Spatio-temporal comparative analysis of scooter share in Washington D.C.

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering and Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Jassar, Gulsagar Singh.

Advisor dc:contributor.advisor

• Daniel Freund.

• Engineering and Management Program.
• System Design and Management Program.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/132887

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/132887