Revisiting the application of simultaneous perturbation stochastic approximation towards signal timing optimization

References

• Agbolosu-Amison, S., Park, B., & Yun, I. (2009, October). Comparative evaluation of heuristic optimization methods in urban arterial network optimization. Paper presented at the IEEE ITSC, St. Louis, MO.
   Google Scholar
• Balakrishna, R., Antoniou, C., Ben-Akiva, M., Koutsopoulos, H., & Wen, Y. (2007). Calibration of microscopic traffic simulation models: Methods and application. Transportation Research Record: Journal of the Transportation Research Board, 1999, 198–207.
   Web of Science ®Google Scholar
• Basyoni, Y., & Talaat, H. (2015). A bilevel traffic data extraction procedure via cellular phone network for intercity travel. Journal of Intelligent Transportation Systems, 19(3), 289–303.
   Web of Science ®Google Scholar
• Bullock, D., Clayton, R., Mackey, J., Misgen, S., Stevens A., Sturdevant, J., Taylor, M. (2014). Automated traffic signal performance measures. ITE Journal, 84(3), 33–39.
   Web of Science ®Google Scholar
• Cipriani, E., Florian, M., Mahut, M., & Nigro, M. (2011). A gradient approximation approach for adjusting temporal origin-destination matrices. Transportation Research Part C: Emerging Technologies, 19(2), 270–282.
   Web of Science ®Google Scholar
• Cobos, C., Daza, C., Martínez, C., Mendoza, M., Gaviria, C., Arteaga, C., & Paz, A. (2016a). Calibration of CORSIM vehicular traffic flow models using a memetic algorithm with solis and wets local search chaining. Advances in Artificial Intelligence: Lecture Notes in Computer Science, 10022, 365–375.
   Google Scholar
• Cobos, C., Erazo, C., Luna, J., Mendoza, M., Gaviria, C., Arteaga, C., & Paz, A. (2016b). Multi-objective memetic algorithm based on NSGA-II and simulated annealing for calibrating CORSIM micro-simulation models of vehicular traffic flow. Advances in Artificial Intelligence: Lecture Notes in Computer Science, 9868, 468–476.
   Google Scholar
• Cipriani, E. & Gori, S. (2000). Signal control and assignment joint models. Traffic and Transportation Studies, 614–621.
   Google Scholar
• Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
   Web of Science ®Google Scholar
• Ghanim, M. & Abu-Lebdeh, G. (2015). Real-time dynamic transit signal priority optimization for coordinated traffic networks using genetic algorithms and artificial neural networks. Journal of Intelligent Transportation Systems, 19(4), 327–338.
   Web of Science ®Google Scholar
• Hale, D. K., Antoniou, C., Brackstone, M., Michalaka, D., Moreno, A., & Parikh, K. (2015). Optimization-based assisted calibration of traffic simulation models. Transportation Research Part C: Emerging Technologies, 55, 100–115.
   Web of Science ®Google Scholar
• Hale, D. K., Park, B., Stevanovic, A., Su, P., & Ma, J. (2015). Optimality versus run time for isolated signalized intersections. Transportation Research Part C: Emerging Technologies, 55, 191–202.
   Web of Science ®Google Scholar
• He, Q., Head, K. L., & Ding, J. (2011). Heuristic algorithm for priority traffic signal control. Transportation Research Record: Journal of the Transportation Research Board, 2259, 1–7.
   Web of Science ®Google Scholar
• Highway Capacity Manual. (2010). Transportation research board of National Academies, Washington DC.
   Google Scholar
• Kirkpatrick, S., Gelatt, G. C., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598).
   PubMed Web of Science ®Google Scholar
• Koonce, P., Rodegerdts, L., Lee, K., Quayle, S., Beaird, S., Braud, C.,... Urbanik, T. (2008). Traffic signal timing manual. No. FHWA-HOP-08-024.
   Google Scholar
• Li, L., Chen, X., & Zhang, L. (2016). A global optimization algorithm for trajectory data based car-following model calibration. Transportation Research Part C, 68, 311–332.
   Web of Science ®Google Scholar
• Lo, H. K., & Chow, A. H. (2004). Control strategies for oversaturated traffic. ASCE Journal of Transportation Engineering, 130(4), 466–478.
   Web of Science ®Google Scholar
• Markou, I. & Antoniou, C. (2015, June). Developing insight into effective SPSA parameters through sensitivity analysis. Paper presented at the 4th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2015), Budapest, Hungary.
   Google Scholar
• Oda, T., Otokita, T., Tsugui, T., Kohno, M., & Mashiyama, Y. (1996). Optimization of signal control parameters using a genetic algorithm. Paper presented at the 3rd World Congress on ITS, Orlando, FL.
   Google Scholar
• Ozguven, E. E., & Ozbay, K. (2008). Simultaneous perturbation stochastic approximation algorithm for solving stochastic problems of transportation network analysis: performance evaluation. Transportation Research Record: Journal of the Transportation Research Board, 2085, 12–20.
   Web of Science ®Google Scholar
• Park, B. (1998). Development of genetic algorithm-based signal optimization program for oversaturated intersections. Ph.D. Dissertation, Texas A&M University.
   Google Scholar
• Paz, A., Molano, V., Martinez, E., Gaviria, C., & Arteaga, C. (2015). Calibration of traffic flow models using a memetic algorithm. Transportation Research Part C: Emerging Technologies, 55, 432–443.
   Web of Science ®Google Scholar
• Paz, A., Molano, V., & Sanchez, M. (2015). Holistic calibration of microscopic traffic flow models: Methodology and real world application studies. In Engineering and applied sciences optimization, (pp. 33–52). Springer International Publishing.
   Google Scholar
• Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), 33–57.
   Google Scholar
• Robertson, D. (1969). TRANSYT: A traffic network study tool. Road Research Laboratory Report LR 253.
   Google Scholar
• Schorr, J., Hamdar, S., & Silverstein, C. (2016). Measuring the safety impact of road infrastructure systems on driver behavior: Vehicle instrumentation and real world driving experiment. Journal of Intelligent Transportation Systems, 0(0), 1–11.
   Google Scholar
• Spall, J. C. (1998). Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Transactions on Aerospace and Electronic Systems, 34(3), 817–823.
   Web of Science ®Google Scholar
• Spall, J. C. (1995). Method and apparatus for model-free optimal signal timing for system-wide traffic control. US Patent 5668717 A.
   Google Scholar
• Spall, J. C. (2012). Stochastic optimization. In Handbook of computational statistics, (pp. 173–201). Berlin Heidelberg, Germany: Springer.
   Google Scholar
• TRB Research Needs Statements accessed on February 28th. (2014). Retrieved from http://rns.trb.org/dproject.asp?n=15590
   Google Scholar
• Trelea, I. C. (2003). The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85(6), 317–325.
   Web of Science ®Google Scholar
• Webster, F. V. (1958). Traffic signal settings. Road Research Technical Report 39.
   Google Scholar
• Zhao, L., Peng, X., Li, L., & Li, Z. (2011). A fast signal timing algorithm for individual oversaturated intersection. IEEE Transactions on Intelligent Transportation Systems, 12(1), 280–283.
   Web of Science ®Google Scholar
• Zhu, N., Ma, S., & Zheng, L. (2017). Travel time estimation oriented freeway sensor placement problem considering sensor failure. Journal of Intelligent Transportation Systems, 21(1), 26–40.
   Web of Science ®Google Scholar