Infinitesimal Perturbation Analysis of Stochastic Flow Models with Delays: Application to Multi-Intersection Traffic Light Control

We extend Stochastic Flow Models (SFMs), which are used for a large class of hybrid systems, by including the delays which typically arise in flow movement. We apply this framework to the multi-intersection traffic light control problem by including transit delays for vehicles moving from one intersection to the next. Using Infinitesimal Perturbation Analysis (IPA) for this SFM with delays, we derive new online gradient estimates of several congestion cost metrics with respect to the controllable green and red cycle lengths. The IPA estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. We introduce two new cost metrics to better capture congestion and show that the inclusion of delays in our analysis leads to improved performance relative to models that ignore delays.

[1]  Christos G. Cassandras,et al.  Multi-intersection Traffic Light Control with blocking , 2015, Discret. Event Dyn. Syst..

[2]  GengYanfeng,et al.  Multi-intersection Traffic Light Control with blocking , 2015 .

[3]  Michael C. Fu,et al.  Application of perturbation analysis to traffic light signal timing , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[4]  Christos G. Cassandras,et al.  Perturbation Analysis and Optimization of Stochastic Hybrid Systems , 2010, Eur. J. Control.

[5]  Christos G. Cassandras,et al.  Introduction to Discrete Event Systems , 1999, The Kluwer International Series on Discrete Event Dynamic Systems.

[6]  Christos G. Cassandras,et al.  Adaptive Quasi-Dynamic Traffic Light Control , 2016, IEEE Transactions on Control Systems Technology.

[7]  Christos G. Cassandras,et al.  Traffic light control using Infinitesimal Perturbation Analysis , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[8]  Christos G. Cassandras,et al.  Perturbation analysis for online control and optimization of stochastic fluid models , 2002, IEEE Trans. Autom. Control..

[9]  Avishai Mandelbaum,et al.  ON PATIENT FLOW IN HOSPITALS: A DATA-BASED QUEUEING-SCIENCE PERSPECTIVE , 2015 .

[10]  Yorai Wardi,et al.  A Unified Approach to Infinitesimal Perturbation Analysis in Stochastic Flow Models: The Single-Stage Case , 2010, IEEE Transactions on Automatic Control.

[11]  Michael C. Fu,et al.  ONLINE TRAFFIC LIGHT CONTROL THROUGH GRADIENT ESTIMATION USING STOCHASTIC FLUID MODELS , 2005 .

[12]  Steven X. Ding,et al.  Data-driven monitoring for stochastic systems and its application on batch process , 2013, Int. J. Syst. Sci..

[13]  E. Bair,et al.  Applied Groundwater Modeling—Simulation of Flow and Advective Transport , 2016 .

[14]  Christos G. Cassandras,et al.  Perturbation analysis of stochastic hybrid systems and applications to resource contention games , 2011 .