Unified Route Choice Framework and Empirical Study in Urban Traffic Control Environment

• Real-world traffic control systems (TCS) (1)“Fixed-Time”: SYNCHRO-generated coordinated signal timings Cycle times, splits and offsets, provided by the City of Pittsburgh (2) “Adaptive”: Scalable URban TRAffic Control (SURTRAC) system A decentralized TCS currently controlling 18 intersections in Pittsburgh Has reduced the average travel time through the pilot site by over 25% • Microscopic simulation to capture complexity of urban traffic control • Reach an Equilibrium (No disturbance) – The adaptive RCS reduced average travel time by 21.7%, and the adaptive TCS produced a further reduction 13.4% • Fixed-Time vs. Adaptive Traffic Control – Adaptive TCS: Real-time adaptation leads to flexible capacity control and reducing the risk of congestion – Fixed-time TCS: The loss of effectiveness as dynamic flow changes might be seen as modeling an aging problem that is observed in the real world • Observations on Decision Components – R4 (best response) can help for reaching to near optimal, R3 (inertia) can help for maintaining stable in congested cases – LRI2 and ABM variants make better decisions by updating choice probabilities rather than directly based on the cost array

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

[2]  Xiangdong Xu,et al.  Examining the scaling effect and overlapping problem in logit-based stochastic user equilibrium models , 2012 .

[3]  Richard Mounce,et al.  A splitting rate model of traffic re-routeing and traffic control , 2011 .

[4]  Chenfeng Xiong,et al.  Agent-Based Approach for Integrating Departure Time and Dynamic Traffic Assignment Models , 2013 .

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

[6]  R. Jayakrishnan,et al.  Modeling Framework to Analyze Effect of Multiple Traffic Information Service Providers on Traffic Network Performance , 2013 .

[7]  Kate Smith-Miles,et al.  Cross-disciplinary perspectives on meta-learning for algorithm selection , 2009, CSUR.

[8]  Yuan Yan Tang,et al.  Multi-agent oriented constraint satisfaction , 2002, Artif. Intell..

[9]  D. McFadden,et al.  MIXED MNL MODELS FOR DISCRETE RESPONSE , 2000 .

[10]  Jason R. Marden,et al.  Joint Strategy Fictitious Play with Inertia for Potential Games , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[11]  Chaitanya Swamy,et al.  The effectiveness of Stackelberg strategies and tolls for network congestion games , 2007, SODA '07.

[12]  Claudio Meneguzzer,et al.  REVIEW OF MODELS COMBINING TRAFFIC ASSIGNMENT AND SIGNAL CONTROL , 1997 .

[13]  Carlos F. Daganzo,et al.  On Stochastic Models of Traffic Assignment , 1977 .

[14]  M. Bierlaire,et al.  Sampling of Alternatives for Route Choice Modeling , 2009 .

[15]  John R. Anderson,et al.  Human Symbol Manipulation Within an Integrated Cognitive Architecture , 2005, Cogn. Sci..

[16]  R. McKelvey,et al.  Quantal Response Equilibria for Normal Form Games , 1995 .

[17]  S. Bekhor,et al.  Route Choice Models Used in the Stochastic User Equilibrium Problem: A Review , 2004 .

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

[19]  R. Hertwig,et al.  The priority heuristic: making choices without trade-offs. , 2006, Psychological review.

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

[21]  S. Hart,et al.  A simple adaptive procedure leading to correlated equilibrium , 2000 .

[22]  Stephen F. Smith,et al.  A few good agents: multi-agent social learning , 2008, AAMAS.

[23]  Zhen Qian,et al.  A Hybrid Route Choice Model for Dynamic Traffic Assignment , 2013 .

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

[25]  Song Gao,et al.  A rank-dependent expected utility model for strategic route choice with stated preference data , 2013 .

[26]  Berthold Vöcking,et al.  Fast convergence to Wardrop equilibria by adaptive sampling methods , 2006, STOC '06.

[27]  Johanne Cohen,et al.  Distributed Learning of Equilibria in a Routing Game , 2009, Parallel Process. Lett..

[28]  Yiheng Feng,et al.  A Hierarchical Agent-Based Simulation for Modeling Traveler Behaviors , 2013 .

[29]  Hani S. Mahmassani,et al.  On Boundedly Rational User Equilibrium in Transportation Systems , 1987, Transp. Sci..

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