on Copula-Bayesian Network and its application in comparative investigation on risky lane-changing and car-following maneuvers ［J］．Accident Analysis & Prevention，2021，154：106061-1-23.
13	贾若，戴昇宏，黄霓，等．交通拥堵判别方法研究综述［J］．华南理工大学学报（自然科学版），2021，49（4）：124-139.
	JIA Ruo， DAI Shenghong， HUANG Ni，et al ．Literature review on traffic congestion identification methods ［J］．Journal of South China University of Technology（Natural Science Edition），2021，49（4）：124-139.
14	YE L， YAMAMOTO T ．Modeling connected and autonomous vehicles in heterogeneous traffic flow ‍［J］．Physica A：Statistical Mechanics and ITS Applications，2018，490：269-277.
15	ZHU M， WANG X， TARKO A，et al ．Modeling car-following behavior on urban expressways in Shanghai：A naturalistic driving study ［J］．Transportation Research Part C：Emerging Technologies，2018，93：425-445.
16	彭博，王玉婷，谢济铭，等．城市干线短交织区元胞自动机多级换道决策模型［J］．交通运输系统工程与信息，2020，20（4）：41-48.
	PENG Bo， WANG Yu-ting， XIE Ji-ming，et al ．Multi-stage lane-changing decision model for urban trunk road’s short weaving area based on cellular automata ［J］．Journal of Transportation Systems Engineering and Information Technology，2020，20（4）：41-48.
17	刘敬华，吉文超，李帅．基于驾驶行为的城市道路车辆主观换道模型研究［J］．城市道桥与防洪，2021（7）：232-237.
	LIU Jing-hua， JI Wen-chao， LI Shuai ．Study on subjective lane changing model of urban road vehicles based on driving behavior ［J］．Urban Roads Bridges & Flood Control，2021（7）：232-237.
18	公路路线设计规范：［S］.

区域	换道类型	车均换道次数^1）		准确率/%
区域	换道类型	实际观测值	元胞仿真值	准确率/%
分流区	强制性换道	1.261	1.367	92.24
分流区	选择性换道	0.142	0.176	80.68
合流区	强制性换道	1.000	1.000	100.00
合流区	选择性换道	0.116	0.147	78.90

区域	车道类型	进口道驶入流率			出口道驶出流率
区域	车道类型	元胞仿真值/（辆·h^-1）	实际观测值/（辆·h^-1）	误差率/%	元胞仿真值/（辆·h^-1）	实际观测值/（辆·h^-1）	误差率/%
分流区	内侧车道	1 528	1 520	0.53	1 530	1 475	3.73
	中间车道	1 464	1 459	0.34	1 175	1 142	2.89
	外侧车道	1 025	1 011	1.38	860	936	8.12
	匝道	—	—	—	452	437	3.43
合流区	内侧车道	1 519	1 553	2.19	1 487	1 608	7.52
	中间车道	1 648	1 614	2.11	1 663	1 669	0.36
	外侧车道	796	847	6.02	1 156	1 138	1.58
	匝道	383	401	4.49	—	—	—

分流影响区			合流影响区
变速车道长度/m	渐变段长度/m	通行能力/（pcu·h^-1）	变速车道长度/m	渐变段长度/m	通行能力/（pcu·h^-1）
95	70	3 317	155	60	3 273
110	80	3 403	180	70	3 324
125	90	3 556	200	80	3 442
145	100	3 642	230	90	3 476

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[8]	GUO Miao, ZHAO Xiaohua, YAO Ying, et al. Study on Accident Risk Based on Driving Behavior and Traffic Operating Status [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(9): 29-38.
[9]	KANG Lan, CHEN Zonglin, LIN Yiwei . Axial Compression Behavior of High-Strength Circular Steel Tube Confined High-Strength Steel Reinforced Concrete Short Columns [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(7): 1-12.
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[11]	LU Yu, GU Zhuhao, LIU Shewen, et al. Optimization Design of Bow Ice-Breaking Capability Based on Actual Ice Condition [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(2): 50-57.
[12]	XIONG Ergang, ZU Kun, HU Qinbin, et al. Shear Capacity Prediction for RC Beams Without Stirrups Based on Mechanical Research [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(11): 115-124.
[13]	XU Xilin, ZHONG Shuying, ZHOU Xiaoli, et al. Safety Evaluation and Probiotic Characteristics of Enterococcus faecalis EF-ZA1107-06 [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(11): 52-61.
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