Citation

Mathew S, Pulugurtha SS, Duvvuri S. Accid. Anal. Prev. 2022; 168: e106615.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.aap.2022.106615

PMID

35219106

Abstract

Motor vehicle crashes are one of the leading causes of teen deaths worldwide. It is important to assess the environment and identify the risk factors influencing teen crashes for planning strategies and improving their safety. This research, therefore, focuses on exploring the effect of road network, demographic, and land use characteristics to compute teen crash frequency. Data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA were considered for the evaluation. Data related to teen crashes were obtained from the Highway Safety Information System (HSIS). Demographic and land use characteristics were extracted around each selected road segment using two different buffer widths (0.25 miles and 0.5 miles). Teen crash frequency of each road segment was used as the dependent variable. The generalized linear models with the negative binomial distribution (GLM-based NB model) and geographically weighted negative binomial regression models (GWNBR and GWNBRg) were developed and compared. The annual average daily traffic (AADT), light commercial land use, number of household units, and number of pupils enrolled in public or private high schools are significant explanatory variables influencing teen crash frequency. Both methods have good predictive capabilities and can be used to compute teen crash frequency. However, the GWNBR and GWNBRg better capture the spatial dependency and spatial heterogeneity of associated risk factors influencing teen crash frequency.

Language: en

Keywords

Driver; Negative binomial regression; Geographically weighted regression; Crash; Teen