Freeway automatic incident detection using learning models - backpropagation, SVM and FuzzyARTMAP

Daehyon Kim

International Journal of Urban Sciences, 2013, vol. 17, issue 1, 109-116

Abstract: The cost of delays caused by incidents is significant and a traffic management center needs to quickly detect and remove them from the freeway to reduce the impact of an incident. A study on quick and efficient automatic incident detection has been an important field of transportation research and many algorithms based on loop detector data have been developed for automatic incident detection on freeways. However, several simple and familiar algorithms, such as the California algorithms, the McMaster algorithm, and the Minnesota algorithm, have had limited success in their overall performance in terms of detection rate, false alarm rate, and mean time to detect an incident. Recently, detection algorithms based on the neural network models are known as the one of the most popular and efficient approaches for real-time automatic incident detection. Moreover, many researchers have shown that the neural network models were much more efficient than various other previous models. However, various types of neural network models and learning mechanisms have been developed and it is an important task to choose the best model in order to achieve the best performance in automatic incident detection. In a previous study, support vector machine (SVM), which is based on statistical learning theory, has been shown to be more efficient than Backpropagation algorithm, easily the most useful and popular neural network. In this paper, FuzzyARTMAP, which is a combination of fuzzy logic and adaptive resonance theory, has been used for automatic incident detection. Experiments have been carried out using real world freeway incident and incident-free data collected on freeway segments located in Seoul, Korea. A comparative study with three models, Backpropagation, SVM, and FuzzyARTMAP, has been conducted to assess the incident detection performance in terms of detection rate and false alarm rate. Experimental results in this study showed that FuzzyARTMAP might provide better performance than the Backpropagation and SVM models for automatic incident detection.

Date: 2013
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DOI: 10.1080/12265934.2013.766502

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