Full List of Publications
Hosseinzadeh, Karimpour, Kluger and Orthober (2022). Data linkage for crash outcome assessment: Linking police-reported crashes, emergency response data, and trauma registry records. Journal of Safety Research. You Tube
Dibaj, Hosseinzadeh, Mladenović and Kluger (2021). Where Have Shared E-Scooters Taken Us So Far? A Review of Mobility Patterns, Usage Frequency, and Personas. Sustainability.
Hosseinzadeh, Karimpour and Kluger (2021). Factors influencing shared micromobility services: An analysis of e-scooters and bikeshare. Transportation Research Part D. YouTube
Karimpour, Kluger, Liu, and Wu (2021). Effects of speed feedback signs and law enforcement on driver speed. Transportation Research Part F: Traffic Psychology and Behaviour. YouTube
Hosseinzadeh, Algomaiah, Kluger, and Li. (2021). E-scooters and Sustainability: Investigating the Relationship between the Density of E-Scooter Trips and Characteristics of Sustainable Urban Development. Sustainable Cities and Society. YouTube
Karimpour, Kluger, and Wu. (2020). Traffic Sensor Data-Based Assessment of Speed Feedback Signs. Journal of Transportation Safety and Security.
Li, Kluger, Hu, Wu, and Zhu. (2018). Reconstructing Vehicle Trajectories to Support Travel Time Estimation. Transportation Research Record.
Kluger, Smith, Park, and Dailey. (2106). Identification of safety-critical events using kinematic vehicle data and the discrete fourier transform. Accident Analysis and Prevention.
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. Method: A heuristic framework is developed to match EMS run reports to crashes through time, location, and other indicators present in both datasets. Types of matches between EMS and crashes were classified. To investigate the fidelity of the match approach, a manual review of a sample of data was conducted. A comparative bias analysis was implemented on several key variables.