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Title:

Integrating Macro and Micro Level Safety Analyses: A Bayesian Approach Incorporating Spatial Interaction

Accession Number:

01657935

Record Type:

Component

Abstract:

Crash frequency analysis is a crucial tool to investigate transportation safety problems. Traditionally, crash frequency analyses have been undertaken at the macro- and micro-levels, independently. If conducted in the same study area, the macro- and micro-level crash analyses should investigate the same crashes but by aggregating the crashes at different levels. Hence, the crash counts at the two levels should be correlated and integrating macro- and micro-level crash frequency analyses in one modeling structure might have the ability to better explain crash occurrence by realizing the effects of both macro- and micro-level factors. This study proposes a Bayesian integrated spatial crash frequency model, which links the crash counts of macro- and micro-levels based on the spatial interaction. In addition, the proposed model considers the spatial autocorrelation of different types of road entities (i.e., segments and intersections) at the micro-level with a joint structure. Two independent non-integrated models for macro- and micro-levels were also estimated separately and compared with the integrated model. The results indicated that the integrated model can provide better model performance for estimating macro- and micro-level crash counts, which validates the concept of integrating the models for the two levels. Also, the integrated model provides more valuable insights about the crash occurrence at the two levels by revealing both macro- and micro-level factors. It is expected that the proposed integrated model can help practitioners implement more reasonable transportation safety plans and more effective engineering treatments to proactively enhance safety.

Supplemental Notes:

This paper was sponsored by TRB committee ANB10 Standing Committee on Transportation Safety Management. Integrating Macro- and Microlevel Safety Analyses: A Bayesian Approach Incorporating Spatial Interaction: This is an alternate title.

Report/Paper Numbers:

18-00144

Language:

English

Authors:

Cai, Qing
Abdel-Aty, Mohamed
Lee, Jaeyoung
Huang, Helai

Pagination:

6p

Publication Date:

2018

Conference:

Transportation Research Board 97th Annual Meeting

Location: Washington DC, United States
Date: 2018-1-7 to 2018-1-11
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

References; Tables

Uncontrolled Terms:

Bayesian approach

Subject Areas:

Highways; Planning and Forecasting; Safety and Human Factors

Source Data:

Transportation Research Board Annual Meeting 2018 Paper #18-00144

Files:

TRIS, TRB, ATRI

Created Date:

Jan 8 2018 10:03AM