While micromobility solutions have flourished and grown during the pandemic, the existential question of how to solve the biggest challenge ahead for the micromobility segment—how users ride—remains unanswered. In this post, we’ll touch on a number of opportunities for the industry to embrace innovation and technology in order to remain the chosen method of transportation, even in a post-COVID world.

Watch an expert-packed panel discussion exploring new technologies like VR, AI, sensors, sidewalk detection, and gamification which hold the promise to reshape user behavior and solve micromobility issues for cities and operators. The panel is moderated by micromobility International and features leaders from Lyft, Unagi, Knack, and Synapse.

As the only form of public transit that is readily capable of supporting social distancing, shared bikes and scooters will be an essential component of an effective urban economic recovery strategy in the coming months. Prior to COVID-19, the city of San Francisco announced that future shared scooter platforms will be required to have deterrents to riding on sidewalks. Naturally, being both SF residents who will be affected by this decision, and curious engineers with micromobility industry experience, we decided to leverage our expertise in machine learning to explore the specifics involved in implementing a system which can determine if a scooter is riding on a street or a sidewalk.