Spatio-temporal comparative analysis of scooter share in Washington D.C.
Name
1263357384-MIT.pdf
Size
6.47 MB
Format
Adobe PDF
Checksum (MD5)
d723115ec29e7b7031283e0aae376227
Author(s)
Jassar, Gulsagar Singh.
Advisor(s)
Daniel Freund.
Date Issued
2021
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Engineering and Management Program.
System Design and Management Program.
MIT Department
Massachusetts Institute of Technology. Engineering and Management Program
Terms of Use
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.
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