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The Effects of Age and Gender on Pedestrian Traffic Injuries: A Random Parameters and Latent Class Analysis

Pedestrians are vulnerable road users because they do not have any protection while they walk. They are unlike cyclists and motorcyclists who often have at least helmet protection and sometimes additional body protection (in the case of motorcyclists with body-armored jackets and pants). In the US, pedestrian fatalities are increasing and becoming an ever larger proportion of overall roadway fatalities (NHTSA, 2016), thus underscoring the need to study factors that influence pedestrian-injury severity and potentially develop appropriate countermeasures. One of the critical elements in the study of pedestrian-injury severities is to understand how injuries vary across age and gender ‒ two elements that have been shown to be critical injury determinants in past research. In the current research effort, 4829 police-reported pedestrian crashes from Chicago in 2011 and 2012 are used to estimate multinomial logit, mixed logit, and latent class logit models to study the effects of age and gender on resulting injury severities in pedestrian crashes. The results from these model estimations show that the injury severity level for older males, younger males, older females, and younger females are statistically different. Moreover, the overall findings also show that older males and older females are more likely to have higher injury-severity levels in many instances (if a crash occurs on city streets, state maintained urban roads, the primary cause of the crash is failing to yield right-of way, pedestrian entering/ leaving/ crossing is not at intersection, road surface condition is dry, and road functional class is a local road or street). The findings suggest that well-designed and well-placed crosswalks, small islands in two-way streets, narrow streets, clear road signs, provisions for resting places, and wide, flat sidewalks all have the potential to result in lower pedestrian-injury severities across age/gender combinations.

Identiferoai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-7556
Date21 June 2016
CreatorsRaharjo, Tatok Raharjo
PublisherScholar Commons
Source SetsUniversity of South Flordia
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceGraduate Theses and Dissertations
Rightsdefault

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