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

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DOI: 10.4236/jtts.2012.21006    5,902 Downloads   10,411 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

H. Shankar, P. Raju and K. Rao, "Multi Model Criteria for the Estimation of Road Traffic Congestion from Traffic Flow Information Based on Fuzzy Logic," Journal of Transportation Technologies, Vol. 2 No. 1, 2012, pp. 50-62. doi: 10.4236/jtts.2012.21006.

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