Traffic Lights with Auction-Based Controllers: Algorithms and Real-World Data

Real-time optimization of traffic flow addresses important practical problems: reducing a driver's wasted time, improving city-wide efficiency, reducing gas emissions and improving air quality. Much of the current research in traffic-light optimization relies on extending the capabilities of traffic lights to either communicate with each other or communicate with vehicles. However, before such capabilities become ubiquitous, opportunities exist to improve traffic lights by being more responsive to current traffic situations within the current, already deployed, infrastructure. In this paper, we introduce a traffic light controller that employs bidding within micro-auctions to efficiently incorporate traffic sensor information; no other outside sources of information are assumed. We train and test traffic light controllers on large-scale data collected from opted-in Android cell-phone users over a period of several months in Mountain View, California and the River North neighborhood of Chicago, Illinois. The learned auction-based controllers surpass (in both the relevant metrics of road-capacity and mean travel time) the currently deployed lights, optimized static-program lights, and longer-term planning approaches, in both cities, measured using real user driving data.

[1]  Stephen F. Smith,et al.  SURTRAC: Scalable Urban Traffic Control , 2013 .

[2]  Klemens Böhm,et al.  Agent-Based Traffic Control Using Auctions , 2007, CIA.

[3]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[4]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[5]  Michèle Sebag,et al.  Automatic graph drawing and Stochastic Hill Climbing , 1999 .

[6]  Marco Wiering,et al.  Multi-Agent Reinforcement Learning for Traffic Light control , 2000 .

[7]  Peter Stone,et al.  Traffic Intersections of the Future , 2006, AAAI.

[8]  Gerald Heisig Nervousness in Material Requirements Planning Systems , 2002 .

[9]  John H. Holland,et al.  When will a Genetic Algorithm Outperform Hill Climbing , 1993, NIPS.

[10]  Hannes Hartenstein,et al.  The impact of traffic-light-to-vehicle communication on fuel consumption and emissions , 2010, 2010 Internet of Things (IOT).

[11]  Stephen F. Smith,et al.  Schedule-driven intersection control , 2012 .

[12]  T. Urbanik,et al.  Reinforcement learning-based multi-agent system for network traffic signal control , 2010 .

[13]  Rahul Sukthankar,et al.  Approximating the Effects of Installed Traffic Lights: A Behaviorist Approach Based on Travel Tracks , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[14]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[15]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[16]  Daniel Krajzewicz,et al.  Recent Development and Applications of SUMO - Simulation of Urban MObility , 2012 .

[17]  Stephen F. Smith,et al.  Schedule-Driven Coordination for Real-Time Traffic Network Control , 2012, ICAPS.

[18]  Jalan Kajang-Puchong,et al.  The Use of Genetic Algorithm for Traffic Light and Pedestrian Crossing Control , 2009 .

[19]  Martin Fössleitner,et al.  Traffic: Why We Drive the Way We Do (And What it Says About Us) , 2009 .

[20]  Glenn Geers,et al.  Transportation and Information , 2013, SpringerBriefs in Computer Science.

[21]  Gerald Heisig Planning Stability in Material Requirements Planning Systems , 2002 .

[22]  Thomas Urbanik,et al.  Enhanced Genetic Algorithm for Signal-Timing Optimization of Oversaturated Intersections , 2000 .

[23]  Rahul Sukthankar,et al.  Micro-Auction-Based Traffic-Light Control: Responsive, Local Decision Making , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[24]  Andrew Cumming,et al.  Multiple Traffic Signal Control Using A Genetic Algorithm , 1999, ICANNGA.

[25]  Shumeet Baluja,et al.  Fast Probabilistic Modeling for Combinatorial Optimization , 1998, AAAI/IAAI.

[26]  Shumeet Baluja,et al.  Genetic Algorithms and Explicit Search Statistics , 1996, NIPS.

[27]  Ozan K. Tonguz,et al.  Modeling urban traffic: A cellular automata approach , 2009, IEEE Communications Magazine.

[28]  Ardalan Vahidi,et al.  Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time , 2011, IEEE Transactions on Control Systems Technology.

[29]  D Christensen,et al.  EVALUATION OF INDUCTION LOOP INSTALLATION PROCEDURES IN FLEXIBLE PAVEMENTS - FINAL REPORT , 1985 .

[30]  Liviu Iftode,et al.  Adaptive Traffic Lights Using Car-to-Car Communication , 2007, 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring.

[31]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[32]  Klemens Böhm,et al.  Traffic Management Based on Negotiations between Vehicles - A Feasibility Demonstration Using Agents , 2007, AMEC/TADA.

[33]  Mark Harman,et al.  A theoretical & empirical analysis of evolutionary testing and hill climbing for structural test data generation , 2007, ISSTA '07.

[34]  Pat Langley,et al.  Learning Cooperative Lane Selection Strategies for Highways , 1998, AAAI/IAAI.

[35]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[36]  Baher Abdulhai,et al.  Reinforcement learning for true adaptive traffic signal control , 2003 .

[37]  Javier J. Sánchez Medina,et al.  Genetic algorithms and cellular automata: a new architecture for traffic light cycles optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[38]  Piyushimita Thakuriah,et al.  Transportation and Information: Trends in Technology and Policy , 2013 .

[39]  G. Harik Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .

[40]  Stephen D. Boyles,et al.  Auction-based autonomous intersection management , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[41]  Martin Wattenberg,et al.  Stochastic Hillclimbing as a Baseline Mathod for Evaluating Genetic Algorithms , 1995, NIPS.