Date of Award
6-7-2024
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2024
Department
Computer Science and Engineering
First Advisor
Behnam Dezfouli
Second Advisor
Yuhong Liu
Abstract
The burgeoning market for shared e-scooters is significantly hampered by the short lifespan of commercial e-scooters, which currently average just three months due to rough handling by users. To address this challenge, our project aims to extend the lifespan of shared e-scooters through an innovative onboard solution that discourages detrimental riding behaviors.
Our solution integrates a portable sensor hub from STMicroelectronics to capture ride data, which is then processed and sent via a user’s iOS app to a Google Firebase backend. A machine learning model running in the cloud analyzes the data to extract valuable metrics. These metrics are displayed on a dedicated web application, enabling ride-sharing companies to monitor and influence user behavior effectively. By providing these insights, our solution not only promotes the longevity of the e-scooters but also enhances the operational feasibility for service providers, with the potential to transform the economic landscape of urban ride-sharing.
Recommended Citation
Phadke, Soham; Ravala, Suvass; Jerome, Joshua; Batra, Raghav; and Hussain, Mubashir, "E-Scooter Black Box" (2024). Computer Science and Engineering Senior Theses. 281.
https://scholarcommons.scu.edu/cseng_senior/281