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Validating traffic simulation for crash risk assessment using field crash data.

Maria G Oikonomou1, George Yannis1

  • 1National Technical University of Athens, Department of Transportation Planning and Engineering, 5 Iroon Polytechniou St., GR-15773 Athens, Greece.

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|September 10, 2025
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Summary
This summary is machine-generated.

Traffic simulation accurately predicts road crash risk with 87.7% accuracy, enhancing safety assessments. This reliable framework aids interventions where direct observation is difficult.

Keywords:
Comparative analysisCrash dataRoad safetySimulation validationTraffic simulation

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Area of Science:

  • Road safety engineering
  • Traffic simulation modeling
  • Data analysis and pattern recognition

Background:

  • Advancements in traffic simulation tools necessitate accurate and reliable safety assessments.
  • Bridging the gap between simulation models and real-world safety observations is crucial for robust methodologies.
  • Comparative analysis of simulated and real-world traffic safety metrics aids in identifying safety patterns.

Purpose of the Study:

  • To conduct a comprehensive comparative analysis of traffic safety metrics from simulated and real-world data.
  • To employ clustering techniques for identifying distinct road safety patterns.
  • To validate the accuracy and reliability of traffic simulation in predicting road crash risk.

Main Methods:

  • Utilized Aimsun Next for traffic simulation and the Surrogate Safety Assessment Model (SSAM) to extract traffic conflicts and assess crash risk.
  • Analyzed real-world crash data from the Hellenic Statistical Authority (ELSTAT) (2017-2019), including injury details, vehicles, and crash counts.
  • Compared simulation metrics (flow, capacity, crash risk) with observational data (speed limits, road lengths, injuries, crash counts).

Main Results:

  • Identified two distinct clusters: roads with low and high crash risks, showing minimal overlap.
  • Achieved approximately 87.7% accuracy in classifying road crash risk using traffic simulation data.
  • Highlighted the critical need for thorough calibration, as inaccurately predicted roads lacked sufficient traffic data.

Conclusions:

  • Validated a framework for traffic safety assessment applicable in scenarios where direct observation is impractical.
  • Demonstrated the reliability of traffic simulation for predicting road crash risk.
  • Emphasized the framework's potential to enhance road safety and guide interventions in dynamic traffic environments.