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Full data imputation for freeway time-specific safety performance functions' estimation.

Jingwan Fu1, Mohamed Abdel-Aty1, Xin Yan1

  • 1Department of Civil, Environmental, and Construction Engineering, Department of Statistics and Data Science, University of Central Florida (UCF), Orlando, FL 32816-2450, United States.

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Summary
This summary is machine-generated.

This study introduces an iterative imputation method to fill missing traffic data for developing accurate time-specific Safety Performance Functions (SPFs). The method successfully reconstructs traffic patterns, enabling reliable crash prediction models even without complete data.

Keywords:
Data imputationFreewayNegative binomial modelSafety performance functionTime-specific

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Area of Science:

  • Transportation Engineering
  • Traffic Safety Analysis
  • Data Science

Background:

  • Accurate crash frequency predictions require time-specific Safety Performance Functions (SPFs).
  • High-resolution traffic data (volume and speed) is often missing or unarchived in many states, hindering SPF development.
  • Existing methods lack robust solutions for imputing complete traffic datasets.

Purpose of the Study:

  • To propose and validate a novel iterative imputation method for reconstructing missing traffic volume and speed data.
  • To enable the development of time-specific SPFs in states lacking complete traffic data.
  • To assess the accuracy and effectiveness of imputed data in traffic modeling.

Main Methods:

  • Calculated crash rates for 18 states and used One-Way ANOVA to group states with similar crash rates.
  • Developed and tested an iterative imputation method using traffic data from Florida (FL) and Virginia (VA).
  • Validated imputed data against real-collected data using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE).

Main Results:

  • The iterative imputation method successfully captured traffic patterns comparable to real data.
  • MAE for imputed Ln Volume was 2.47 vehicles/segment/3 hours; MAE for imputed Ln AvgSpeed was 1.36 mph in FL.
  • MAPE for imputed Ln Volume was 11.07%; MAPE for imputed Ln AvgSpeed was 7.40% in FL.
  • Time-specific SPFs developed using imputed data achieved 87.1% prediction accuracy for the morning peak model.

Conclusions:

  • The proposed iterative imputation method is effective for reconstructing missing traffic data.
  • Imputed data can be reliably used to develop accurate time-specific Safety Performance Functions.
  • This approach addresses data gaps and facilitates dynamic crash prediction modeling in data-scarce regions.