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A safety analysis of a specific location requires the knowledge of "base crash rates", also known in the literature as "expected values", for identifying crash patterns at the study location. Base crash rate models were developed for the following nine crash types: (a) Injury (b) PDO (c) Total (d) Wet (e) Night (f) Rear end (g) Sideswipe (h) Fixed object and (i) Left turn crashes. These models were developed for intersection legs (also called intersection approaches) as the crash behavior of different legs within the same intersection were likely to be different. Base crash rates for the following eight leg types were modeled based on the type of control: (a) Signalized legs at four way signalized intersections (b) Signalized legs at three way signalized intersections (c) No control legs at one way stop controlled intersections (d) Stop controlled legs at one way stop controlled intersections (e) No control legs at two way stop controlled intersections (f) Stop controlled legs at two way stop controlled intersections (g) No control legs at intersections with flashing beacons and (h) Stop controlled legs at intersections with flashing beacons. This study focused on intersections formed by two or more State or US routes. To evaluate the complex interaction among the dependent and independent variables, Automatic Interaction Detection (AID) technique was used. After the completion of AID analysis, stepwise multiple regression technique was used to develop mathematical models for the split groups. It is recommended that the AID and regression models developed in this study (Appendices II and III) be used for estimating statewide base crash rates in Ohio.
A safety analysis of a specific location includes a "base crash rate" analysis for identifying crash patterns at the study location. If the calculated crash rate for any crash type at a particular location is found to be higher than the "base crash rate", then that location is chosen for further study to determine if a safety problem actually exists at that location and, if so, what counter measure(s) can be used. The primary objective of this study was to develop a set of mathematical models to estimate base crash rates for freeways in Ohio. The models estimate the incremental changes in the dependent variables (crash rate for different types of crashes)resulting from changes in the independent variables including geometric features, operational controls, and environmental conditions. The base crash density/rate models were developed for the following crash types: (a)Injury crashes, (b) PDO crashes, (c) Total crashes, (d) Angle crashes, (e) Fixed crashes, (f) Rear end crashes, (g) Sideswipe crashes, (h) Wet road crashes, and (i) Night crashes. To evaluate the complex interaction among the dependent and independent variables, Automatic Interaction Detection (AID) technique was used. After the completion of AID analysis, stepwise multiple regression technique was used to develop mathematical models for the split groups. Finally, the models were validated with the one-third data that was set aside for validation. This study developed state-wide and district-wide base crash density and rate models for freeways. These models are recommended for use by ODOT for evaluating freeway crashes. The results of this study have a high potential of implementation in Ohio.
The overall objectives of this research study may be stated as follows: Determine if surface characteristic measurements can be correlated to wet-pavement crashes in Ohio; Provide improved guidance on the use of ribbed versus smooth tires for pavement surface friction testing in Ohio, including the identification of suggested minimum surface friction numbers associated with each tire type; Provide recommended desirable or target surface friction numbers as a function of site categories and friction demand. Accomplishments of these objectives will help ODOT address their goal of reducing total crashes 10 percent and rear-end crashes by 25 percent by 2015.