Utility optimization framework for a distributed traffic control of urban road networks

Route choice behavior has been recognized as an important factor in the traffic management and control strategy design. State-of-the-art research in this field (e.g. anticipatory traffic control approaches) often formulates this as a bi-level optimization problem where users’ reactions to changes in signal control are taken into account in the design of the control policies. Solving such a combined optimization problem is possible but difficult and complex which often involves the determination of the dynamic user equilibrium (UE). Nevertheless, anticipatory control inherently aims to optimize the signal control that anticipates or reacts to the users’ route choice and thus is a reactive control. Furthermore, the approaches used in anticipatory control are based on the coupling between control and traffic assignment where the route choice behavior of road users is unrealistically assumed to be in equilibrium.

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

[2]  Chris Tampère,et al.  An extended coordinate descent method for distributed anticipatory network traffic control , 2015 .

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

[4]  P R Lowrie,et al.  The Sydney coordinated adaptive traffic system - principles, methodology, algorithms , 1982 .

[5]  Henk Taale,et al.  Integrated anticipatory control of road networks: A game-theoretical approach , 2008 .

[6]  M J Smith,et al.  A LOCAL TRAFFIC CONTROL POLICY WHICH AUTOMATICALLY MAXIMISES THE OVERALL TRAVEL CAPACITY OF AN URBAN ROAD NETWORK , 1980 .

[7]  John D. C. Little,et al.  Optimization of Traffic Signal Settings by Mixed-Integer Linear Programming , 1975 .

[8]  Ronghui Liu,et al.  Traffic control and route choice; capacity maximization and stability , 2015 .

[9]  István Varga,et al.  Distributed traffic control system based on model predictive control , 2010 .

[10]  Pravin Varaiya,et al.  The Max-Pressure Controller for Arbitrary Networks of Signalized Intersections , 2013 .

[11]  Pitu B. Mirchandani,et al.  A REAL-TIME TRAFFIC SIGNAL CONTROL SYSTEM: ARCHITECTURE, ALGORITHMS, AND ANALYSIS , 2001 .

[12]  N. Ahmed.  Dynamic: Systems and Control With Applications , 2006 .

[13]  Dirk Helbing,et al.  Self-control of traffic lights and vehicle flows in urban road networks , 2008, 0802.0403.

[14]  Kay W. Axhausen,et al.  The potential of information provision in a simulated road transport network with non-recurrent congestion , 1995 .

[15]  Jan de Gier,et al.  Traffic flow on realistic road networks with adaptive traffic lights , 2010, 1011.6211.

[16]  Tung Le,et al.  Linear-Quadratic Model Predictive Control for Urban Traffic Networks , 2013 .

[17]  Leandros Tassiulas,et al.  Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1992 .

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

[19]  Steven T Waller,et al.  Integrated Network Capacity Expansion and Traffic Signal Optimization Problem: Robust Bi-level Dynamic Formulation , 2010 .

[20]  Moshe Ben-Akiva,et al.  Game-Theoretic Formulations of Interaction Between Dynamic Traffic Control and Dynamic Traffic Assignment , 1998 .

[21]  Ben Waterson,et al.  The evolution of urban traffic control: changing policy and technology , 2013 .

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

[23]  Henk Wymeersch,et al.  Back-Pressure Traffic Signal Control With Fixed and Adaptive Routing for Urban Vehicular Networks , 2016, IEEE Transactions on Intelligent Transportation Systems.

[24]  Nathan H. Gartner,et al.  Implementation of the OPAC adaptive control strategy in a traffic signal network , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[25]  Nathan H. Gartner,et al.  OPAC: A DEMAND-RESPONSIVE STRATEGY FOR TRAFFIC SIGNAL CONTROL , 1983 .

[26]  Henk Taale,et al.  Anticipatory Optimization of Traffic Control , 2000 .

[27]  Bart De Schutter,et al.  Toward System-Optimal Routing in Traffic Networks: A Reverse Stackelberg Game Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[28]  Alexander L. Stolyar.  Large number of queues in tandem: Scaling properties under back-pressure algorithm , 2011, Queueing Syst. Theory Appl..

[29]  R E Allsop.  SOME POSSIBILITIES FOR USING TRAFFIC CONTROL TO INFLUENCE TRIP DISTRIBUTION AND ROUTE CHOICE , 1974 .

[30]  Satish V. Ukkusuri,et al.  A Bi-level Formulation for the Combined Dynamic Equilibrium based Traffic Signal Control , 2013 .

[31]  Daniel Vanderpooten,et al.  Multiobjective and multimodal adaptive traffic light control on single junctions , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[32]  Ashish Bhaskar,et al.  Information provision and network performance represented by Macroscopic Fundamental Diagram , 2013 .

[33]  Mike Smith,et al.  Dynamics of route choice and signal control in capacitated networks , 2011 .

[34]  Alexander L. Stolyar,et al.  Novel Architectures and Algorithms for Delay Reduction in Back-Pressure Scheduling and Routing , 2009, IEEE INFOCOM 2009.

[35]  Itamar Elhanany,et al.  A Novel Signal-Scheduling Algorithm With Quality-of-Service Provisioning for an Isolated Intersection , 2008, IEEE Transactions on Intelligent Transportation Systems.

[36]  Andy H. F. Chow,et al.  Generalized Network Fundamental Diagram for motorway traffic management , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[37]  Wang,et al.  Review of road traffic control strategies , 2003, Proceedings of the IEEE.

[38]  A. Sen.  On Economic Inequality , 1974 .