An integrated decomposition and approximate dynamic programming approach for on-demand ride pooling


Through smartphone apps, drivers and passengers can dynamically enter and leave ride-hailing platforms. As a result, ride-pooling is challenging due to complex system dynamics and different objectives of multiple stakeholders. In this paper, we study ride-pooling with no more than two passenger groups who can share rides in the same vehicle. We dynamically match available drivers to randomly arriving passengers and also decide pick-up and drop-off routes. The goal is to minimize a weighted sum of passengers' waiting time and trip delay time. A spatial-and-temporal decomposition heuristic is applied and each subproblem is solved using Approximate Dynamic Programming (ADP), for which we show properties of the approximated value function at each stage. Our model is benchmarked with the one that optimizes vehicle dispatch without ride-pooling and the one that matches current drivers and passengers without demand forecasting. Using test instances generated based on the New York City taxi data during one peak hour, we conduct computational studies and sensitivity analysis to show (i) empirical convergence of ADP, (ii) benefit of ride-pooling, and (iii) value of future supply-demand information.

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