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Area of Science:

  • Traffic Engineering
  • Transportation Safety
  • Data Science

Background:

  • Traditional traffic safety analyses rely on aggregated data (e.g., annual average daily traffic and annual crash counts).
  • This aggregated approach overlooks the dynamic influence of real-time traffic conditions like speed, volume, and density on safety outcomes.
  • Existing models often fail to capture the time-varying nature of traffic flow and its impact on crash occurrence.

Purpose of the Study:

  • To evaluate the relationship between traffic crashes and traffic flow quality at various temporal aggregation levels.
  • To compare the predictive performance of crash models using different data aggregation intervals (15-minute, hourly, annual).
  • To assess the impact of incorporating speed data and compare sensor-based speeds with private-sector probe speed data.

Main Methods:

  • Utilized continuous count station data and private-sector probe data from rural and urban freeways in Virginia.
  • Developed and contrasted crash prediction models incorporating traffic volume, speed, and geometric variables at 15-minute, hourly, and annual aggregation levels.
  • Assessed model performance improvements with the inclusion of speed data and compared different speed data sources.

Main Results:

  • Models using average hourly volume and speed data significantly improved crash predictions compared to annual models lacking speed information.
  • Mean absolute prediction error decreased by 11% for rural and 20% for urban models when comparing annual average daily traffic-based models to hourly volume models.
  • Models using private-sector probe speed data showed substantial improvements (10% rural, 20% urban) over annual average daily traffic models, demonstrating their viability.

Conclusions:

  • Hourly traffic data, including speed and volume, enhances crash prediction accuracy over traditional annual methods.
  • Private-sector probe speed data offers a viable alternative for developing robust crash prediction models, even with a slight performance difference compared to sensor data.
  • The findings support the integration of real-time traffic data for more effective traffic safety management and analysis.