A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Traffic-incident detection-algorithm based on nonparametric regression
IEEE Transactions on Intelligent Transportation Systems
Real-time hazardous traffic condition warning system: framework and evaluation
IEEE Transactions on Intelligent Transportation Systems
Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data
IEEE Transactions on Intelligent Transportation Systems
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Traditional traffic accident prediction uses long-term traffic data such as annual average daily traffic and hourly volume. In contrast to traditional traffic accident prediction, real-time traffic accident prediction uses real-time traffic data, obtained from inductive loop detectors and usually collected every 20 or 30 seconds, to identify hazardous traffic conditions to potentially prevent the traffic accident occurrence. We aim at identifying traffic patterns leading to traffic accidents and not leading to traffic accidents in this study. Support vector machines (SVM) are used to classify traffic conditions into those two patterns with real-time traffic data. Traffic accident data and its corresponding real-time traffic data are collected from the traffic simulation software TSIS, which is a microscopic traffic simulation software. This is the first time the SVM method is applied for real-time traffic accident prediction. The experimental results show that it is promising for real-time traffic accident prediction by using the support vector machine method.