Vehicle classification using GPS data

Vehicle classification information is crucial to transportation planning, facility design, and operations. Traditional vehicle classification methods are either too expensive to be deployed for large areas or subject to errors under specific situations. In this paper, we propose methods to classify vehicles using GPS data extracted from mobile traffic sensors, which is considered to be low-cost especially for large areas of urban arterials. It is found that features related to the variations of accelerations and decelerations (e.g., the proportions of accelerations and decelerations larger than 1 meter per square second, and the standard deviations of accelerations and decelerations) are the most effective in terms of vehicle classification using GPS data. By classifying general trucks from passenger cars, the average misclassification rate is about 1.6% for the training data, and 4.2% for the testing data.

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