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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 patterns are applied on the sensor input in real-time format to classify train traction and reduce the error/risk of classification that may cause service interruptions and incidents. The model can enable us to manage a number of state natures (i.e., spin/slip, normal, and slide), features (i.e., delta speed and train speed), and prior knowledge traction conditions. This model engine can be implemented in any programming language in onboard or embedded computers. As a result, the impact of noisy sensors (inaccurate data) and its delays in such a hard real-time control system is mitigated. This conceptual model is applied to a case study with promising results for target and simulation systems.