Abstract:
Autonomous vehicles, ridesharing, and self-driving technology will change how ride hailing platforms plan their services. However, advances in technology, road congestion, environmental concerns, fuel usage, vehicle emissions, and high vehicle usage costs have focused attention on better vehicle use and capacity. We propose a distributed multi-hop ride-sharing (MHRS) algorithm that uses deep reinforcement learning to learn optimal vehicle dispatch and matching decisions from the external environment. Compared to ride-sharing algorithms, MHRS helps customers save 30% and use fleets 20% more efficiently by letting them switch vehicles. Multi-hop flexibility improves ride-sharing services by allowing customers and companies to interact seamlessly.
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