NSF ScooterLab

ScooterLab is a research initiative focused on micromobility and urban planning Funded by The National Science Foundation, led by Dr. Murtuza Jadliwala . It integrates advanced data collection, machine learning, and cyber-physical systems to improve the urban mobility landscape. My involvement in ScooterLab has revolved around backend development, data management, and API work, especially in areas such as ramp data parsing, resampling, and ensuring efficient data flow across various modules.

ScooterLab uses participatory sensing techniques to gather information on how micromobility vehicles interact with urban infrastructure, providing valuable insights for planners and city officials. Through my work, I have helped ensure that the backend systems are robust, secure, and capable of handling large datasets from micromobility vehicles in real time.

By collecting and analyzing data from micromobility vehicles, ScooterLab seeks to improve safety, efficiency, and sustainability in urban environments. As part of the project, I helped build a scalable backend system capable of supporting dynamic data flows, enabling seamless integration of various micromobility sensors and devices. We also focused on privacy and security, ensuring that all data collection aligns with strict ethical guidelines.

In addition to backend development, my role extended to data analytics, where I contributed to generating actionable insights from the collected data, which helped refine urban mobility solutions and optimize vehicle routing and infrastructure development.

You can learn more about the ScooterLab project here.