2026-09-19T03:45:58Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1328872025-10-30T15:50:04Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Daniel Freund.
Jassar, Gulsagar Singh.
Massachusetts Institute of Technology. Engineering and Management Program.
System Design and Management Program.
Massachusetts Institute of Technology. Engineering and Management Program
n-us-dc
2021-10-08T17:10:36Z
2021-10-08T17:10:36Z
2020
2021
https://hdl.handle.net/1721.1/132887
1263357384
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, February, 2021
Cataloged from the official version of thesis. "February 2021."
Includes bibliographical references (pages 76-79).
Geospatial-temporal data for different e-scooter firms was collected and investigated for differences in e-scooter usage patterns among customers of the firms. Computational analysis using predictive algorithms and correlation analysis was done to find co-relationally important features for predicting the dependent variable. Data-preprocessing included computing trips from geospatial data and dividing the city into smaller clusters for analysis using geohashes. Hourly weather data was added to the geospatial temporal data to account for weather impact on the number of trips. The Spatio-temporal analysis shows a correlation between the percentage of scooters parked at a location and the success rate of the firm with the highest scooters getting the highest number of trips.
by Gulsagar Singh Jassar.
S.M. in Engineering and Management
S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program
79 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Engineering and Management Program.
System Design and Management Program.
Spatio-temporal comparative analysis of scooter share in Washington D.C.
Thesis
application/pdf
2021-10-08T17:10:36Z
Publication
Master
SysDes
unknown
unknown
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<Abstract>Geospatial-temporal data for different e-scooter firms was collected and investigated for differences in e-scooter usage patterns among customers of the firms. Computational analysis using predictive algorithms and correlation analysis was done to find co-relationally important features for predicting the dependent variable. Data-preprocessing included computing trips from geospatial data and dividing the city into smaller clusters for analysis using geohashes. Hourly weather data was added to the geospatial temporal data to account for weather impact on the number of trips. The Spatio-temporal analysis shows a correlation between the percentage of scooters parked at a location and the success rate of the firm with the highest scooters getting the highest number of trips.</Abstract>
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