Optimizing Signal Timings from the Field

Traditionally, when traffic signals are retimed, a significant difference is seen between signal timings recommended by optimization software and those implemented in field controllers. Those two sets of signal timings rarely match each other, and often a manual process is involved in transferring the data to and from the field controllers. A method is presented: signal timings are downloaded from field controllers, optimized by a software package, and then uploaded to field controllers. The method is VISGAOST, a stochastic optimization program, working with VISSIM–ASC/3 software-in-the-loop simulation to optimize the signal timings obtained from the field. The method was applied to optimize signal timings for a five-intersection urban arterial segment in West Valley City, Utah. Traffic operations simulated by a high-fidelity VISSIM represented field observations reliably. After thousands of potential signal timings were evaluated, VISGAOST found a better set of signal timings than those used in the field. The final signal timings were tested for robustness under fluctuating traffic in microsimulation. The test results show that these optimized signal timings are more robust than those used in the field. Further applications of the method are needed to test the field performance of the signal timings optimized by VISGAOST.

[1]  M. Bacic,et al.  On hardware-in-the-loop simulation , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[2]  Ilsoo Yun,et al.  Evaluation of Adaptive Maximum Feature in Actuated Traffic Controller , 2007 .

[3]  Peter T. Martin,et al.  Stochastic optimization of traffic control and transit priority settings in VISSIM , 2008 .

[4]  Ilsoo Yun,et al.  Stochastic optimization method for coordinated actuated signal systems , 2003 .

[5]  Ayman Smadi,et al.  Use of Hardware-in-the-Loop Traffic Simulation in a Virtual Environment , 2006 .

[6]  Darcy M. Bullock,et al.  Real-Time Offset Transitioning Algorithm for Coordinating Traffic Signals , 2001 .

[7]  Peter T. Martin,et al.  VisSim-Based Genetic Algorithm Optimization of Signal Timings , 2007 .

[8]  David E. Goldberg,et al.  SIGNAL TIMING DETERMINATION USING GENETIC ALGORITHMS , 1992 .

[9]  Darcy M. Bullock,et al.  Evaluation of Diamond Interchange Signal Controller Settings by Using Hardware-in-the-Loop Simulation , 1999 .

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

[11]  Byungkyu Park,et al.  EVALUATION OF TRAFFIC SIGNAL TIMING OPTIMIZATION METHODS USING A STOCHASTIC AND MICROSCOPIC SIMULATION PROGRAM , 2002 .

[12]  Thomas Urbanik,et al.  Traffic Signal Optimization Program for Oversaturated Conditions: Genetic Algorithm Approach , 1999 .

[13]  Michael Kyte,et al.  Traffic Signal Operations Education Through Hands-On Experience: Lessons Learned from a Workshop Prototype , 2003 .

[14]  C E Wallace,et al.  HYBRID GENETIC ALGORITHM TO OPTIMIZE SIGNAL PHASING AND TIMING , 1993 .

[15]  Thomas Urbanik,et al.  ADVANCED TECHNOLOGY APPLICATION: THE "SMART" DIAMOND , 1995 .