Categorizing bicycling environments using GPS-based public bicycle speed data |
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| Affiliation: | 1. Department of Transportation and Logistics Engineering, Hanyang University, Ansan, Republic of Korea;2. Maritime Industry & Logistics Division, Korea Maritime Institute, Seoul, Republic of Korea;1. Department of Urban Design and Planning, University of Washington, Seattle, USA;2. Department of Civil and Environmental Engineering, University of Washington, Seattle, USA;1. Department of Civil Engineering and Applied Mechanics, McGill University, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, QC H3A 0C3, Canada;2. Montreal Department of Public Health, Montreal Health and Social Service Agency, 1301 Sherbrooke Street East, Montréal, QC H2L 1M3, Canada;1. Department of Geography, Hebrew University of Jerusalem, Mount Scopus, 91905, Jerusalem, Israel;2. Department of Management Engineering, Technical University of Denmark, Bygningstorvet 116B, 2800, Kgs. Lyngby, Denmark;3. Institute for Multidisciplinary Research in Quantitative Modelling and Analysis, Catholic University of Louvain, Voie Du Roman Pays 34, 1348, Louvain-la-Neuve, Belgium;4. School of Civil Engineering, The University of Queenslandm, St. Lucia, 4072, Brisbane, Australia |
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| 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. |
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| Keywords: | Public bicycle Bicycling environments Support vector machine Bicycle speed data Bicycle traffic monitoring |
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