Intersection Crash Prediction Modeling With Macro-Level Data From Various Geographic Units

Keywords

Crash prediction model; Macro-level traffic safety; Micro-level traffic safety; Random-effects model; Safety performance function; Zonal effect

Abstract

There have been great efforts to develop traffic crash prediction models for various types of facilities. The crash models have played a key role to identify crash hotspots and evaluate safety countermeasures. In recent, many macro-level crash prediction models have been developed to incorporate highway safety considerations in the long-term transportation planning process. Although the numerous macro-level studies have found that a variety of demographic and socioeconomic zonal characteristics have substantial effects on traffic safety, few studies have attempted to coalesce micro-level with macro-level data from existing geographic units for estimating crash models. In this study, the authors have developed a series of intersection crash models for total, severe, pedestrian, and bicycle crashes with macro-level data for seven spatial units. The study revealed that the total, severe, and bicycle crash models with ZIP-code tabulation area data performs the best, and the pedestrian crash models with census tract-based data outperforms the competing models. Furthermore, it was uncovered that intersection crash models can be drastically improved by only including random-effects for macro-level entities. Besides, the intersection crash models are even further enhanced by including other macro-level variables. Lastly, the pedestrian and bicycle crash modeling results imply that several macro-level variables (e.g., population density, proportions of specific age group, commuters who walk, or commuters using bicycle, etc.) can be a good surrogate exposure for those crashes.

Publication Date

5-1-2017

Publication Title

Accident Analysis and Prevention

Volume

102

Number of Pages

213-226

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.aap.2017.03.009

Socpus ID

85015797253 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85015797253

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