A neural clustering and classification system for sales forecasting of new apparel items
Applied Soft Computing
Modeling the efficiency of top Arab banks: A DEA-neural network approach
Expert Systems with Applications: An International Journal
Profiling blood donors in Egypt: A neural network analysis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A neuro-computational intelligence analysis of the ecological footprint of nations
Computational Statistics & Data Analysis
Recognition of Western style musical genres using machine learning techniques
Expert Systems with Applications: An International Journal
Construct support vector machine ensemble to detect traffic incident
Expert Systems with Applications: An International Journal
En-route security monitoring based on an incident detection algorithm for commercial vehicles
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
A neuro-computational intelligence analysis of the global consumer software piracy rates
Expert Systems with Applications: An International Journal
A hybrid model of partial least squares and neural network for traffic incident detection
Expert Systems with Applications: An International Journal
Automatic traffic incident detection based on nFOIL
Expert Systems with Applications: An International Journal
Hi-index | 0.01 |
This paper proposes a new technique for freeway incident detection using a constructive probabilistic neural network (CPNN). The CPNN incorporates a clustering technique with an automated training process. The work reported in this paper was conducted on Ayer Rajah Expressway (AYE) in Singapore for incident detection model development, and subsequently on I-880 freeway in California, for model adaptation. The model developed achieved incident detection performance of 92% detection rate and 0.81% false alarm rate on AYE, and 91.30% detection rate and 0.27% false alarm rate on I-880 freeway using the proposed adaptation method. In addition to its superior performance, the network pruning method employed facilitated model size reduction by a factor of 11 compared to a conventional probabilistic neural network. A more impressive size reduction by a factor of 50 was achieved after the model was adapted for the new site. The results from this paper suggest that CPNN is a better adaptive classifier for incident detection problem with a changing site traffic environment