A systematic review and meta-analysis of data linkage between motor vehicle crash and hospital-based datasets.

Motor vehicle crash data linkage has emerged as a vital tool to better understand the injury outcomes and the factors contributing to crashes. This systematic review and meta-analysis aims to explore the existing knowledge on data linkage between motor vehicle crashes and hospital-based datasets, summarize and highlight the findings of previous studies, and identify gaps in research. A comprehensive and systematic search of the literature yielded 54 studies for a qualitative analysis, and 35 of which were also considered for a quantitative meta-analysis.

Data linkage for crash outcome assessment: Linking police-reported crashes, emergency response data, and trauma registry records.

Introduction: Traffic crash reports lack detailed information about emergency medical service (EMS) responses, the injuries, and the associated treatments, limiting the ability of safety analysts to account for that information. Integrating data from other sources can enable a better understanding of characteristics of serious crashes and further explain variance in injury outcomes. In this research, an approach is proposed and implemented to link crash data to EMS run data, patient care reports, and trauma registry data.

Do EMS times associate with injury severity?

In this study, emergency medical services times, along with other crash-related explanatory variables, have been used to investigate influential factors on injury severity. To overcome the complexity of emergency medical services times impact on crash outcome, the interaction effects of EMS times and injury location on the body were also investigated in a separate model. This study utilized the linked data of police-reported crash data and emergency medical services runs, including 2192 crash injuries that transferred to hospital.

Identification of safety-critical events using kinematic vehicle data and the discrete fourier transform.

Recent technological advances have made it both feasible and practical to identify unsafe driving behaviors using second-by-second trajectory data. Presented in this paper is a unique approach to detecting safety-critical events using vehicles' longitudinal accelerations. A Discrete Fourier Transform is used in combination with K-means clustering to flag patterns in the vehicles' accelerations in time-series that are likely to be crashes or near-crashes.