Incorporating Neural Network Traffic Prediction into Freeway Incident Detection

Because of their superior capabilities in emulating nonlinear systems, neural network models have been applied to traffic prediction with various degrees of success. However, these neural network-based traffic prediction models have not been used for incident detection. On the other hand, it is expe...

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Bibliographic Details
Published in:Transportation research record 1999, Vol.1679 (1), p.101-111
Main Authors: Teng, Hualiang, Martinelli, David R., Taggart, Benjamin T.
Format: Article
Language:English
Subjects:
Applied sciences
Exact sciences and technology
Ground, air and sea transportation, marine construction
Road transportation and traffic
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Summary:Because of their superior capabilities in emulating nonlinear systems, neural network models have been applied to traffic prediction with various degrees of success. However, these neural network-based traffic prediction models have not been used for incident detection. On the other hand, it is expected that the performance of an incident detection algorithm can be improved if an advanced prediction model is used. The development of several traffic prediction models that were then integrated into incident detection algorithms is documented. The traffic prediction models were developed on the basis of three different choices of independent variables, whereas the incident detection algorithms used different decision functions. The results show that a good prediction model can improve the performance of an incident detection algorithm only when the decision function of the algorithm is appropriately chosen.
ISSN:0361-1981
2169-4052
DOI:10.3141/1679-14