Categorizing bicycling environments using GPS-based public bicycle speed data
- Authors
- Joo, Shinhye; Oh, Cheol; Jeong, Eunbi; Lee, Gunwoo
- Issue Date
- Jul-2015
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Public bicycle; Bicycling environments; Support vector machine; Bicycle speed data; Bicycle traffic monitoring
- Citation
- TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, v.56, pp.239 - 250
- Indexed
- SCIE
SCOPUS
- Journal Title
- TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Volume
- 56
- Start Page
- 239
- End Page
- 250
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/17845
- DOI
- 10.1016/j.trc.2015.04.012
- ISSN
- 0968-090X
- 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 Ltd. All rights reserved.
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