Data mining the Kansas traffic-crash database

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, 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.; 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. They are: (1) Total Crash Rate (TCR) [Injury + Fatal + Property Damage Only]; (2) Injury Crash Rate (ICR) [Disabling Injury + Possible Injury + Non-Incapacitating Injury]; (3) Severe Injury Crash Rate (SICR) [Disabling + Fatal]; (4) Fatal Crash Rate (FCR) [Fatal].; The models developed for each of the road networks are unique and show that geometric variables and traffic have a significant impact on the crash behavior. The models developed in this study would be utilized by Kansas Department of Transportation in evaluating roadway design features, reconstruction impacts and to make decisions in regard to future traffic planning operations. This study used crash rates to develop an investment index tool which can be used by engineers/planners for justifying investment of funds on smaller networks which do not make it to the top of high priority locations for reconstruction impacts.; Sensitivity analysis was performed on all the geometric variables in the models. It has been found that all the continuous variables have different effects on different networks. It is very difficult to generalize the behavior of a particular variable. Same results were observed for categorical variables too.; Vehicle Type, Driver age and seat belt use have also been studied and it has been found that Driver Age Group (18-20) has the highest involvement in crashes on all road networks. Passenger cars have the highest crash involvement among vehicle types and among all vehicle types; bus drivers have the highest seat belt compliance for all networks.; This research serves as a starting point to demonstrate the use of artificial neural networks to develop crash rate prediction models that could present useful insight to the potential corresponding safety and traffic operation performance.