Rico, A., Sakai, Y., & Larson, K. (2020, November). JettSen: A Mobile Sensor Fusion Platform for City Knowledge Abstraction. In Proceedings of the Future Technologies Conference (pp. 773-791). Springer, Cham.

Abstract

In the past years, mobility trends in cities around the world have been pushing for safer, greener, and more efficient transportation systems. This shift in mobility trends creates an opportunity for using mobile lightweight infrastructure, such as bicycles, as a generator of knowledge that will benefit commuters alongside the environmental and societal performance of cities. We propose a system architecture design for an open source mobile sensor fusion apace a platform with a knowledge abstraction framework that enables citizens, urban planners, researchers, and city officials to better address the complex issues that are innate to cities. The system is mounted on a commercial electric assist bike and is able to combine sensor input that describes the bicycle’s electro-mechanical, geospatial, and environmental states. The system proposes sensor flexibility and modularity as key characteristics, and the abstraction framework conceptualizes the way in which these characteristics can be best exploited for city improvement. We demonstrate the functionality of the system and framework through the creation of a use case implementation for clustering bike trip patterns using unsupervised learning clustering techniques. This platform outlines a way to migrate focus from providing solutions to asking the right questions in order to satisfy citizens’ needs.