A route network planning method for urban air delivery

Journal Article (2022)
Author(s)

Xinyu He (City University of Hong Kong)

Fang He (Shanghai Jiao Tong University, City University of Hong Kong)

Lishuai Li (TU Delft - Aerospace Engineering, City University of Hong Kong)

Lei Zhang (Antwork Technology)

Gang Xiao (Shanghai Jiao Tong University)

Research Group
Air Transport & Operations
DOI related publication
https://doi.org/10.1016/j.tre.2022.102872 Final published version
More Info
expand_more
Publication Year
2022
Language
English
Research Group
Air Transport & Operations
Journal title
Transportation Research Part E: Logistics and Transportation Review
Volume number
166
Article number
102872
Downloads counter
399
Collections
Institutional Repository
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.