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In this paper, a real-time intelligent traction control model for speed sensor vehicles in computer-based transit systems is proposed. Using the Bayesian decision theory, the model analyzes speed sensor data to learn and classify the train traction conditions (i.e., spin/slip, normal, and slide) that are required for studying vehicle motion patterns. The(More)
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