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Estimation of traffic density from drone-based delivery in very low level urban airspace

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  • Doole, Malik
  • Ellerbroek, Joost
  • Hoekstra, Jacco

Abstract

Driven by rising consumer demand, interest is growing in the application of autonomous unmanned aerial vehicles (drones) for the last-mile delivery of small express packages and fast-food meals in cities. To be realised, this would require the Very Low Level (VLL) urban airspace to be able to cope with high traffic densities of commercial delivery drones. The potential benefits of such novel drone-based applications are a reduction of traffic congestion in cities, lower greenhouse gas emissions and more efficient transportation operations. To help realise this concept, programs such as U-Space, the unmanned traffic management system for Europe, are developing important services such as deconfliction management and dynamic capacity management. However, for several of these services, design choices will depend on how, and how extensive they will be used. It therefore becomes important to estimate how many delivery drones would operate in a typical city. This paper aims to provide an estimate by establishing a framework to determine the traffic demand for express drone-based package delivery of five European countries. In addition, a detailed case-study is presented for determining traffic density of express package drone delivery for Paris metropolitan area in order to assess the feasibility from a user's perspective. The paper also discusses the potential of fast-food meal delivery drones compared to traditional delivery modes for Paris. Results suggest that hourly traffic densities culminating from express package and fast-food meal delivery drones will exceed today's global commercial aircraft traffic of 10,000 per day by more than six-fold for just one potential metropolitan city.

Suggested Citation

  • Doole, Malik & Ellerbroek, Joost & Hoekstra, Jacco, 2020. "Estimation of traffic density from drone-based delivery in very low level urban airspace," Journal of Air Transport Management, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:jaitra:v:88:y:2020:i:c:s0969699719304004
    DOI: 10.1016/j.jairtraman.2020.101862
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    Citations

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    Cited by:

    1. Jurica Ivošević & Emir Ganić & Antonio Petošić & Tomislav Radišić, 2021. "Comparative UAV Noise-Impact Assessments through Survey and Noise Measurements," IJERPH, MDPI, vol. 18(12), pages 1-20, June.
    2. Aditya Kamat & Saket Shanker & Akhilesh Barve & Kamalakanta Muduli & Sachin Kumar Mangla & Sunil Luthra, 2022. "Uncovering interrelationships between barriers to unmanned aerial vehicles in humanitarian logistics," Operations Management Research, Springer, vol. 15(3), pages 1134-1160, December.
    3. Büyüközkan, Gülçin & Ilıcak, Öykü, 2022. "Smart urban logistics: Literature review and future directions," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    4. Shahzad, Muhammad Faisal & Yuan, Jingbo & Shahzad, Khuram, 2024. "Elevating culinary skies: Unveiling hygiene motivations, environmental trust, and market performance in drone food delivery adoption in China," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    5. Cui, Shaohua & Yang, Ying & Gao, Kun & Cui, Heqi & Najafi, Arsalan, 2024. "Integration of UAVs with public transit for delivery: Quantifying system benefits and policy implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    6. Mulumba, Timothy & Diabat, Ali, 2024. "Optimization of the drone-assisted pickup and delivery problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).

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