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. 
Jin, X.
Cheu, R.L. 
Keywords: Adaptive neural networks
Incident detection
Multi-layer feed-forward neural network
Network pruning
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.
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
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


checked on Nov 30, 2020

Page view(s)

checked on Dec 1, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.