Applying the colocation quotient index to crash severity analyses

Pei Fen Kuo, Dominique Lord

研究成果: Article同行評審


Examining the spatial relationships among crashes of various severity levels is essential for gaining a better understanding of the severity distribution and potential contributing factors to collisions. However, relatively few scholars have focused on analyzing this type of data. Therefore, in this study, we utilized a new index, the colocation quotient, to measure the spatial associations among crashes of various severities that occurred in College Station, Texas. This new method has been widely used to define the colocation pattern of categorized data in various fields, but it has not yet been applied to crash severity data. According to our findings, (1) crashes tended to be at the same injury level as those of neighboring ones, which was most significant for fatal crashes and second most significant for non-injury crashes; (2) the colocation quotient matrix tended to be symmetrical in non-injury crashes versus injury crashes (minor injury, major injury, and fatal); and, (3) DWIs (driving while intoxicated) and hit-and runs did not show a strong pattern. These colocation quotient results could be helpful for predicting crash severity and by providing traffic engineers with more effective traffic safety measures.

期刊Accident; analysis and prevention
出版狀態Published - 2020 二月 1

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Safety, Risk, Reliability and Quality
  • Public Health, Environmental and Occupational Health

指紋 深入研究「Applying the colocation quotient index to crash severity analyses」主題。共同形成了獨特的指紋。