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Citation |
Li Y, Fan WD. Accid. Anal. Prev. 2019; 131: 284-296. |
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Affiliation |
USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3261, 9201 University City Boulevard, Charlotte, NC, 28223-0001, United States. Electronic address: wfan7@uncc.edu. |
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Copyright |
(Copyright © 2019, Elsevier Publishing) |
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DOI |
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PMID |
31351231 |
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Abstract |
There are more than 2000 pedestrians reported to be involved in traffic crashes with vehicles in North Carolina every year. 10%-20% of them are killed or severely injured. Research studies need to be conducted in order to identify the contributing factors and develop countermeasures to improve safety for pedestrians. However, due to the heterogeneity inherent in crash data, which arises from unobservable factors that are not reported by law enforcement agencies and/or cannot be collected from state crash records, it is not easy to identify and evaluate factors that affect the injury severity of pedestrians in such crashes. By taking advantage of the latent class clustering (LCC), this research firstly applies the LCC approach to identify the latent classes and classify the crashes with different distribution characteristics of contributing factors to the pedestrian-vehicle crashes. By considering the inherent ordered nature of the traffic crash severity data, a partial proportional odds (PPO) model is then developed and utilized to explore the major factors that significantly affect the pedestrian injury severities resulting from pedestrian-vehicle crashes for each latent class previously obtained in the LCC. This study uses police reported pedestrian crash data collected from 2007 to 2014 in North Carolina, containing a variety of features of motorist, pedestrian, environmental, roadway characteristics. Parameter estimates and associated marginal effects are mainly used to interpret the models and evaluate the significance of each independent variable. Lastly, policy recommendations are made and future research directions are also given. Language: en |
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Keywords |
Crash; Latent class clustering; North Carolina; Partial proportional odds; Pedestrian |