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Title: Automated detection of lane-blocking freeway incidents using artificial neural networks
Authors: Cheu, R.L. 
Ritchie, S.G.
Issue Date: Dec-1995
Citation: Cheu, R.L.,Ritchie, S.G. (1995-12). Automated detection of lane-blocking freeway incidents using artificial neural networks. Transportation Research Part C 3 (6) : 371-388. ScholarBank@NUS Repository.
Abstract: A major source of urban freeway delay in the U.S. is non-recurring congestion caused by incidents. The automated detection of incidents is an important function of a freeway traffic management center. A number of incident detection algorithms, using inductive loop data as input, have been developed over the past several decades, and a few of them are being deployed at urban freeway systems in major cities. These algorithms have shown varying degrees of success in their detection performance. In this paper, we present a new incident detection technique based on artificial neural networks (ANNs). Three types of neural network models, namely the multi-layer feedforward (MLF), the self-organizing feature map (SOFM) and adaptive resonance theory 2 (ART2), were developed to classify traffic surveillance data obtained from loop detectors, with the objective of using the classified output to detect lane-blocking freeway incidents. The models were developed with simulation data from a study site and tested with both simulation and field data at the same site. The MLF was found to have the highest potential, among the three ANNs, to achieve a better incident detection performance. The MLF was also tested with limited field data collected from three other freeway locations to explore its transferability. Our results and analyzes with data from the study site as well as the three test sites have shown that the MLF consistently detected most of the lane-blocking incidents and typically gave a false alarm rate lower than the California, McMaster and Minnesota algorithms currently in use. © 1996.
Source Title: Transportation Research Part C
ISSN: 0968090X
Appears in Collections:Staff Publications

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