  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 assignment authority is to
assign 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
requests for trips while ensuring that the taxis are empty as little as
possible. In this paper, we study the problem of spatial-temporal demand
forecasting and competitive supply (SOUP). We address the problem 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 (ST-GCSL) algorithm that predicts the service 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 (DROP) algorithm that considers both the
spatial-temporal predictions and the supplydemand state. We report on extensive
experiments with realworld and synthetic data that offer insight into the
performance of the solution and show that it is capable of outperforming the
state-of-the-art proposals.
