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Road network safety evaluation using Bayesian hierarchical joint model.

Jie Wang1, Helai Huang1

  • 1Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China.

Accident; Analysis and Prevention
|March 6, 2016
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Summary
This summary is machine-generated.

This study introduces a Bayesian hierarchical joint model to improve road network safety evaluations. The model enhances traffic safety planning by considering micro and macro variables, outperforming previous methods.

Keywords:
Bayesian hierarchical joint modelMacro-level variablesMicro-level variablesRoad network crash predictionSafety evaluation

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Area of Science:

  • Transportation Engineering
  • Civil Engineering
  • Urban Planning

Background:

  • Transportation system performance is typically evaluated on safety and efficiency.
  • Road network planning has historically prioritized capacity and efficiency over safety.
  • Integrating safety into the road network planning stage is crucial for comprehensive transportation development.

Purpose of the Study:

  • To develop a Bayesian hierarchical joint model for road network safety evaluation.
  • To enable transportation planners to incorporate traffic safety considerations into road network planning.
  • To establish relationships between road network risk and various micro and macro-level variables.

Main Methods:

  • Development of a Bayesian hierarchical joint model.
  • Inclusion of micro-level variables (road entities, traffic volume) and macro-level variables (socioeconomic, trip generation, network density).
  • Consideration of network spatial correlation between intersections and road segments.
  • Comparison with a previous joint model and a negative binomial model using a selected road network.

Main Results:

  • The proposed hierarchical joint model demonstrated superior goodness-of-fit and predictive performance compared to the joint and negative binomial models.
  • Random effects at the Traffic Analysis Zone (TAZ) level were found to be significant.
  • Spatial correlation between intersections and adjacent road segments was also significant.

Conclusions:

  • The hierarchical joint model is a reasonable and effective approach for crash prediction and analysis in road networks.
  • The significance of TAZ-level random effects and spatial correlation supports the use of this model.
  • The hierarchical joint model offers a valuable alternative for road-network-level safety modeling.