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A new approach for calibrating safety performance functions.

Ahmed Farid1, Mohamed Abdel-Aty1, Jaeyoung Lee1

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This study develops and calibrates safety performance functions (SPFs) for rural highways. A novel K nearest neighbor method significantly improves SPF calibration accuracy compared to existing techniques.

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Area of Science:

  • Transportation Engineering
  • Traffic Safety Research
  • Statistical Modeling

Background:

  • Safety Performance Functions (SPFs) are crucial for road safety analysis, including identifying high-risk locations and evaluating safety interventions.
  • Roadway agencies utilize SPFs, often adopting national models like those from the Highway Safety Manual (HSM), and may calibrate them to local conditions.
  • Existing calibration methods, such as the HSM technique and the calibration function, offer ways to adapt SPFs but may have limitations.

Purpose of the Study:

  • To develop and calibrate SPFs for total crashes on rural divided multilane highway segments across four diverse states: Florida, Ohio, California, and Washington.
  • To evaluate the performance of the Highway Safety Manual (HSM) calibration method and the calibration function for these SPFs.
  • To introduce and assess a new calibration technique using the K nearest neighbor (KNN) data mining method for improving SPF accuracy.

Main Methods:

  • Development of SPFs for total crashes specific to rural divided multilane highways in Florida, Ohio, California, and Washington.
  • Application of the Highway Safety Manual (HSM) calibration method to adapt the developed SPFs to each state's conditions.
  • Implementation and evaluation of the calibration function method for SPF calibration.
  • Proposal and testing of the K nearest neighbor (KNN) data mining technique as an alternative SPF calibration method.

Main Results:

  • New SPFs for total crashes were developed for rural divided multilane highway segments in the studied states.
  • Both the HSM calibration method and the calibration function were applied, with performance varying across states.
  • The K nearest neighbor (KNN) data mining method demonstrated superior performance in SPF calibration based on goodness of fit (GOF) metrics compared to the other two methods.

Conclusions:

  • The K nearest neighbor (KNN) method offers a more accurate and effective approach for calibrating safety performance functions (SPFs) for rural divided multilane highways.
  • The findings suggest that localized calibration, particularly using advanced data mining techniques, can significantly enhance the reliability of SPFs for traffic safety management.
  • This research provides valuable insights for transportation agencies seeking to improve their road safety analysis and decision-making processes through better SPF calibration.