@article{ref1,
title="An approach-based crash analysis and appraisal model for safety design and management",
journal="Journal of the Eastern Asia Society for Transportation Studies",
year="2005",
author="Wei, KY and Hwang, Kevin P.",
volume="6",
number="",
pages="3713-3727",
abstract="This research focuses on examining the interacting relationship between vehicle crashes and road engineering factors. The back-propagation artificial neural network (BPN) method with feed-forward structure is used to develop the model. Each intersection is decomposed into several approach sets to establish the proposed microscopic relationship with vehicle crashes. A total of 1,225 records at 69 primary/secondary intersections in Tainan, Taiwan, were used to calibrate the bi-level models of 11 crash types. First is a distinguish model to tell if an approach set will have crash; the second is a prediction model to forecast crash numbers by type for each approach set. Error rates for all distinguish models are under 15% with an average of 6.1%. Error rates for all prediction models are under 25% with an average of 6.7%. An overall RMS is 0.1617. Practical field test also proves model's validity.<p />",
language="",
issn="1341-8521",
doi="",
url="http://dx.doi.org/"
}