All Title Author
Keywords Abstract


Multi Model Criteria for the Estimation of Road Traffic Congestion from Traffic Flow Information Based on Fuzzy Logic

DOI: 10.4236/jtts.2012.21006, PP. 50-62

Keywords: Fuzzy Inference Systems, Fuzzy Rules, Congestion, Human Perception

Full-Text   Cite this paper   Add to My Lib

Abstract:

In this study, the road traffic congestion of Dehradun city is evaluated from traffic flow information using fuzzy techniques. Three different approaches namely Sugeno, Mamdani models which are manually tuned techniques, and an Adaptive Neuo-Fuzzy Inference System (ANFIS) which an automated model decides the ranges and parameters of the membership functions using grid partition technique, based on fuzzy logic. The systems are designed to human’s feelings on inputs and output levels. There are three levels of each input namely high, medium and low for input density, fast, medium and slow for input speed, and five levels of output namely free flow, slow moving, mild congestion, heavy congestion and serious jam for the road traffic congestion estimation. The results, obtained by fuzzy based techniques show that the manually tuned Sugeno type technique achieves 72.05% accuracy, Mamdani type technique achieves 83.82% accuracy, and Adaptive Neuro-Fuzzy Inference System technique achieves 88.23% accuracy. ANFIS technique appears better than the manually tuned fuzzy technique, and also the manually tuned fuzzy technique gives good accuracy which leads that the fuzzy inference system can capture the human perception better through manual adjustment of input/output membership functions.

References

[1]  Bureau of Public Roads, “Highway Capacity Manual: Practical applications of Research,” US Department of Commerce, Washington DC, 1950.
[2]  D. Branston, “Link Capacity Functions: A Review” Trans- portation Research, Vol. 10, No. 4, 1976, pp. 223-236. doi:10.1016/0041-1647(76)90055-1
[3]  P. Posawang, S. Phosaard, W. Polnigongit and W. Pattara-Atikom, “Perception-Based Road Traffic Congestion Classification Using Neural Networks,” Proceedings of the World Congress on Engineering, London, 1-3 July 2009.
[4]  A. P. Addepalli, “Study of Mixed Traffic Flow Characteristics: A Microscopic Simulation Approachm,” M.Tech Thesis, IIT, Madras, 2000.
[5]  C.-C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controller I,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 20, No. 2, 1990, pp. 404-418. doi:10.1109/21.52551
[6]  C.-C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controller II,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 20, No. 2, 1990, pp. 419-435. doi:10.1109/21.52552
[7]  A. Kablan, “Adaptive Neuro-Fuzzy Inference System for Financial Trading Using Intraday Seasonality Observa- tion Model,” World Academy of Science, Engineering and Technology, Vol. 58, No. , 2009, pp. 479-488.
[8]  F. Porikli and X. Li, “Traffic Congestion Estimation Us- ing HMM Models without Vehicle Tracking,” IEEE In- telligent Vehicles Symposium, Parma, 14-17 June 2004, pp. 188-193. doi:10.1109/IVS.2004.1336379
[9]  W. Pattara-Atikom and P. Pongpaibool, “Estimating Road Traffic Congestion Using Vehicle Velocity,” Proceeding of 6th International Conference on Telecommunications, Chengdu, June 2006, pp. 1001-1004.
[10]  B. Krause and C. von Altrock, “Intelligent Highway by Fuzzy Logic: Congestion Detection and Traffic Control on Multi-Lane Roads with Variable Road Signs,” 5th In- ternational Conference on Fuzzy Systems, New Orleans, 8-11 September 1996, pp. 1832-1837. doi:10.1109/FUZZY.1996.552649
[11]  A. S. Alfa, “Understanding Urban Traffic Congestion dur- ing Peak Periods,” Proceedings of International Confer- ence on Road and Road Transport Problems (ICORT-88), Roorkee, 12-15 December 1988, pp. 518-527.
[12]  L. Jia and C. Li, “Congestion Evaluation from Traffic Flow Information Based on Fuzzy Logic,” IEEE Intelligent Transportation Systems, Vol. 1, 2003, pp. 50-53.
[13]  P. Mitra, “ANFIS Based Automatic Voltage Regulator with Hybrid Learning Algorithm,” International Journal of Advances in Soft Computing and Applications, Vol. 2, 2010.
[14]  S. M. Seyedhoseini, “Application of Adaptive Neuro-Fuz- zy Inference System in Measurement of Supply Chain Agility: Real Case Study of a Manufacturing Company,” African Journal of Business Management, Vol. 4, No. 1, 2010, pp. 83-96.
[15]  T. O. S. Hanafy, “A Modified Algorithm to Model Highly Nonlinear System,” Journal of American Science, Vol. 6, No. 12, 2010, pp. 747-759.
[16]  T. Takagi and M. Sugeno, “Derivation of Fuzzy Control Rules from Human Operator’s Control Actions,” Pro- ceedings of IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, Mar- seilles, 19-21 July 1983, pp. 55-60.
[17]  A. P. Paplinski, “Adaptive Neuro-Fuzzy Inference Sys- tem (ANFIS),” 20 May 2005.
[18]  L. A. Zadeh, “Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 3, No. 1, 1973, pp. 28-44.
[19]  N. Patchanee, P. Tangamchit and P. Pongpaibool, “Road Traffic Estimation from a GPS-Equipped Car Using Fuz- zy Logic,” Proceeding of 29th Electrical Engineering Con- ference, Chonburi, 9-10 November 2006, pp. 1081-1084.

Full-Text

comments powered by Disqus