Modeling the Spatial Effects of Land-Use Patterns on Traffic Safety Using Geographically Weighted Poisson Regression
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Modeling the Spatial Effects of Land-Use Patterns on Traffic Safety Using Geographically Weighted Poisson Regression Chengcheng Xu 1,2,3 & Yuxuan Wang 1,2,3 & Wei Ding 1,2,3 & Pan Liu 1,2,3 Accepted: 10 September 2020/ # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Kmeans 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. Keywords Land use patterns . Crash frequency . Traffic safety . K-means clustering .
Geographically weighted Poisson regression
* Chengcheng Xu [email protected] Yuxuan Wang [email protected] Wei Ding [email protected] Pan Liu [email protected] Extended author information available on the last page of the article
Xu C. et al.
1 Introduction With the increasing damages and casualties resulting from crashes, growing attentions have been given to proactively incorporating traffic safety at the stage of lane use and traffic planning. Although urban land-use morphology (ULM) does not have direct impacts on traffic safety, land use planning decisions can influence demographic/ socioeconomic characteristics of an area, and hence traffic volumes and crashes (Pulugurha et al. 2013). Therefore, proper combinations of different land uses strategy can be helpful for preventing and reducing crashes (Wedagama et al. 2006). Understanding the associations of land-use morphology and traffic safety are of important values for incorporating traffic safety consideration into urban planning. A number of studies have been conducted to investigate the impacts of land use characteristics on traffic safety. In these studies, different parameters were proposed to measure the characteristics of land-use patterns, such as the land-use proportion (Wedagama et al. 2006), the areas of different land uses (Wier et al. 2009), and the number of schools (Ng et al. 2002). Kim and Yamashita (2002) conducted an empirical analysis to compare percentage of crash counts across different land-use types and found that commercial land is the most hazardous. Th
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