The new model and algorithm can optimize transit schedules resulting in "rush hour savings of millions of dollars per day."

"Transit Planning Optimization under Ride-hailing Competition and Traffic Congestion," was written by Dartmouth Engineering Professor Vikrant Vaze alongside Keji Wei Th'20, as well as Alexandre Jacquillat of the Massachusetts Institute of Technology.

This study attempts to understand what would happen if a public transit agency were to explicitly consider commuter choice factors and what that would do to the commute when designing their schedules.

Commuters choose modes of transportation based on travel convenience, prices, travel times and traffic congestion. The authors have found that the opposite is also true — their choice in turn changes the traffic patterns and travel times.

The authors note that by considering both of these points, they can come up with a better alignment of available transportation options with passengers' preferences in mind — by redistributing public transit resources where they provide the strongest societal benefits.

"In the interest of the overall urban ecosystem, a transit operator should critically assess what kinds of trips and travel needs transit is better equipped to serve and at the same time, what are some of the areas where it might be better to cut down and let on-demand operators take up a larger proportion of trips. Such thoughtful reconfiguration can benefit diverse stakeholders simultaneously," continued Vaze. "What we found is that this leads to schedules that are better for passengers, better for transportation operators and better for the city as a whole, a rare win-win-win."

Podcast: Optimizing transportation scheduling for a win, win, win situation for all