Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (6): 1948-1962.doi: 10.13229/j.cnki.jdxbgxb.20221102

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Strategies for controlling vehicle movements at signal⁃free intersections in intelligent networked environment

Fu-quan PAN1(),Yuan-zheng NIU1,Li-xia ZHANG2,Jin-shun YANG1,Xiu-feng CHEN1,De-qi CHEN1   

  1. 1.School of Civil Engineering，Qingdao University of Technology，Qingdao 266520，China
    2.School of Mechanical and Automotive Engineering，Qingdao University of Technology，Qingdao 266520，China
  • Received:2022-08-28 Online:2025-06-01 Published:2025-07-23

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Abstract:

In order to realize the intelligent control of connected and autonomous driving vehicle in the intersection without signal in the intelligent network connection environment and improve the intersection passage efficiency， a vehicle passage control strategy based on the gap theory was proposed. According to the function and usage of the intersection area， the intersection area was divided into a change zone， a regulation zone， a buffer zone， a physical zone and a recovery zone. A vehicle conflict zone calculation model for the physical zone was established by considering the physical size of real vehicles， and a mathematical model for the clearance control of straight-straight， straight-left-turn and left-turn-left-turn vehicles was developed by optimising the trajectory of left-turn vehicles as an elliptical trajectory. A vehicle speed induction model for the regulation zone and buffer zone was established based on the trigonometric acceleration control strategy. The use of the efficiency and rationality of the control strategy and model were compared and verified by using joint simulation of Vissim and Matlab. The results show that the proposed control strategy and model can enable the conflicting vehicles to pass through the conflicting area sequentially without stopping； comparing with the signal control strategy， the average delay time of vehicles through the intersection is reduced by 55.97%， the average travel time is reduced by 41.87%， and the vehicle energy consumption is reduced by 33.31% under this control strategy and model at a traffic volume of 1 600 pcu/h， and the higher the traffic volume， the more significant the improvement effect is.

Key words: transport systems engineering, intelligent network links, signal-free intersections, gap theory, control strategies, conflict zones

CLC Number: 

  • U491.2

Fig.1

Intersection time slot control development"

Fig.2

Schematic diagram of gap control intersection"

Fig.3

Intersection control area division in a smart networked mixed traffic environment"

Table 1

Parameter values for the CACC model"

CACC参数取值
控制参数k10.45
控制参数k20.25
控制间隔Δt/s0.01
安全车头时距Tc/s0.6
最小停车间距s0/m2
车身长L/m5

Table 2

Recommended values for instantaneous power parameters for electric vehicles"

参数取值
车辆质量（包括驾驶员）M/kg1 266
滚动阻力系数frl0.026 9
电枢常数和磁通量的乘积K/（kg·m-1）0.35
电动机电阻r/Ω10.08
车轮半径R/m0.5

Fig.4

Intersection area CAV access control strategy"

Fig.5

Illustration of the vehicle passage process during CAV gap control"

Fig.6

Diagram of vehicle clearance in the conflict area"

Fig.7

Areas of conflict between straight ahead and straight ahead traffic"

Fig.8

Areas of conflict between straight ahead and left-turning vehicles"

Fig.9

Microscopic diagram of the action distance of the conflict zone between straight ahead and left-turning vehicles"

Fig.10

Areas of conflict between left-turning and left-turning vehicles"

Fig.11

Microscopic diagram of the action distance between left-turn and left-turn vehicle conflict areas"

Fig.12

Segmented trigonometric acceleration/ deceleration control strategy"

Fig.13

Schematic diagram of simulated intersection"

Table 3

Average vehicle delays and average travel times under different control schemes"

指标流量/（pcu·h-1）
8001 2001 600

信号

控制

平均延误/s17.8230.4841.29
平均行程时间/s63.1775.68118

智能

控制

平均延误/s15.4816.9818.03
平均行程时间/s44.958.8468.59

降低

比率

延误/%12.7943.0555.97
行程时间/%28.9332.8241.87

Fig.14

Comparison of vehicle average delay simulation evaluation results under different control strategies"

Fig.15

Comparison of simulation evaluation results of vehicle average travel time under different control strategies"

Table 4

Average vehicle energy consumption under different control schemes"

指标流量/（pcu·h-1）
8001 2001 600
信号控制能耗/（kw·h-1）2.314.788.4
智能控制能耗/（kw·h-1）1.893.595.63
能耗降低比率/%17.6924.8333.11

Fig.16

Comparison of intersection vehicle energy consumption under different control strategies"

[1] Zhong G, Zhang J, Yin T T, et al. A cooperative management strategy designed for unsignalized intersections under a connected vehicle environment[J]. Coat International Conference of Transportation Professionals, 2015, 2015: 233-245.
[2] 胡永辉, 金旭峰, 王亦兵, 等. 智能网联混行动力异构交通流生态驾驶[J].中国公路学报, 2022, 35(3): 15-27.
Hu Yong-hui, Jin Xu-feng, Wang Yi-bing, et al. Intelligent networked hybrid mobility for heterogeneous traffic flow eco-driving[J]. Chinese Journal of Highways, 2022, 35(3): 15-27.
[3] Lee J, Park B. Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment[J]. IEEE Intelligent Transportation System, 2012, 13(1): 81-90.
[4] 张游, 潘福全, 张丽霞, 等. 车路协同环境下智能交叉口车速控制[J]. 控制理论与应用, 2022, 39(6): 1057-1064.
Zhang You, Pan Fu-quan, Zhang Li-xia,et al. Intelligent intersection speed control in a vehicle-road cooperative environment[J]. Control Theory and Applications, 2022, 39(6): 1057-1064.
[5] 潘福全, 张游, 张丽霞, 等. 车路协同下基于间隙理论的交叉口智能控制策略[J]. 重庆交通大学学报: 自然科学版, 2022, 41(1): 44-52.
Pan Fu-quan, Zhang You, Zhang Li-xia, et al. Intelligent control strategy of intersection based on gap theory under vehicle-road cooperation[J]. Journal of Chongqing Jiaotong University (Natural Science Edition), 2022, 41(1): 44-52.
[6] Chen W, Liu Y. Gap-based automated vehicular speed guidance towards eco-driving at an unsignalized intersection[J]. Transportmetrica B: Transport Dynamics, 2017, 2017(3): 1-22.
[7] Chai L G, Cai B G, Wei S G, et al. Connected and autonomous vehicles coordinating approach at intersection based on space-time slot[J]. Transportmetrica A: Transport Science, 2018, 14(10): 929-951.
[8] 刘显贵, 王晖年, 洪经纬, 等. 网联环境下信号交叉口车速控制策略及优化[J]. 交通运输系统工程与信息, 2021, 21(2): 82-90.
Liu Xian-gui, Wang Hui-nian, Hong Jing-wei, et al. Signal intersection speed control strategy and optimization in a network-linked environment[J]. Transportation Systems Engineering and Information, 2021, 21(2): 82-90.
[9] Mahyar A, Mehdi N, Oliver G. Optimal traffic control at smart intersections: automated network fundamental diagram[J]. Transportation Research Part B, 2019, 137: 2-18.
[10] Zhang Y, Cassandras C G. Decentralized optimal control of connected automated vehicles at signal-free intersections including comfort-constrained turns and safety guarantees[J]. Automatica, 2019, 109: No.108563.
[11] 常玉林, 张成祥, 张鹏, 等. 车联网环境下基于间隙优化的无信号交叉口车速控制方法[J]. 重庆理工大学学报: 自然科学, 2021, 35(3): 10-17, 60.
Chang Yu-lin, Zhang Cheng-xiang, Zhang Peng, et al. A gap optimization-based speed control method for signal-free intersections in a connected vehicle environment[J]. Journal of Chongqing University of Technology (Natural Sciences), 2021, 35(3): 10-17, 60.
[12] 潘福全, 张丽霞, 陆键, 等. 接入管理技术在公路交叉口安全改善中的运用[J]. 北京工业大学学报, 2011, 37(2): 237-242.
Pan Fu-quan, Zhang Li-xia, Lu Jian, et al. The application of access management technology in highway intersection safety improvement[J]. Journal of Beijing University of Technology, 2011, 37(2): 237-242.
[13] Milanés V, Shladover S E, Spring J, et al. Cooperative adaptive cruise control in real traffic situations[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 15(1): 296-305.
[14] Milanés V, Shladover S E. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data[J]. Transportation Research Part C: Emerging Technologies, 2014, 48: 285-300.
[15] Xiao L, Wang M, Schakel W, et al. Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks[J]. Transportation Research Part C: emerging technologies, 2018, 96: 380-397.
[16] Wu X, Freese D, Cabrera A, et al. Electric vehicles' energy consumption measurement and estimation[J]. Transportation Research Part D: Transport and Environment, 2015, 34: 52-67.
[17] Altan O D, Wu G, Barth M J, et al. GlidePath: eco-friendly automated approach and departure at signalized intersections[J]. IEEE Transactions on Intelligent Vehicles, 2017, 2(4): 266-277.
[18] 张健, 吴坤润, 杨敏, 等. 智能网联环境下交叉口双环自适应控制模型[J]. 吉林大学学报: 工学版, 2021, 51(2): 541-548.
Zhang Jian, Wu Kun-run, Yang Min, et al. Dual-loop adaptive control model for intersections in an intelligent network link environment[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(2): 541-548.
[19] Stevanovic J, Stevanovic A, Martin P T, et al. Stochastic optimization of traffic control and transit priority settings in VISSIM[J]. Transportation Research Part C: Emerging Technologies, 2008, 16(3): 332-349.
[20] Mahmassani H S. 50th Anniversary invited article—autonomous vehicles and connected vehicle systems: flow and operations considerations[J]. Transportation Science, 2016, 50(4): 1140-1162.
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