Talk 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.

Talk Outline

  1. Background and Literature Review of Drone Traffic Management
  2. Introduction to the Formulation of Drone Logistics Network Planning Problems
  3. Introduction to a Priority-Based Heuristic Planning Algorithm
  4. Discussion on Potential Future Research Directions

About the Speakers

Lishuai Li

Associate Professor at the School of Data Science, City University of Hong Kong. She holds a Bachelor’s degree in Aircraft Design and Engineering from Fudan University, and both a Master’s and Ph.D. in Aeronautics and Astronautics from the Massachusetts Institute of Technology. She is a member of the Global Future Council of the World Economic Forum and was recognized among the top 2% of the world’s most cited scholars (single year) by the Stanford-Elsevier metrics in 2022. Her research focuses on the interdisciplinary field of intelligent transportation systems and data science, including the development of various data analysis methods using large-scale operational data. These methods are applied to traditional aviation safety management and operational improvements, air traffic management and forecasting, and health monitoring of train systems. She is currently researching the traffic management and infrastructure challenges of drone delivery services and urban air mobility.

Xinyu He

Ph.D. student at the School of Data Science, City University of Hong Kong. Research focuses on urban air mobility route network design, unmanned aircraft system traffic management, with related research published in high-impact journals such as Transportation Research Part E.

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