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A route network planning method for urban air delivery

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  • He, Xinyu
  • He, Fang
  • Li, Lishuai
  • Zhang, Lei
  • Xiao, Gang

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.

Suggested Citation

  • He, Xinyu & He, Fang & Li, Lishuai & Zhang, Lei & Xiao, Gang, 2022. "A route network planning method for urban air delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:transe:v:166:y:2022:i:c:s1366554522002526
    DOI: 10.1016/j.tre.2022.102872
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    References listed on IDEAS

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    1. Moshref-Javadi, Mohammad & Lee, Seokcheon & Winkenbach, Matthias, 2020. "Design and evaluation of a multi-trip delivery model with truck and drones," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    2. Lemardelé, Clément & Estrada, Miquel & Pagès, Laia & Bachofner, Mónika, 2021. "Potentialities of drones and ground autonomous delivery devices for last-mile logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    3. Rajendran, Suchithra & Srinivas, Sharan, 2020. "Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    4. Chung, Sai-Ho, 2021. "Applications of smart technologies in logistics and transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    5. Zhang, Guowei & Zhu, Ning & Ma, Shoufeng & Xia, Jun, 2021. "Humanitarian relief network assessment using collaborative truck-and-drone system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    6. Shen, Lixin & Wang, Yaodong & Liu, Kunpeng & Yang, Zaili & Shi, Xiaowen & Yang, Xu & Jing, Ke, 2020. "Synergistic path planning of multi-UAVs for air pollution detection of ships in ports," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    7. Kitonsa, H. & Kruglikov, S. V., 2018. "Significance of drone technology for achievement of the United Nations sustainable development goals," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 4(3), pages 115-120.
    8. Liu, Shan & Jiang, Hai & Chen, Shuiping & Ye, Jing & He, Renqing & Sun, Zhizhao, 2020. "Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
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    Cited by:

    1. Jin, Zhongyi & Ng, Kam K.H. & Zhang, Chenliang & Wu, Lingxiao & Li, Ang, 2024. "Integrated optimisation of strategic planning and service operations for urban air mobility systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    2. Hu, Zhangchen & Chen, Heng & Lyons, Eric & Solak, Senay & Zink, Michael, 2024. "Towards sustainable UAV operations: Balancing economic optimization with environmental and social considerations in path planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    3. Jin, Zhongyi & Ng, Kam K.H. & Zhang, Chenliang & Liu, Wei & Zhang, Fangni & Xu, Gangyan, 2024. "A risk-averse distributionally robust optimisation approach for drone-supported relief facility location problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).

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