An Algorithm for Estimating Origins and Destinations of Shared E-Scooter Trips from Public Data-Feeds
We present an algorithm that estimates shared e-scooter trip origins and destinations using public General Bikeshare Feed Specification (GBFS) data. The model addresses challenges like GPS lag and randomized vehicle IDs. The algorithm is field-tested and validated using data for the city of Fairfax,...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Findings Press
2024-11-01
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| Series: | Findings |
| Online Access: | https://doi.org/10.32866/001c.125812 |
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| Summary: | We present an algorithm that estimates shared e-scooter trip origins and destinations using public General Bikeshare Feed Specification (GBFS) data. The model addresses challenges like GPS lag and randomized vehicle IDs. The algorithm is field-tested and validated using data for the city of Fairfax, VA. When applied to Washington, D.C., a larger city, the algorithm demonstrates scalability for larger, complex urban settings. Our methodology equips urban planners and activity center managers with a robust tool for analyzing micromobility patterns, enhancing first- and last-mile connectivity, and optimizing transportation systems---without relying on proprietary data from operators. |
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| ISSN: | 2652-8800 |