A new approach for calibrating safety performance functions. (October 2018) - Farid, Ahmed; Abdel-Aty, Mohamed; Lee, Jaeyoung
Highlights: Safety performance functions (SPFs) are calibrated to multiple states' conditions. The K nearest neighbor (KNN) data mining method is proposed to calibrate the SPFs. The KNN method is compared with those of two SPF calibration methods. As per the results, the proposed method outperforms the other two. Abstract: Safety performance functions (SPFs) are statistical regression models used for estimating crash counts by roadway facility classification. They are required for identifying high crash risk locations, assessing the effectiveness of safety countermeasures and comparing road designs in terms of safety. Roadway agencies may opt to develop local SPFs or adopt them from elsewhere such as the national Highway Safety Manual (HSM), provided by the American Association of State Highway and Transportation Officials. The HSM offers a simple technique to calibrate its SPFs to conditions of specific jurisdictions. A more recent calibration technique, also known as the calibration function, is similar to that of the HSM. In this research, we develop SPFs of total crashes for rural divided multilane highway segments in four states. The states are Florida, Ohio, California and Washington. We also calibrate each SPF to each state using the HSM calibration method and the calibration function. Furthermore, we propose the use of the K nearest neighbor data mining method for calibrating SPFs. According to the goodness of fit (GOF) results, our proposed calibration method performs better than the other two methods.