SOUP: Spatial-Temporal Demand Forecasting and Competitive Supply in Transportation

We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an authority assigns agents to requests such that the average idle time of the agents is minimized. An example is the scheduling of taxis (agents) to meet incoming passenger requests for trips while ensuring that the taxis are empty as little as possible. We address the problem of spatial-temporal demand forecasting and competitive supply (SOUP) in two steps. First, we build a granular model that provides spatial-temporal predictions of requests. Specifically, we propose a Spatial-Temporal Graph Convolutional Sequential Learning (
${{\sf ST\text{-}GCSL}}$
) model that predicts requests across locations and time slots. Second, we provide means of routing agents to request origins while avoiding competition among the agents. In particular, we develop a demand-aware route planning (
${{\sf DROP}}$
) algorithm that considers both the spatial-temporal predictions and the supply-demand state. We report on extensive experiments with real-world data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-the-art proposals.