Adaptive Neuro-Fuzzy Inference System for Traffic Cycle Optimization

W. Awad, S. Al-Agtash, and A. Nsour

References

  1. [1] J. Luk, Two traffic responsive area traffic control methods:Scat and Scoot, Traffic Engineering and Control, 24(1), 1984, 14–20.
  2. [2] N. Gartner, C. Stamatindius, & P. Tarnoff, Development ofadvanced traffic signal control strategies for intelligent transportation systems: Multilevel design, Transportation ResearchRecord 1494, 1995.
  3. [3] U.S. Transportation Research Board, National Research Council, Highway capacity manual, Special Report 209, 3rd ed., Washington, DC, 1994.
  4. [4] R. Vincent, A. Mitchel, & D. Robertson, User guide to RANSYT version, TRRL Report LR 888, Transport and Road Research Laboratory, Corwthrone, 1980.
  5. [5] I. Burrow & P. Willoughby, OSCADY: A computer program toaid the design of isolated signal junctions, paper presented at13th Summer Annual Meeting, Seminar N, PTRC, Universityof Sussex, July 1985.
  6. [6] F.V. Webster & B.M. Cobbe, Traffic signals, Ministry ofTransports, Road Research Technical Paper No. 56 (London:HMSO, 1962).
  7. [7] R.E. Allsop, SIGSET: A computer program for calculatingtraffic signal settings, Traffic Engineering and Control, 12 (2),June 1971.
  8. [8] A. Nsour, I. Al-Hujazi, & S. Al-Agtash, Optimal traffic cycleestimation using neural networks, International Journal ofModeling and Simulation, 20 (3), 2000, 221–226.
  9. [9] J.-S.R. Jang, ANFIS: Adaptive-network-based fuzzy inferencesystems, IEEE Trans. on Systems, Man, and Cybernetics,23 (3), 1993, 665–685. doi:10.1109/21.256541
  10. [10] TRANSYT-7F, User’s guide, U.S. Department of Transportation, Federal Highway Adiminstration, Washington, DC, December 1991.

Important Links:

Go Back