Dynamic Public Resource Allocation Based on Human Mobility Prediction

The objective of public resource allocation, e.g., the deployment of billboards, surveillance cameras, base stations, trash bins, is to serve more people. However, due to the dynamics of human mobility patterns, people are distributed unevenly on the spatial and temporal domains. As a result, in many cases, redundant resources have to be deployed to meet the crowd coverage requirements, which leads to high deployment costs and low usage. Fortunately, with the development of unmanned vehicles, the dynamic allocation of those public resources becomes possible. To this end, we provide the first attempt to design an effective and efficient scheduling algorithm for the dynamic public resource allocation. We formulate the problem as a novel multi-agent long-term maximal coverage scheduling (MALMCS) problem, which considers the crowd coverage and the energy limitation during a whole day. Two main components are employed in the system: 1) multi-step crowd flow prediction, which makes multi-step crowd flow prediction given the current crowd flows and external factors; and 2) energy adaptive scheduling, which employs a two-step heuristic algorithm, i.e., energy adaptive scheduling (EADS), to generate a scheduling plan that maximizes the crowd coverage within the service time for agents. Extensive experiments based on real crowd flow data in Happy Valley (a popular theme park in Beijing) demonstrate the effectiveness and efficiency of our approach.