Mobility is being reshaped by shared rides, connectivity, and electrification — but the industry is fragmented, and seamless door-to-door service requires many operators to cooperate. We study the business models and pricing mechanisms that make that cooperation profitable and fair.

Our frameworks derive fair prices in ridehailing systems with asymmetric demand and travel time, and design incentive and profit-sharing schemes so that independent operators in a multimodal network are better off participating than going it alone. We also study rebalancing — moving idle vehicles to where demand will be — as a reinforcement learning problem.

Highlights

• Fair pricing of ridehailing services under asymmetric demand and travel time.
• Incentive design and profit sharing across multimodal transportation networks.
• Multi-objective vehicle rebalancing via reinforcement learning.

See the Research overview and the Publications list.