@article{ref1,
title="Injury accident prediction models for signalized intersections",
journal="Transportation research record",
year="1988",
author="Lau, Michael Yiu-Kuen and May, Adolf D., Jr",
volume="1172",
number="",
pages="58-67",
abstract="An intuitive methodology for developing accident prediction models for signalized intersections based on the Traffic Accident Surveillance and Analysis System (TASAS) in California is illustrated. A fairly new grouping and classifying technique called Classification and Regression Trees (CART) was used as a building block for developing injury accident models. The proposed methodology is a three-level procedure with a &quot;tree&quot; structure for easy interpretation and application. Macroscopic-type models for injury accidents per year are derived, and the following factors have been found to be significant: traffic intensity, proportion of cross street traffic, intersection type, signal type, number of lanes on cross streets and main streets, and left turn arrangements. On the basis of the results, it is also apparent that the models derived from the proposed methodology and TASAS provide more intuition and flexibility than the existing models used in California and other models derived from both site observations and accident record systems.     Record URL:        http://onlinepubs.trb.org/Onlinepubs/trr/1988/1172/1172-007.pdf<p /><p>Language: en</p>",
language="en",
issn="0361-1981",
doi="",
url="http://dx.doi.org/"
}