Abstract
Land transport in rural areas are characterized by the abundance of road bends, varied road conditions, elevated terrains, and the far distance between each station. Such situations have rendered full-path drone delivery impossible, and thus deliveries using drones must inevitably be combined with land transportation in the whole process. Ever since the Covid-19 outbreak, there has been an unprecedentedly high demand for efficiency in the delivery of medical goods such as vaccines and medicines, especially in rural areas. Such measures prove indispensable in preventing the spread of the disease among all citizens. Conventionally, the abundance of road conditions, the length, and width of paths, and the characteristics of road bends, are considered and analyzed by human staff qualitatively using their experience and personal judgment before deciding on the best delivery path and the optimal network. To overcome the shortcomings of conventional methods, this article proposes a machine learning-based algorithm that considers all the different road conditions as well as the terrain elevations systematically and quantitatively to determine the best delivery path and construct the optimal delivery network system. When combined with drone delivery, our algorithm will also yield the most feasible position for the drone to be deployed and stationed to deliver the goods to the intended destinations, thereby creating a more comprehensive delivery network system.
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References
Stastna, M., Vaishar, A.: The relationship between public transport and the progressive development of rural areas. Land Use Policy 67, 107–114 (2017). https://doi.org/10.1016/j.landusepol.2017.05.022
Kim, S.J., Lim, G.J., Cho, J., Cote, M.J.: Drone-aided healthcare services for patients with chronic diseases in rural areas. J. Intell. Robot. Syst. 88, 163–180 (2017). https://doi.org/10.1007/s10846-017-0548-z
Park, J., Kim, S., Suh, K.: A comparative analysis of the environmental benefits of drone-based delivery services in urban and rural areas. Sustainability 10, 888 (2018). https://doi.org/10.3390/su10030888
Tomej, K., Liburd, J.J.: Sustainable accessibility in rural destinations: a public transport network approach. J. Sustain. Tour. 28, 222–239 (2019). https://doi.org/10.1080/09669582.2019.1607359
Swanson. D.: A simulation-based process model for managing drone deployment to minimize total delivery time. IEEE Eng. Manag. Rev. 47, 154–167 (2019). https://doi.org/10.1109/EMR.2019.2926245
Tang, C.S., Veelenturf, L.P.: The strategic role of logistics in the industry 4.0 era. Transp. Res. E 129, 1–11 (2019). https://doi.org/10.1016/j.tre.2019.06.004
Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11, 189 (2019). https://doi.org/10.3390/su11010189
Porru, S., Misso, F.E., Pani, F.E., Repetto, C.: Smart mobility and public transport: opportunities and challenges in rural and urban areas. J. Traffic. Transp. Eng. Engl. Ed. 7, 88–97 (2020). https://doi.org/10.1016/j.jtte.2019.10.002
Kirschstein, T.: Comparison of energy demands of drone-based and ground-based parcel delivery services. Trans. Res. Part D Trans. Environ. 78, 102209 (2020). https://doi.org/10.1016/j.trd.2019.102209
Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12, 2789 (2020). https://doi.org/10.3390/su12072789
Amicone, D., Cannas, A., Marci, A., Tortora, G.: A smart capsule equipped with artificial intelligence for autonomous delivery of medical material through drones. Appl. Sci. 11, 7976 (2021). https://doi.org/10.3390/app11177976
Euchi, J.: Do drones have a realistic place in a pandemic fight for delivering medical supplies in healthcare systems problems? Chinese J. Aeronaut. 129, 1–11 (2021). https://doi.org/10.1016/j.cja.2020.06.006
Acknowledgements
The authors would like to gratefully acknowledge the financial support received from the Malaysian Technical Standards Forum Berhad through the Industry Promotion and Development Grant (IPDG) under grant no. MTSFB/IPDG2020/GICT-02 and the Ministry of Higher Education, Malaysia through the Fundamental Research Grant Scheme (FRGS) under grant no. FRGS/1/2020/STG06/UCSI/02/1.
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Quek, S.G., Selvachandran, G., Sham, R., Siau, C.S., Ramli, M.H.M., Ahmad, N. (2022). A Fuzzy Logic Based Optimal Network System for the Delivery of Medical Goods via Drones and Land Transport in Remote Areas. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_121
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DOI: https://doi.org/10.1007/978-3-030-96308-8_121
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