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Traffic signal timing using two-dimensional correlation, neuro-fuzzy and queuing based neural networks

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Abstract

Optimizing the traffic signal control has an essential impact on intersections efficiency in urban transportation. This paper presents a two-stage method for intersection signal timing control. First, the traffic volume is predicted using a neuro-fuzzy network called Adaptive neuro-fuzzy inference system (ANFIS). The inputs of this network include two-dimensional, hourly and daily, traffic volume correlations. In the second stage, appropriate signal cycle and optimized timing of each phase of the signal are estimated using a combination of Self Organizing and Hopfield neural networks. The energy function of the Hopfield network is based on a traffic model derived by queuing analysis. The performance of the proposed method has been evaluated for real data. The two-dimensional correlation presents superior performance compared to hourly traffic correlation. The evaluation of proposed overall method shows considerable intersection throughput improvement comparing to the results taken form Synchro software.

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References

  1. Niittymaki J (2001) General fuzzy rule base for isolated traffic signal control. J Transp Plann Technol 24(3):227–247

    Article  Google Scholar 

  2. Henry JJ, Farges JL, Gallego JL (1998) Neuro-fuzzy techniques for traffic control. In: Proceedings of the 8th IFAC/IFIP/IFORS symposium on transportation systems, vol 2, Chania, Greece, pp 719–724

  3. Lilin Z, Lei J, Luo Y (2006) An intelligent control method for urban traffic signal based on fuzzy neural network. In: Proceedings of the 6th world congress on intelligent control and automation, Dalian, China, pp 3430–3434

  4. Maghrebi F (1998) On a Hopfield net arising in the modelling and control of over-saturated signalized intersections. Neural Process Lett 10(3):161–169

    Article  Google Scholar 

  5. Wei W, Wang MJ (2003) Fuzzy-MOGA-based traffic signal control at intersection. In: Proceedings of the 2nd international conference on machine learning and cybernetics, pp 639–644

  6. Dipti S, Chee CM, Cheu RL (2006) Neural networks for real-time traffic signal control. IEEE Trans Intell Transp Syst 7(3):261–272

    Google Scholar 

  7. Teodorovic D, Varadarajan V, Jovan P, Chinnaswamy M, Sharath R (2006) Dynamic programming—neural network real-time traffic adaptive signal control algorithm. Ann Oper Res 143(1):123–131

    Google Scholar 

  8. Nishikawa I, Sakakibara K, Iritai T (2005) 2 Types of complex-valued hopfield networks and the application to a traffic signal control. Proceedings of international joint conference on neural networks, Montreal, Canada, pp 782–787

  9. Ning Z, Pitu M (2005) Analyses of vehicular delays and queues at intersections with adaptive and fixed timing control strategies. Proceedings of the 8th international IEEE conference on intelligent transportation systems, Vienna, Austria, pp 97–102

  10. Yu XH (2005) An integrated model for signalized traffic intersection control. Proceedings of the 2005 IEEE conference on control applications Toronto, Canada, pp 1427–1430

  11. Innamaa S (2000) Short-term prediction of traffic situation using mlp-neural networks. Proceedings of the 7th world congress on intelligent transport systems, Turin, Italy, pp 6–9

  12. Annunziato M, Bertini I, Pannicelli A, Pizzuti S (2003) Evolutionary feed-forward neural networks for traffic prediction. International congress on evolutionary methods for design, optimization and control (EUROGEN), Barcelona, Spain, pp 17–20

  13. Yasdi R (1999) Prediction of road traffic using a neural network approach. Neural Comput Appl 8:135–142

    Article  Google Scholar 

  14. Yin H, Wong Sc, Xu J,Wong CK (2002) Urban traffic flow prediction using a fuzzy neural approach. Transp Res Part C: Emerg Technol 10(2):85–98

    Article  Google Scholar 

  15. Dharia A, Adeli H (2003) Neural network model for rapid forecasting of freeway link travel time. Eng Appl Art Intell 16(7):607–613

    Article  Google Scholar 

  16. Tong HY, Hung WT (2002) Neural network modeling of vehicle discharge headway at signalized intersection: model descriptions and results. Transp Res Part A: Policy Practice 36:17–40

    Article  Google Scholar 

  17. Qiao F, Yi P, Yang H, Devaradonda S (2002) Fuzzy logic based intersection delay estimation. J Math Comput Modell 36(11):11–13

    Google Scholar 

  18. Ishak S, M. SCE, Alecsandru C (2004) Optimizing traffic prediction performance of neural networks under various topological, input, and traffic condition settings. J Transp Eng 130(4):452–465

  19. LuC, Wei W, Tan Y (2004) Traffic variable estimation and traffic signal control based on soft computation. IEEE intelligent transportation systems conference, USA, pp 1045–1050

  20. Takagi H (1988) Queuing analysis of polling models. ACM Comput Sur 20(1):5–28

    Article  MATH  MathSciNet  Google Scholar 

  21. Chee HN (1996) Queueing modeling fundamentals. Wiley, New York

  22. Shing J, Jang JR (1993) ANFIS: adaptive-neural-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  23. Castellano G, Fanelli AM (2000) A self-organizing neural fuzzy inference network. IEEE-INNS-ENNS Int Joint Conference on Neural Networks (IJCNN’00) 5:14–19

    Article  Google Scholar 

  24. Freeman A (1991) Neural network, algorithms, applications and programming techniques. Addison Wesley, Reading

  25. Wasserman P (1989) Neural computing; theory and practice. New York. Van Nostrand

  26. http://www.trafficware.com

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Authors and Affiliations

  1. Computer Department, Isfahan University, Isfahan, Iran

    Marjan Kaedi, Naser Movahhedinia & Kamal Jamshidi

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  1. Marjan Kaedi
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  2. Naser Movahhedinia
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  3. Kamal Jamshidi
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Correspondence to Marjan Kaedi.

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Kaedi, M., Movahhedinia, N. & Jamshidi, K. Traffic signal timing using two-dimensional correlation, neuro-fuzzy and queuing based neural networks. Neural Comput & Applic 17, 193–200 (2008). https://doi.org/10.1007/s00521-007-0094-x

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  • DOI: https://doi.org/10.1007/s00521-007-0094-x

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