Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230900016-7.doi: 10.11896/jsjkx.230900016

• Interdiscipline & Application • Previous Articles     Next Articles

Traffic Subarea Boundary Control Strategy Based on Nonlinear Traffic Flow Model

WANG Xiaolong   

  1. Shanxi Intelligent Transportation Research Institute Co.,Ltd,Taiyuan,030032,China
  • Published:2024-06-06
  • About author:WANG Xiaolong,born in 1984,master,senior engineer. His main reseach interests is intelligent traffic control.
  • Supported by:
    National Natural Science Foundation of China(61703300).

Abstract: Urban traffic flow has complex nonlinear dynamic characteristics,which cannot be accurately described by a simplified linear traffic flow model.Therefore,in this paper,on the basis of considering the influence of perturbation on subarea boundary control,a nonlinear urban macroscopic traffic flow model considering perturbation is firstly established,so that the model can better describe the operation of actual traffic flow.Secondly,a subarea boundary control strategy based on iterative learning control is designed by combining the periodic characteristics of urban traffic flow operation,and the convergence of the iterative learning control law is analyzed by using the Lipschitz condition and partial derivatives.Finally,the effectiveness of the traffic subarea boundary control strategy based on the nonlinear traffic flow model is demonstrated by simulation cases.

Key words: Urban traffic, Perimeter control, Iterative learning control, Nonlinear traffic flow model

CLC Number: 

  • TP273
[1]YU Z,NING N W,ZHENG Y L,et al.Review of Intelligent Traffic Signal Control Strategies Driven by Deep Reinforcement Learning[J].Computer Science,2023,50(4):159-171.
[2]OU Y Z,ZHOU S Y,LV Y,et al.DRL-based Vehicle ControlStrategy for Signal-free Intersections[J].Computer Science,2022,49(3):46-51.
[3]SUN H,CHEN C L,LIU Q,et al.Traffic Signal Control Me-thod Based on Deep Reinforcement Learning[J].Computer Science,2020,47(2):169-174.
[4]HADDAD J,ZHENG Z F.Adaptive perimeter control for multi-region accumulation-based models with state delays[J].Transportation Research Part B,2020,137(7):133-153.
[5]HADDAD J,SHRAIBER A.Robust perimeter control designfor an urban region[J].Transportation Research Part B:Me-thodological,2014,68:315-332.
[6]KEYVAN-EKBATAN I,PAPAGEORGIOU M,KNOOP V L.Controller design for gating traffic control in presence oftime-delay in urban road networks[J].Transportation Research Procedia,2015,59(10):308-322.
[7]HADDAD J.Optimal perimeter control synthesis for two urban regions with aggregate boundary queue dynamics[J].Transportation Research Part B,2015,96(13):1-25.
[8]HAJIAHMADI M,HADDAD J,SCHUTTER B D,et al.Optimal hybrid perimeter and switching plans control for urban traffic networks[J].IEEE Transactions on Control Systems Technology,2015,23(2):464-478.
[9]ZHONG R X,CHEN C,HUANG Y P,et al.Robust perimeter control for two urban regions with macroscopic fundamental diagrams:a control-Lyapunov function approach[J].Transportation Research Procedia,2017,23(9):922-941.
[10]HADDAD J.Optimal coupled and decoupled perimeter control in one-region cities[J].Control Engineering Practice,2017,61(10):134-148.
[11]DING H,ZHNAG Y,ZHENG X,et al.Hybrid perimeter control for two-region urban cities with different states[J].Control Systems Technology,IEEE Transactions,2018,26(6):2049-2062.
[12]HADDAD J,ZHENG Z.Adaptive perimeter control for multi-region accumulation-based models with state delays[J].Transportation Research Part B,2018,137(6):1-21.
[13]HOU Z S,XU J X.Freeway traffic density control using iterative learning control approach[C]//IEEE.The IEEE 6th International Conference on Intelligent Transportation Systems.Shanghai:IEEE Press;2003:1081-1086.
[14]HOU Z S,XU J X,YAN J W.An iterative learning approach for density control of freeway traffic flow via ramp metering[J].Transportation Research Part C,2008,16(1):71-97.
[15]HOU Z,XU X,YAN J,et al.A complementary modularizedramp metering approach based on iterative learning control and ALINEA[J].IEEE Transactions on Intelligent Transportation Systems,2011,12(4):1305-1318.
[16]YAN F,TIAN F L,SHI Z K,et al.Iterative learning approach for traffic signal control of urban road networks[J].IET Control Theory and Applications,2017,11(4):466-475.
[17]YAN F,TIAN F L,SHI Z K.An extended signal control strategy for urban network traffic flow[J].Physica A:Statistical Mechanics and its Applications,2016,445:117-127.
[18]DING Y,JIN S,YIN C,et al.ILC based perimeter control for an urban traffic network[C]//2016 14th International Conference on Control,Automation,Robotics and Vision(ICARCV).IEEE,2016:1-6.
[19]SU X,HUANG C,WANG W,et al.Iterative learning control with initial rectifying for nonlinear robotic system[C]//2017 11th Asian Control Conference(ASCC).IEEE,2017:1543-1547.
[20]YAN F,WANG K,SHI Z K.Iterative learning perimeter control method for traffic sub-region considering disturbances[J].Physica A:Statistical Mechanics and its Applications,2021,578(1):126104.
[1] LIU Jia-cun, ZHAO Gui-yan, MEI Qi-xiang. Study of Optimal Learning Law and Simplified Learning Law of Iterative Learning Control in Frequency Domain [J]. Computer Science, 2019, 46(2): 327-332.
[2] HAO Xiao-hong, LI Zhuo-yue and WANG Hua. PID-type Iterative Learning Control for a Class of Nonlinear Systems with Arbitrary Initial Value [J]. Computer Science, 2016, 43(2): 283-286.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!