Traffic Management for UAV-based Parcel Delivery in Multimodal Systems

Abstract

With dramatically increasing traffic demand, the air transportation system not only has been expanding in scale, but also welcoming new entrants including Unmanned Aerial Vehicles (UAVs). With potentially large demand for UAV delivery in the foreseeable future, the need for and importance of efficiently managing UAV traffic in urban airspace is arising. We first addressed high density UAV delivery operations in low-altitude urban airspace. We proposed a framework of Unmanned Aircraft System Traffic Management (UTM) including UAV path planning, Traffic Management models with conflict detection and resolution (CD&R), and mechanism design for airspace resource allocation. We found that strategic UTM can enable a sizable share of UAV deliveries with relatively low congestion costs. Then we explored the potential benefit of operating multiple delivery modes synergistically integrating traditional trucks, electric cargo bikes and UAVs. Multi-modal systems are increasingly perceived as the future solution to help reduce traffic congestion, air pollution, noise, and safety concerns. We optimized several different multi-modal strategies in a manner that considers the impact of truck traffic on urban congestion. This work will eventually determine whether and under what conditions UAV delivery can mitigate road congestion in a cost-effective manner. Also in traditional commercial aviation, multi-modal systems integrating air with ground transportation to enable flight diversion can be one of the solutions to improve the efficiency of Air Traffic Management (ATM). 

 

Speaker

Supervised by Prof. Mark Hansen, Ms Ang Li is a PhD candidate in the Transportation Engineering Programme, Civil and Environmental Engineering Department at University of California Berkeley. Ms Li is also a researcher in the National Center of Excellence in Aviation Operations Research (NEXTOR), and Safe Transportation Research and Education Center (SafeTREC). Her research areas focus on Unmanned Aircraft Systems Traffic Management (UTM), regional Air Traffic Management (ATM), multi-modal transportation network, last-mile parcel delivery, advanced air mobility, mechanism design and resource allocation problems, using operations research and machine learning. Ms Li has collaborated with NASA and co-developed the new course ‘Aviation Data Science’ taught at UC Berkeley. She is the recipient of the Best Paper Award from ICNS conference and the Robert Wadell Endowed Fellowship for Engineering Innovation in 2021 and 2022 respectively. She is also selected as Rising Star in CEE by Carnegie Mellon University in 2022.