Please use this identifier to cite or link to this item:
|Title:||Adaptive neural network models for automatic incident detection on freeways||Authors:||Srinivasan, D.
|Keywords:||Adaptive neural networks
Multi-layer feed-forward neural network
Probabilistic neural network
|Issue Date:||Mar-2005||Citation:||Srinivasan, D., Jin, X., Cheu, R.L. (2005-03). Adaptive neural network models for automatic incident detection on freeways. Neurocomputing 64 (1-4 SPEC. ISS.) : 473-496. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neucom.2004.12.001||Abstract:||Automated incident detection (AID) is an essential component of an Advanced Traffic Management and Information Systems (ATMIS), which provides round the clock incident detection, and helps initiate the required action in case of an accident or incident. This paper evaluates three promising neural network models: multi-layer feed-forward neural network (MLF), basic probabilistic neural network (BPNN) and constructive probabilistic neural network (CPNN) for their incident detection performance. An important consideration in neural network-based incident detection systems is the deployment of a trained neural network on traffic systems with considerably different driving conditions. The models were developed and tested on an original freeway site in Singapore, and tested on a new freeway site in the US for their adaptability. The paper presents comparative evaluation in terms of their classification accuracy, adaptability, and network size. Results indicate that although the MLF model gives excellent classification results on the development site, the CPNN model outperforms the other two in terms of its adaptability and flexible structure. The results suggest that CPNN model has the highest potential for use in an operational automatic incident detection system for freeways. © 2004 Elsevier B.V. All rights reserved.||Source Title:||Neurocomputing||URI:||http://scholarbank.nus.edu.sg/handle/10635/50668||ISSN:||09252312||DOI:||10.1016/j.neucom.2004.12.001|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 30, 2020
WEB OF SCIENCETM
checked on Nov 30, 2020
checked on Dec 1, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.