Pedestrian injury analysis with consideration of the selectivity bias in linked police-hospital data

Evaluation of crash-related injuries by medical specialists in hospitals is believed to be more exact than rather a cursory evaluation made at the crash scene. Safety analysts sometimes reach for hospital data and use them in combination with the police crash data. One issue that needs to be addressed is the, so-called, selectivity (or selection) bias possible when data used in analysis are not coming from random sampling. If not properly addressed, this issue can lead to a considerable bias in both the model coefficient estimates and the model predictions. This paper investigates pedestrian injury severity factors using linked police-hospital data.A bivariate ordered probit model with sample selection is used to check for the presence of the selectivity bias and to account for it in the MAIS estimates on the Maximum Abbreviated Injury Scale (MAIS). The presence of the sample selection issue has been confirmed. The selectivity bias is considerable in predictions of low injury levels.The pedestrian injury analysis identified and estimated several severity factors, including pedestrian, road, and vehicle characteristics. Male and older pedestrians were found to be particularly exposed to severe injuries. Rural roads and high-speed urban roads appear to be more dangerous for pedestrians, particularly when crossing such roads. Crossing a road between intersections was found to be particularly dangerous behavior. The size and weight of the vehicle involved in a pedestrian crash were also found to have an effect on the pedestrian injury level. The relevant safety countermeasures that may improve pedestrian safety have been proposed.

► We analyze severity of pedestrian injuries using linked police-hospital data.
► Sample selection bias is present in ordered probit model estimates.
► Sample selection bias is mitigated with a bivariate ordered probit model.
► Factors of pedestrian injury severity are estimated with the bivariate model.