@article{ref1,
title="Categorizing bicycling environments using GPS-based public bicycle speed data",
journal="Transportation research part C: emerging technologies",
year="2015",
author="Joo, Shinhye and Oh, Cheol and Jeong, Eunbi and Lee, Gunwoo",
volume="56",
number="",
pages="239-250",
abstract="A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist's perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist's perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring. (C) 2015 Elsevier Publications.<p /><p>Language: en</p>",
language="en",
issn="0968-090X",
doi="10.1016/j.trc.2015.04.012",
url="http://dx.doi.org/10.1016/j.trc.2015.04.012"
}