This paper introduces the preliminary categorization of motorcycle crash data collected from 100 riders as they rode for a period between 2 months and 2 years. These riders resided in California, Florida, Virginia, and Arizona, and both video and motorcycle kinematic data were collected for every ride. The videos of incidents were reviewed, and the events were described with motorcycle-specific categories for event severity, event nature, incident type, precipitating event, rider reaction, and postmaneuver control. Within the data set of more than 38,000 trips, 22 incidents defined as crashes (involving 18 riders) were identified and categorized. The majority of the crashes (15) were single-vehicle, low-speed crashes. Of the remaining crashes, one was a single-vehicle (higher speed) crash, two were rear-end collisions, and four involved one vehicle turning into or across the path of another at an intersection. Rider response to the precipitating event for more than half of the crashes involved no visible front braking or lateral input. Most crashes occurred within 20 min of the beginning of a trip, during daylight, and in favorable weather conditions. There was no overriding hypothesis implying a relationship of crash occurrence to a specific demographic group in terms of location, age, gender, or motorcycle class. This research applied motorcycle incident categorization to 22 crashes. The categorization terminology and definitions are also applicable to near-crashes. This information is useful in providing an unbiased understanding of what occurs during such incidents and offering a basis for the structured cataloging of crashes, as well as future categorization of near-crashes and crash-relevant events.


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    Title :

    Exploratory Analysis of Motorcycle Incidents Using Naturalistic Riding Data


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2015-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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