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Developing safety performance functions for freeways at different aggregation levels using multi-state microscopic

Jinghui Yuan1, Mohamed Abdel-Aty1, Jingwan Fu1

  • 1Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.

Accident; Analysis and Prevention
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PubMed
Summary
This summary is machine-generated.

Developing Safety Performance Functions (SPFs) using more granular traffic data, like daily and hourly volumes, improves crash prediction accuracy compared to traditional annual methods. Temporal variations significantly impact roadway safety analysis.

Keywords:
Aggregation levelFreewayHigh occupancy vehicle laneMicroscopic traffic detector dataSafety performance function

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Area of Science:

  • Transportation Engineering
  • Traffic Safety Analysis
  • Roadway Safety Evaluation

Background:

  • Safety Performance Functions (SPFs) are crucial for roadway safety evaluation.
  • Traditional SPFs often use aggregated traffic data (e.g., Annual Average Daily Traffic - AADT), potentially missing temporal variations.
  • This aggregation can limit the accuracy of crash prediction models.

Purpose of the Study:

  • To develop and compare SPFs at various temporal aggregation levels using microscopic traffic data.
  • To investigate the impact of different traffic characteristics (volume, speed, HOV lanes) on crash frequencies.
  • To assess the transferability of SPFs developed at different aggregation levels across different states.

Main Methods:

  • Developed SPFs using microscopic traffic detector data from California, Florida, and Virginia.
  • Considered five aggregation levels: AAWDHT, AAWEHT, AAWDPT, AADOWT, and AADT.
  • Analyzed the significance of segment length, volume, average speed, and speed standard deviation as exposure and risk variables.

Main Results:

  • Segment length and volume were significant exposure variables across all aggregation levels.
  • Average speed negatively correlated with crashes, while speed standard deviation positively correlated.
  • High Occupancy Vehicle (HOV) lane operation showed a positive association with crash frequency.
  • Models using Annual Average Weekday Peak/Off-peak Traffic (AAWDPT) and Annual Average Day of the Week Traffic (AADOWT) outperformed AADT-based SPFs.
  • SPFs were transferable between Florida and Virginia, but not between California and the other states.

Conclusions:

  • More granular temporal aggregation (AAWDPT, AADOWT) significantly enhances SPF accuracy for crash prediction.
  • Temporal traffic variations and speed dynamics are critical factors in roadway safety.
  • State-specific factors influence the transferability of developed SPFs, necessitating localized model development or adaptation.