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Modeling Bicycle Conflict on Non-Motorized Paths on Suburban College Campuses

Bicycling is becoming more and more used as a way of commuting in a person’s average day. It is also a popular way for college students and faculty to get around on their campus, but it varies by the type and size of the community.

With a rise in bicycling on campuses there comes a rise in bicycle collisions with vehicles, pedestrians, and other bicycles. There has been extensive research studying bicycle and pedestrian crashes. However, most of this research involves crashes with vehicles which are more common. While some aspects of this research can be applied to non-motorized paths, there is a lack of research strictly focusing on only bicycles and pedestrians.

This study aimed to fill this knowledge gap by developing a model to identify locations on roads and paths (hotspots) on college campuses that are likely to have a bicycle collision and predict the likelihood of a serious bicycle crash on a non-motorized path based on the characteristics of the path. This study identified those interactions between bicyclists and pedestrians on non-motorized paths on a suburban college campus in Newark, USA.

Findings suggest that pedestrian density of a path is a major factor in the maximum speed bicyclists can achieve. The wider the path is, the higher the maximum speed is that a bicyclist can obtain. This is because a wider path width decreases the pedestrian density. The grade of the path has little effect on bicycle speeds. The results of the models were displayed on a GIS map that is visually appealing to a viewer. The paths were color coded based on their level of safety, so it is easy to observe problematic areas of the network.

In short, this technique can be applied to the entire campus network of non-motorized paths to study the whole system. This can then be used by planners and designers to identify areas that need upgrading and improve the overall safety of the non-motorized path system.


Article by Zachary Nerwinski, et al, from University of Delaware, Newark, DE, USA.

Full access: http://mrw.so/59GMox

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