Linchao Li This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Xu Qu1,2, Jian Zhang1,2 and Bin Ran1,2,3

1Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Jiangsu, P.R. China
2Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Jiangsu, P.R. China
3Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, United States


 

Received: May 10, 2017
Accepted: June 5, 2017
Publication Date: December 1, 2017

Download Citation: ||https://doi.org/10.6180/jase.2017.20.4.01  

ABSTRACT


Traffic incident detection is an essentialpart of the intelligenttransportation systemand attracts lots of attention from researchers in different areas. To reduce the computation and to avoid overfitting of the traditional feed-forward neural network, an extreme machine learning is implemented to detect traffic incidents. Using the real-world data, the proposed model is compared with feed-forward neural network and other two commonly used models by several evaluation criteria. The results indicate the proposed model has the highest accuracy, detection rate and lowest false alarm rate. Moreover, it consumes less time. In conclusion, the performance of the proposed model is better than other benchmark models and suit to detect traffic incidents.


Keywords: Intelligent Transportation System, Extreme Machine Learning, Feed-forward Neural Network, Performance


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