Revisiting Hit-and-Run Crashes: A Geo-Spatial Modeling Method

Abstract

Hit-and-run crashes often delay emergency response and may result in increasing/secondary harms/damages to the victims in the crash. This study revisited hit-and-run crashes using a geo-spatial modeling approach, specifically, Geographically Weighted Regression (GWR), to explore geo-referenced crash data. The data cover motor vehicle crashes (N = 138,529) in Southeast Michigan including 20,813 hit-and-run crashes in 2015. This study presented the results from both traditional regression and GWR models. GWR model results can be mapped in space, and the maps offer visual insights about the spatially varying correlates of hit-and-run crashes that are not available from previous studies. Results from traditional binary logit model are generally consistent with findings in previous studies. For example, hit-and-run is more likely to occur on weekends or during nighttime (especially without street lights on). Driving under impairment (DUI) seems to increase the likelihood of hit-and-run. GWR models also uncovered spatially varying correlates of hit-and-run. For example, DUI crashes in the northwest of the Detroit metropolitan area are associated with an even greater hit-and-run likelihood than those in other parts in this area. In addition, the local socio-economic factors are included in the analysis. Results show that hit-and-run is more likely to occur in census tracts with a higher unemployment rate, a lower household income, a smaller portion of college-educated population, and a greater population density. The study demonstrates a way of making sense of geo-referenced traffic safety data. The geo-spatial modeling method is useful for prioritizing specific geographic regions/corridors for safety improvement countermeasures, and outperforms traditional modeling techniques.