Improving bicycle safety is considered as a growing concern for two reasons. First, in the United States in recent years, about 700 cyclists were killed and about 48,000 were injured in bicycle motor vehicle crashes each year. Regarding crash location, from 2008 to 2012 in the United States, more than 30% of cyclist fatalities occurred at intersections. Furthermore, up to 16% of bicycle-related crashes were due to cyclist violations at intersections. Second, from 2000 to 2011, bicycle commuting rates in the United States has increased: by 80% in large Bicycle Friendly Cities (BFCs), by 32% in non-BFCs, and overall by 47%. Also, cycling as one of the sustainable and eco-friendly modes of transport is receiving more attention than before. In this paper, to investigate factors affecting cyclist behavior at signalized intersections, a naturalistic cycling experiment was designed and conducted. Applying mixed effects generalized regression analysis, movement and presence of other users were found as significant factors that influence the probability of red light violations by cyclists. Moreover, several machine learning algorithms were adopted to develop cyclist violation prediction models at signalized intersections. The violation prediction models were developed based on kinetic information of cyclists approaching the intersection. The results showed a promising performance of the prediction models in terms of high true positive rates and low false positive rates.


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    Title :

    Investigating cyclist violations at signal-controlled intersections using naturalistic cycling data


    Contributors:


    Publication date :

    2016-11-01


    Size :

    432468 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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