Citation

Chung H, Duan Q, Chen Z, Yang Y. Case Stud. Transp. Policy 2023; 12: e100933.

Copyright

(Copyright © 2023, World Conference on Transport Research Society, Publisher Elsevier Publishing)

DOI

10.1016/j.cstp.2022.100933

PMID

unavailable

Abstract

The development of city transportation often promotes prosperity in urban economies, but it can also cause safety issues that adversely affect local residents. It is widely acknowledged that the built environment has a significant impact on traffic accidents. Many studies have shown a link between different land use characteristics and accidents, although these investigations have used different land use characteristic variables, and but few have attempted to allow for spatial heterogeneity. To better understand how urban land use affects the frequency of traffic accidents, this study undertakes a quantitative analysis of the spatial relationship by analyzing the case study of Suzhou Industrial Park, China. We introduce a term frequency-inverse document frequency (TF-IDF) algorithm to identify land use characteristics based on point of interest (POI) data and adopt Geographically Weighted Negative Binomial Regression (GWNBR) model to accomplish this task. This study confirms that local models outperform global models in terms of their explanatory power. The modeling results demonstrate different effects of POI-based land use on traffic accidents and verify the assumption that inherent non-stationarity exists in the estimation of parameters across space.

Language: en

Keywords

Accidents; Built environment; Geographically Weighted Negative Binomial Regression (GWNBR); Land use; Point-Of-Interest (POI); Spatial variation