A pedestrian serious injury risk prediction method based on posted speed limit

In this study, an optimal model based on the best combination of risk factors according to the Akaike information criterion (AIC) was developed. Additionally, a secondary GPS model using only crash site characteristics that can be derived from GPS coordinates from the crash scene was also developed. The optimal model is based on site and environmental conditions that could be derived from GPS data (posted speed limit, distance from crash site, natural lighting conditions, road geometry, road horizontal alignment and road vertical alignment) as well as pedestrian age/gender, driver age/gender and vehicle model year. The second model only included features that could be derived from GPS data. The optimal model was reasonable in accuracy and gave an under-triage rate of 10% when the injury threshold was set to 15%, with a corresponding over-triage rate of around 60%. The GPS model, despite not being as accurate as the optimal model may be adequate in the absence of all the risk factors required for the optimal model, requiring an injury threshold of 20% to give an under-triage rate of 10%, with the corresponding over-triage rate being around 70%. Both models can potentially be used for serious injury risk prediction (SIRP) for pedestrians involved in a collision with a vehicle.
Source: Accident Analysis and Prevention - Category: Accident Prevention Source Type: research

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