Categorizing bicycling environments using GPS-based public bicycle speed data. (July 2015)

• Record Type: Journal Article
• Authors: Joo, Shinhye; Oh, Cheol; Jeong, Eunbi; Lee, Gunwoo
• Is Part Of: Transportation research. Volume 56(2015)
• Page Start: 239
• Page End: 250
• Publication Date: 2015-07
• Subjects: Public bicycle -- Bicycling environments -- Support vector machine -- Bicycle speed data -- Bicycle traffic monitoring; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2015.04.012
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A methodology for categorizing bicycling environments is proposed. GPS based public bicycle speed data is used. A support vector machine is adopted for the proposed categorization algorithm. Technical feasibility of the proposed algorithm is demonstrated. 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.