Secondary Coordination at Closely Spaced Actuated Traffic Signals
Publication: Journal of Transportation Engineering
Volume 137, Issue 11
Abstract
This paper presents a method of addressing stochastic variation at closely spaced signalized intersections to provide secondary coordination to “minor” movements with significant traffic volumes. A neurofuzzy signal control system was designed in this study to manage a noncoordinated movement to avoid queue spillback. Building on the conventional actuated-coordinated control system, the neurofuzzy controller does not lose the benefit of the primary coordination of the conventional controller but establishes a “secondary coordination” between the upstream coordinated phase (through phase) and the downstream noncoordinated phase (left-turn phase) on the basis of a real-time traffic demand. Under the neurofuzzy signal control, the traffic from the upstream intersection can arrive and join the queue at the downstream left-turn lane and be served in a timely fashion and thus reduce the likelihood of being delayed at the downstream intersection. The simulation results indicate that the neurofuzzy signal control consistently outperformed the conventional actuated-coordinated controller in terms of reduction in systemwide average delay and number of stops per vehicle under a wide range of traffic volumes by nearly 20% under heavier demand conditions.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This paper was based on a study supported by the National Science Foundation under Grant No. NSF0528143. The writers also are grateful for the helpful input from Dr. J. Wesley Hines.
References
Berenji, H. R., and Khedkar, P. (1992). “Learning and tuning fuzzy controllers through reinforcements.” IEEE Trans. Neural Network, 3(5), 724–740.
Bingham, E. (2001). “Reinforcement learning in neurofuzzy traffic signal control.” Eur. J. Oper. Res., 131(2), 232–241.
Chiu, S., and Chand, S. (1993). “Adaptive traffic signal control using fuzzy logic, fuzzy systems.” Proc., 2nd IEEE Int. Conf. on Fuzzy Systems, Vol. 2, San Francisco, 1371–1376.
Chou, C., and Teng, J. (2002). “A fuzzy controller for traffic junction signals.” Inf. Sci. (N.Y.), 143(1–4), 73–97.
Engelbrecht, R. J., and Barnes, K. E. (2003). “Advanced traffic signal control for diamond interchanges.” Transportation Research Record 1856, Transportation Research Board, Washington, DC, 231–238.
Fang, C., and Elefteriadou, L. (2006). “Development of an optimization methodology for adaptive traffic signal control at diamond interchanges.” J. Transp. Eng., 132(8), 629–637.
Favilla, J., Machion, A., and Gomide, F. (1993). Fuzzy traffic control: adaptive strategies, Vol. 1, IEEE, San Francisco, 506–510.
Gulley, N. (1996). Fuzzy logic toolbox for use with MATLAB, MathWorks, Inc., Natick, MA.
Han, L. D., and Li, J. (2007). “Short or long… which is better? Probabilistic approach towards cycle length optimization.” Transportation Research Record 2035, Transportation Research Board, Washington, DC, 150–157.
Han, L. D., and May, A. D. (1990). “Traffic flow characteristics of signalized arterials under disturbance situations.” Research Rep. UCB-ITS-RR-90-12A, Institute of Transportation Studies, University of California at Berkeley, Berkeley, CA.
Kim, J. (1997). “A fuzzy logic control simulator for adaptive traffic management.” Proc., 6th IEEE Int. Conf. on Fuzzy Systems, IEEE, Barcelona, Spain, 1519–1524.
Kovvali, V. G., Messer, C. J., Chaudhary, N. A., and Chu, C. (2002). “Program for optimizing diamond interchanges in oversaturated conditions.” Transportation Research Record 1811, Transportation Research Board, Washington, DC, 166–176.
Lee, J., and Hyung, L. (1999). “Distributed and cooperative fuzzy controllers for traffic intersections group.” IEEE Trans. Syst., Man Cybern: Part C: Appl. Rev., 29(2), 263–271.
Messer, C. J., Fambro, D. B., and Richards, S. H. (1997). “Optimization of pretimed signalized diamond interchanges.” Transportation Research Record 644, Transportation Research Board, Washington, DC, 78–84.
Nakatsuyama, M., Nagahashi, H., and Nishizuka, N. (1984). “Fuzzy logic phase controller for traffic functions in the one-way arterial road.” Proc., IFAC 9th Triennial World Congress, Pergamon Press, Oxford, UK, 2865–2870.
Niittymaki, J. (1999a). “Fuzzy logic two-phase traffic signal control for coordinated one-way streets.” Proc., IEEE Midnight-Sun Workshop on Soft Computing Methods in Industrial Applications, Kuusamo, Finland, 69–74.
Niittymaki, J. (1999b). “Using fuzzy logic to control traffic signals at multi-phase intersections.” Proc., 6th Int. Conf. on Computational Intelligence, Theory and Applications: Fuzzy Days, Springer, Berlin, 354–362.
Niittymaki, J. (2001). “Installation and experiences of field testing a fuzzy signal controller.” Eur. J. Oper. Res., 131(2), 273–281.
Niittymaki, J., and Pursula, M. (2000). “Signal control using fuzzy logic.” Fuzzy Sets Syst., 116(1), 11–22.
Niittymaki, J., and Kikuchi, S. (1998). “Application of fuzzy logic to the control of a pedestrian crossing signal.” Transportation Research Record 1651, Transportation Research Board, Washington, DC, 30–38.
Oliveira-Neto, F. M., Loureiro, C. F. G., and Han, L. D. (2009). “Active and passive bus priority strategies in mixed traffic arterials controlled by SCOOT adaptive signal system.” Transportation Research Record 2128, Transportation Research Board, Washington, DC, 58–65.
Patel, M., and Ranganathan, N. (2001). “IDUTC: An intelligent decision-making system for urban traffic-control applications.” IEEE Trans. Vehi. Technol., 50(3), 816–829.
Sutton, R. S., and Barto, A. G. (1998). Reinforcement learning: An introduction, MIT Press, Cambridge, MA.
Tan, K. K., Khalid, M., and Yusof, R. (1996). “Intelligent traffic lights control by fuzzy logic.” Malays. J. Comput. Sci., 9(2), 29–35.
Trabia, M. B., Kaseko, M. S., and Ande, M. (1999). “A two-stage fuzzy controller for traffic signals.” Transp. Res. C: Emerging Technol., 7(6), 353–367.
TRB. (2000). “Chapter 26 Interchange ramp terminals.” Special Rep. 209: Highway Capacity Manual, Transportation Research Board, National Research Council, Washington, DC.
VISSIM Version 4.30 [Computer software]. Planning Transport Verkekr AG. Karlsruhe, Germany.
Zhang, L., Li, H., and Prevedouros, P. D. (2005). “Signal control for oversaturated intersections using fuzzy logic.” TRB 84th Annual Meeting Compendium of Papers (CD-ROM), Transportation Research Board, National Research Council, Washington, DC.
Information & Authors
Information
Published In
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Jun 15, 2009
Accepted: Feb 17, 2011
Published online: Feb 19, 2011
Published in print: Nov 1, 2011
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.