Distributed Weighted Balanced Control of Traffic Signals for Urban Traffic Congestion

Since urban traffic congestion has become a major problem for big cities in recent years, we propose a distributed control scheme for traffic lights in the network. First, a new criterion called traffic process ability which implies the balance between the traffic demand and traffic capacity of each road is introduced. Moreover, the congestion of a road is mitigated by utilizing the traffic process ability of the neighbors more effectively, where different weights are assigned to roads in each agent according to their importance or the real-time traffic conditions. As only local information is needed, a distributed control scheme in which the road network is divided among several agents is proposed. Furthermore, in order to accelerate the congestion dissipation process, the aggregated state of each agent is introduced into the performance index and balanced with its neighboring agents. The control signals are calculated by agents in a parallel way and the optimization problem is solved iteratively to reach a convergence. Finally, the effectiveness of the proposed control scheme is evaluated by simulation under different scenarios and the performance is compared with the traffic responsive control method SCOOT.

[1]  N. Geroliminis,et al.  An analytical approximation for the macropscopic fundamental diagram of urban traffic , 2008 .

[2]  John D. C. Little,et al.  The Synchronization of Traffic Signals by Mixed-Integer Linear Programming , 2011, Oper. Res..

[3]  Vikash V. Gayah,et al.  Clockwise Hysteresis Loops in the Macroscopic Fundamental Diagram , 2010 .

[4]  Hong Kam Lo,et al.  A novel traffic signal control formulation , 1999 .

[5]  D I Robertson,et al.  "TRANSYT" METHOD FOR AREA TRAFFIC CONTROL , 1969 .

[6]  Bart De Schutter,et al.  Two-Level Hierarchical Model-Based Predictive Control for Large-Scale Urban Traffic Networks , 2017, IEEE Transactions on Control Systems Technology.

[7]  Bart De Schutter,et al.  Multi-agent model predictive control for transportation networks: Serial versus parallel schemes , 2008, Eng. Appl. Artif. Intell..

[8]  In Gwun Jang,et al.  Traffic Signal Optimization for Oversaturated Urban Networks: Queue Growth Equalization , 2015, IEEE Transactions on Intelligent Transportation Systems.

[9]  Lucas Barcelos de Oliveira,et al.  Multi-agent Model Predictive Control of Signaling Split in Urban Traffic Networks ∗ , 2010 .

[10]  Dai Li,et al.  Steady-State Signal Control for Urban Traffic Networks , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[11]  Dewei Li,et al.  Balance traffic control in urban traffic networks based on distributed optimization , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[12]  Emilio Frazzoli,et al.  Capacity-Aware Backpressure Traffic Signal Control , 2013, IEEE Transactions on Control of Network Systems.

[13]  Pravin Varaiya,et al.  Max pressure control of a network of signalized intersections , 2013 .

[14]  Bart De Schutter,et al.  Multi-agent model-based predictive control for large-scale urban traffic networks using a serial scheme , 2015 .

[15]  R E Allsop SIGSET: A COMPUTER PROGRAM FOR CALCULATING TRAFFIC SIGNAL SETTINGS , 1971 .

[16]  Giulio Erberto Cantarella,et al.  Control system design for an individual signalized junction , 1984 .

[17]  Tung Le,et al.  Decentralized signal control for urban road networks , 2013, 1310.0491.

[18]  Jack Haddad Optimal perimeter control synthesis for two urban regions with aggregate boundary queue dynamics , 2017 .

[19]  Bart De Schutter,et al.  Fast Model Predictive Control for Urban Road Networks via MILP , 2011, IEEE Transactions on Intelligent Transportation Systems.

[20]  N. Geroliminis,et al.  Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings - eScholarship , 2007 .

[21]  Carlos F. Daganzo,et al.  Urban Gridlock: Macroscopic Modeling and Mitigation Approaches , 2007 .

[22]  R D Bretherton,et al.  SCOOT-a Traffic Responsive Method of Coordinating Signals , 1981 .

[23]  S. Chand,et al.  Adaptive traffic signal control using fuzzy logic , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[24]  Markos Papageorgiou,et al.  A Multivariable Regulator Approach to Traffic-Responsive Network-Wide Signal Control , 2000 .

[25]  Danwei Wang,et al.  Distributed traffic signal control for maximum network throughput , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[26]  Serge P. Hoogendoorn,et al.  The impact of traffic dynamics on macroscopic fundamental diagram , 2013 .

[27]  Markos Papageorgiou,et al.  A rolling-horizon quadratic-programming approach to the signal control problem in large-scale conges , 2009 .

[28]  Meead Saberi,et al.  Urban Network Gridlock: Theory, Characteristics, and Dynamics , 2013 .

[29]  Ana L. C. Bazzan,et al.  A Distributed Approach for Coordination of Traffic Signal Agents , 2004, Autonomous Agents and Multi-Agent Systems.