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Neural Approximate Dynamic Programming For On-Demand Ride-Pooling, Sanket Shah, Meghna Lowalekar, Pradeep Varakantham
Neural Approximate Dynamic Programming For On-Demand Ride-Pooling, Sanket Shah, Meghna Lowalekar, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
On-demand ride-pooling (e.g., UberPool, LyftLine, GrabShare) has recently become popular because of its ability to lower costs for passengers while simultaneously increasing revenue for drivers and aggregation companies (e.g., Uber). Unlike in Taxi on Demand (ToD) services – where a vehicle is assigned one passenger at a time – in on-demand ride-pooling, each vehicle must simultaneously serve multiple passengers with heterogeneous origin and destination pairs without violating any quality constraints. To ensure near real-time response, existing solutions to the real-time ride-pooling problem are myopic in that they optimise the objective (e.g., maximise the number of passengers served) for the current …