Intersection crash prediction modeling with macro-level data from various geographic units.
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.
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States. Electronic address: Jaeyoung.Lee@ucf.edu.
Jaeyoung
J
Lee
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States.
Mohamed
M
Abdel-Aty
Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States.
Qing
Q
Cai
D000063:Accidents, Traffic; D000367:Age Factors; D001642:Bicycling; D019352:Censuses; D003710:Demography; D004779:Environment Design; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D000069636:Pedestrians; D011156:Population Density; D012111:Residence Characteristics; D012449:Safety; D016138:Walking
Crash prediction model; Macro-level traffic safety; Micro-level traffic safety; Random-effects model; Safety performance function; Zonal effect