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原文传递 Data Mining the Kansas Traffic-Crash Database. Summary

题名：	Data Mining the Kansas Traffic-Crash Database. Summary
作者：	Najjar, Y.; Mandavilli, S.
关键词：	Road networks##Motor vehicle accidents##Accident prediction models##Kansas##Aged drivers##Accident rates##Highway safety##Data collection##Traffic accidents##Seat belt usage##Accident analysis##
摘要：	Traffic crashes results from the interaction of different parameters which includes highway geometrics, traffic characteristics and human factors. Geometric variables include number of lanes, lane width, median width, shoulder width, roadway section length, and shoulder width while traffic characteristics include AADT, Percentage of Heavy Vehicles and Speed. The effect of these parameters can be correlated by crash prediction models that predict crash rates at particular roadway section. Transportation Agencies and State Departments of Transportation are continuously faced with decisions concerning the safety of highways. The evaluation and comparison of alternative long-range highway plans should include the safety implications of respective plans. The commonly available models for safety analysis are crash prediction models. By performing an in-depth analysis of crash databases and developing crash rate prediction models, better decisions can be taken in regard to future traffic planning operations. The main objective of this study is to utilize artificial neural network techniques and develop crash rate prediction models for Kansas road networks. Six networks have been studied and crash prediction models for each network have been developed. Four crash rate categories have been considered in this study.
总页数：	2
报告类型：	科技报告