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Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.

Qi Shi1, Mohamed Abdel-Aty1, Rongjie Yu2

  • 1Department of Civil, Environmental and Construction Engineering, University of Central Florida, Engineering II-215, Orlando, FL 32816, United States.

Accident; Analysis and Prevention
|January 2, 2016
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Summary

Analyzing urban expressway safety, this study found that unprocessed traffic speed data, not capped at the speed limit, best predicts crash likelihood. Lower speeds and higher speed variations significantly increase crash risk.

Keywords:
Automatic Vehicle IdentificationBayesian inferenceMulti-level modelRandom parametersUrban expressway safety

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Area of Science:

  • Traffic Safety Engineering
  • Transportation Science
  • Statistical Modeling

Background:

  • Crash frequency modeling is crucial for traffic safety evaluation.
  • Understanding crash mechanisms requires analyzing traffic flow and roadway geometry.
  • Urban expressways present unique safety challenges due to complex interactions.

Purpose of the Study:

  • To develop a multi-level Bayesian framework for identifying crash contributing factors on an urban expressway.
  • To compare the effectiveness of different traffic data types (processed vs. unprocessed) in crash prediction.
  • To account for hierarchical data structures and heterogeneity in traffic and geometric data.

Main Methods:

  • Utilized a multi-level Bayesian framework incorporating Automatic Vehicle Identification (AVI) data and road geometry.
  • Incorporated both processed (speed capped) and unprocessed (original speed) traffic data.
  • Constructed and compared multi-level, random parameters, and Negative Binomial models under Bayesian inference.

Main Results:

  • Unprocessed traffic speed data demonstrated superior performance in crash frequency modeling.
  • Both multi-level and random parameters models significantly outperformed the standard Negative Binomial model.
  • Models with random parameters achieved the best overall model fit, indicating significant heterogeneity.

Conclusions:

  • Lower speeds and higher speed variations are significant contributing factors to increased crash likelihood on urban expressways.
  • Roadway geometric features, such as auxiliary lanes and horizontal curvature, also play a significant role in crash occurrence.
  • The proposed multi-level Bayesian framework effectively identifies key crash determinants, highlighting the importance of raw traffic data.