Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 오철 | - |
dc.date.accessioned | 2023-07-12T06:01:14Z | - |
dc.date.available | 2023-07-12T06:01:14Z | - |
dc.date.issued | 2015-07 | - |
dc.identifier.citation | Transportation Research Part C: Emerging Technologies, v. 56, Page. 239-250 | - |
dc.identifier.issn | 0968-090X;1879-2359 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0968090X15001539?via%3Dihub | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/183312 | - |
dc.description.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. | - |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea Grant funded by the Korea Government (MEST) (NRF-2010-0029449) | - |
dc.language | en | - |
dc.publisher | Pergamon Press Ltd. | - |
dc.subject | Public bicycle | - |
dc.subject | Bicycling environments | - |
dc.subject | Support vector machine | - |
dc.subject | Bicycle speed data | - |
dc.subject | Bicycle traffic monitoring | - |
dc.title | Categorizing bicycling environments using GPS-based public bicycle speed data | - |
dc.type | Article | - |
dc.relation.volume | 56 | - |
dc.identifier.doi | 10.1016/j.trc.2015.04.012 | - |
dc.relation.page | 239-250 | - |
dc.relation.journal | Transportation Research Part C: Emerging Technologies | - |
dc.contributor.googleauthor | Joo, Shinhye | - |
dc.contributor.googleauthor | Oh, Cheol | - |
dc.contributor.googleauthor | Jeong, Eunbi | - |
dc.contributor.googleauthor | Lee, Gunwoo | - |
dc.sector.campus | E | - |
dc.sector.daehak | 공학대학 | - |
dc.sector.department | 교통·물류공학과 | - |
dc.identifier.pid | cheolo | - |
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