{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/132887"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/132887","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Spatio-temporal comparative analysis of scooter share in Washington D.C.","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_html":"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_has_math":false,"creators":["Jassar, Gulsagar Singh."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Engineering and Management Program","school":null,"contributors":[],"advisors":["Daniel Freund."],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021","date_published":"2021","updated_at":"2026-07-22T22:22:27Z","subjects":["Engineering and Management Program.","System Design and Management Program."],"languages":["eng"],"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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/132887","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Daniel Freund."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Engineering and Management Program","SysDes"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Engineering and Management Program.","System Design and Management Program."]},{"key":"dc:creator","label":"Author","values":["Jassar, Gulsagar Singh."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-10-08T17:10:36Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-10-08T17:10:36Z"]},{"key":"dc:date.issued","label":"Date","values":["2021"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering and Management Program.","System Design and Management Program."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/132887"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M. in Engineering and Management"]},{"key":"dc:title","label":"Title","values":["Spatio-temporal comparative analysis of scooter share in Washington D.C."]}]}],"canonical_facts":{"dc:contributor.advisor":["Daniel Freund."],"dc:contributor.department":["Massachusetts Institute of Technology. Engineering and Management Program","SysDes"],"dc:contributor.other":["Massachusetts Institute of Technology. Engineering and Management Program.","System Design and Management Program."],"dc:creator":["Jassar, Gulsagar Singh."],"dc:date.accessioned":["2021-10-08T17:10:36Z"],"dc:date.available":["2021-10-08T17:10:36Z"],"dc:date.issued":["2021"],"dc: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)."],"dc:description.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."],"dc:description.degree":["S.M. in Engineering and Management"],"dc:identifier.uri":["https://hdl.handle.net/1721.1/132887"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Engineering and Management Program.","System Design and Management Program."],"dc:title":["Spatio-temporal comparative analysis of scooter share in Washington D.C."],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:22:27Z"}