Cho Hyung Ki, Min Joon Young, Choi Jong Uk

The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 3, No. 2, pp. 369-380, Mar. 1996
10.3745/KIPSTE.1996.3.2.369,   PDF Download:

Abstract

Inductive Lood Detector(ILD) has been commonly used in collecting traffic data such as occupancy time and non-occupancy time. From the data, the traffic volume and type of passing vehicle is calculated. To provide reliable data for traffic control and plan, accuracy is required in type recognition which can be utilized to determine split of traffic signal and to provide forecasting data of queue-length for over-saturation control. In this research, a new recognition model is suggested for recognizing type of vehicle form the collected data obtained thorough ILD systems. Two clustering methods, based on statistical algorithms, and one neural network clustering method were employed to test the reliability and accuracy of the methods. In a series of experiments, it was found that the new model can greatly enhance the reliability and accuracy of type recongition rate, much higher that conventional approaches. The model modifies the neural network clustering method and enhances the recongtion accuracy by iteratively applying the algorithm until no more unclustered data remains.

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