Modeling the Spatial Effects of Land-Use Patterns on Traffic Safety Using Geographically Weighted Poisson Regression

Abstract

This study aimed to investigate how land-use pattern affects crash frequency at traffic analysis zone (TAZ) level. Traffic, road network, land use, population and crash data were collected from Los Angeles County, California in 2014. K-means clustering analysis was first conducted to divide land use at each TAZ into five different patterns. Geographically weighted Poisson regression (GWPR) models were then developed to investigate the associations between crash counts and land-use patterns. The elasticity was calculated to compare the safety effect of each explanatory factor across different patterns. The results of this study indicated that land use combinations at TAZs can be divided into different patterns using land-use mix and proportions of different land use types, and that each land use combination can be assigned with a certain safety level. The effects of contributing factors on crash frequency are different across different land-use patterns. The results suggest that proper combinations of different land uses can improve safety performance at the urban and road network planning stage.