Comparative analysis of the spatial analysis methods for hotspot identification

Accid Anal Prev. 2014 May:66:80-8. doi: 10.1016/j.aap.2014.01.017. Epub 2014 Jan 29.

Authors

Affiliations

¹ Jiangsu Key Laboratory of Urban ITS Southeast University, Si Pai Lou #2, Nanjing 210096, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou #2, Nanjing 210096, China. Electronic address: seudarwin@gmail.com.
² Jiangsu Key Laboratory of Urban ITS Southeast University, Si Pai Lou #2, Nanjing 210096, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou #2, Nanjing 210096, China. Electronic address: pan_liu@hotmail.com.
³ Jiangsu Key Laboratory of Urban ITS Southeast University, Si Pai Lou #2, Nanjing 210096, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou #2, Nanjing 210096, China. Electronic address: chenjun@seu.edu.cn.
⁴ Jiangsu Key Laboratory of Urban ITS Southeast University, Si Pai Lou #2, Nanjing 210096, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou #2, Nanjing 210096, China. Electronic address: haowang@seu.edu.cn.

PMID: 24530515
DOI: 10.1016/j.aap.2014.01.017

Abstract

Spatial analysis technique has been introduced as an innovative approach for hazardous road segments identification (HRSI). In this study, the performance of two spatial analysis methods and four conventional methods for HRSI was compared against three quantitative evaluation criteria. The spatial analysis methods considered in this study include the local spatial autocorrelation method and the kernel density estimation (KDE) method. It was found that the empirical Bayesian (EB) method and the KDE method outperformed other HRSI approaches. By transferring the kernel density function into a form that was analogous to the form of the EB function, we further proved that the KDE method can eventually be considered a simplified version of the EB method in which crashes reported at neighboring spatial units are used as the reference population for estimating the EB-adjusted crashes. Theoretically, the KDE method may outperform the EB method in HRSI when the neighboring spatial units provide more useful information on the expected crash frequency than a safety performance function does.

Keywords: Empirical Bayesian; Hotspot; Kernel density; Spatial analysis.

Publication types

Comparative Study
Research Support, Non-U.S. Gov't

MeSH terms

Accidents, Traffic / statistics & numerical data*
Bayes Theorem
Environment Design
Humans
Spatial Analysis*