A learning technique for deploying self-tuning traffic control systems

Currently, a considerable amount of human effort and time is spent for initialization or calibration of operational traffic control systems. Typically, this optimization (fine-tuning) procedure is conducted manually, via trial-and-error, relying on expertise and human judgment and does not always lead to a desirable outcome. This paper presents a new learning/adaptive algorithm that enables automatic fine-tuning of general traffic control systems. The efficiency and online feasibility of the algorithm is investigated through extensive simulation experiments. The fine-tuning problem of three mutually-interacting control modules — each one with its distinct design parameters — of an urban traffic signal control strategy is thoroughly investigated. Simulation results indicate that the learning algorithm can provide efficient automatic fine-tuning, guaranteeing safe and convergent behavior.

[1]  Manolis A. Christodoulou,et al.  Dynamical Neural Networks that Ensure Exponential Identification Error Convergence , 1997, Neural Networks.

[2]  Elias B. Kosmatopoulos,et al.  International comparative field evaluation of a traffic-responsive signal control strategy in three cities. , 2006 .

[3]  Yu. M. Ermol’ev.  On the method of generalized stochastic gradients and quasi-Féjer sequences , 1969 .

[4]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[5]  Markos Papageorgiou,et al.  A rolling-horizon quadratic-programming approach to the signal control problem in large-scale congested urban road networks , 2008 .

[6]  Αναστάσιος Κούβελας.  Adaptive fine-tuning for large-scale nonlinear traffic control systems , 2011 .

[7]  Markos Papageorgiou,et al.  Extensions and New Applications of the Traffic-Responsive Urban Control Strategy: Coordinated Signal Control for Urban Networks , 2003 .

[8]  Elias B. Kosmatopoulos,et al.  Large Scale Nonlinear Control System Fine-Tuning Through Learning , 2009, IEEE Transactions on Neural Networks.

[9]  J. Kiefer,et al.  Stochastic Estimation of the Maximum of a Regression Function , 1952 .

[10]  Ron Meir,et al.  Approximation bounds for smooth functions in C(Rd) by neural and mixture networks , 1998, IEEE Trans. Neural Networks.

[11]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[12]  Marios M. Polycarpou,et al.  High-order neural network structures for identification of dynamical systems , 1995, IEEE Trans. Neural Networks.

[13]  J. Spall.  Multivariate stochastic approximation using a simultaneous perturbation gradient approximation , 1992 .

[14]  M. Papageorgiou,et al.  Adaptive fine-tuning of non-linear control systems with application to the urban traffic control strategy TUC , 2007, 2007 European Control Conference (ECC).

[15]  Markos Papageorgiou,et al.  Adaptive Fine-Tuning of Nonlinear Control Systems With Application to the Urban Traffic Control Strategy TUC , 2007, IEEE Transactions on Control Systems Technology.

[16]  Markos Papageorgiou,et al.  A multivariable regulator approach to traffic-responsive network-wide signal control , 2000 .