Scheduling Aerial Vehicles in an Urban Air Mobility Scheme
This addresses traffic congestion and air pollution in highly populated cities, but the approach is incremental as it builds on existing scheduling methods for new AV data.
The paper tackles the problem of scheduling aerial vehicles (AVs) for Urban Air Mobility to maximize serviced customers and minimize energy consumption by flying at low altitudes, presenting an optimal Integer Linear Program and a near-optimal incremental algorithm for scalability.
Highly populated cities face several challenges, one of them being the intense traffic congestion. In recent years, the concept of Urban Air Mobility has been put forward by large companies and organizations as a way to address this problem, and this approach has been rapidly gaining ground. This disruptive technology involves aerial vehicles (AVs) for hire than can be utilized by customers to travel between locations within large cities. This concept has the potential to drastically decrease traffic congestion and reduce air pollution, since these vehicles typically use electric motors powered by batteries. This work studies the problem of scheduling the assignment of AVs to customers, having as a goal to maximize the serviced customers and minimize the energy consumption of the AVs by forcing them to fly at the lowest possible altitude. Initially, an Integer Linear Program (ILP) formulation is presented, that is solved offline and optimally, followed by a near-optimal algorithm, that solves the problem incrementally, one AV at a time, to address scalability issues, allowing scheduling in problems involving large numbers of locations, AVs, and customer requests.