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                Exploring Injury Severity in Head-On Crashes Using Latent Class Clustering Analysis and Mixed Logit Model: A Case Study of North Carolina
Publication Information
              Publication External Link:Link not available
              Publication Type:Article
                      Abstract:Although only 2 % of crashes are head-on crashes in the United States, they account for over 10 % of all crash-related fatalities. This study aims to investigate the contributing factors that affect the injury severity of head-on crashes and develop appropriate countermeasures. Due to the unobserved heterogeneity inherent in the crash data, a latent class clustering analysis is firstly conducted to segment the head-on crashes into relatively homogeneous clusters. Then, mixed logit models are developed to further explore the unobserved heterogeneity within each cluster. Analyses are performed based on the data collected from the Highway Safety Information System (HSIS) from 2005 to 2013 in North Carolina. The estimated parameters and associated marginal effects are combined to interpret significant variables of the developed models. The proposed method is able to uncover the heterogeneity within the whole dataset and the homogeneous clusters.



Results of this research can provide more reliable and insightful information to engineers and policy makers regarding the contributing factors to head-on crashes.





Liu, P. & Fan, W. (2020). Exploring Injury Severity in Head-On Crashes Using Latent Class Clustering Analysis and Mixed Logit Model: A Case Study of North Carolina. Accident Analysis & Prevention, 135.
              Publishing Date:January 2020
                                                  FHWA Program(s):Research
              Safety
              AMRP Program(s):Safety Data and Analysis
              FHWA Activities:Highway Safety Information System
              Subject Area:Safety and Human Factors U.S. DEPARTMENT OF TRANSPORTATION

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