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Using trajectory data to explore roadway characterization for bikeshare network
| 题名： | Using trajectory data to explore roadway characterization for bikeshare network |
| 正文语种： | 英文 |
| 作者： | Xiaoyue Cathy Liu; Jeffrey Taylor; Richard J. Porter; Ran Wei |
| 作者单位： | Department of Civil & Environmental Engineering, University of Utah, 110 Central Campus Drive, Suite 2000, Salt Lake City, UT 84112, United States; VHB Inc., Venture I, 940 Main Campus Drive, Suite 500, Raleigh, NC 27606, United States; School of Public Policy, University of California at Riverside, 900 University Ave., Riverside, CA 92521, United States |
| 关键词： | bikeshare; centrality; network modeling; road classification; trajectory |
| 摘要： | The rapid expansion of bikeshare programs nationwide provides opportunities to gain insights on the optimal development of multimodal networks and bike-friendly environments. The profusion of trajectory-level data produced by bikeshare systems allows for information extraction on users' route preferences and, if modeled properly, will lead to a greater understanding of road characteristics that are appealing to bikeshare users. Leveraging Global Positioning System (GPS) data obtained from the GREENbike program, this study proposes a method to characterize roadways (e.g. collector, peripheral road, attractive road, and local road) on the basis of a variety of network centrality functions. The methodology is able to uncover the structure of the underlying transportation network and identify locations of critical bicycle infrastructures. A series of centrality measures, including degree, shortest-path betweenness, and random-walk betweenness centrality are implemented to determine the roadway classifications. Their suitability and usability for this purpose is then explored and discussed at length through a sensitivity analysis. The method can be applied to any bikeshare system that has access to trajectory-level (i.e. GPS, crowdsourcing) data for identifying road attributes that are appealing to bike users. Results can effectively guide future investment choices. |
| 出版年： | 2018 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | 22 |
| 期： | 6 |
| 页码： | 530-546 |
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