In 2050, close to 70% of our population is projected to live in urban centers. At the same time, our public transportation struggles to serve the existing population effectively, with 45% of people having no adequate access. Even worse, 55% of taxi and ride-sharing demand is coming from the lowest income group in the US due to unavailability of alternatives in their living areas.
At Routable AI we believe that to keep our cities livable, we need more affordable, convenient and equitable transportation. We envision a city where a small fleet of shuttles and busses provide completely on-demand, shared and affordable transportation. Think of it as UberPool on steroids.
It is however extremely difficult to compute routes for such a fleet to effectively use the seats, while still providing passengers with an efficient ride. This requires a whole new order of optimization algorithms, that have not been available before.
At Routable AI we have developed a game-changing routing technology originating from 5 years of MIT research to efficiently use high-capacity vehicles completely on-demand while still providing everyone with an extremely efficient ride. We can perform dynamic routing for vehicles shared by 1 to 12+ passengers at the same time.
The video below visualizes what such a fleet looks like. All the dots in this simulation are 7-seater vehicles, and the lines are their planned routes. The red dots on the routes are passenger pick-ups and drop-offs. The vehicle routes continuously adapt to most efficiently serve the passengers.