Abstract
Adapting to the growing aviation traffic has become an immense challenge on a global scale. The aviation industry has become a crucial component of our society and is a major force behind global economic, social, and cultural advancement. Our research examines aviation connectivity using network centrality based on topological characteristics. The goal is to achieve urban sustainability by analyzing the airways transportation system, especially when some airports are not functioning due to epidemics or chaotic outbursts. For this purpose, this research has proposed an efficient model for recommending a set of convenient alternative routes between airports based on a number of connecting flights and customizing the route discovery. We have performed urban transportation analytics for India and generalized the approach for worldwide air routes. Our work will assist the passengers in selecting the best routes based on their preferences of cost, distance, and places to visit during the journey. Our country-specific as well as worldwide case studies are performed with geo-visualizations, along with the network centrality and optimal route statistics.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
Research data associated with this paper is acquired from OpenFlights, which is available on public domain.
Code Availability
The research code will be made available on-request from readers.
Abbreviations
- ITS :
-
Intelligent Transportation System
- IATA :
-
International Air Transport Association
- ICAO :
-
International Civil Aviation Organization
- LAT :
-
Latitude
- LONG :
-
Longitude
- ALT :
-
Altitude
- Tz DT :
-
Timezone in “tz” (Olson) format in Database Time
- DST :
-
Daylight Saving Time
- GDP :
-
Gross Domestic
- AAI :
-
Airport Authority of India
- PPA :
-
Population per Unit
References
Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., Chen, C.: Data-driven intelligent transportation systems: A survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011)
Kathuria, A., Parida, M., Sekhar, C.R.: A review of service reliability measures for public transportation systems. Int. J. Intell. Transp. Syst. Res. 18, 243–255 (2020)
Hamidi, H., Kamankesh, A.: An approach to intelligent traffic management system using a multi-agent system. Int. J. Intell. Transp. Syst. Res. 16, 112–124 (2018)
OpenFlights Dataset: https://openflights.org/data.html. Accessed on Feb 2022, (2017).
Census 2011, dataset - https://www.kaggle.com/gokulrajkmv/indian-statewise-data-from-rbi, date of last access: Feb 2023
Aamir, M., Masroor, S., Ali, Z.A., Ting, B.T.: Sustainable framework for smart transportation system: A case study of Karachi. Wirel. Pers. Commun. 106(1), 27–40 (2019)
Wang, K., Fu, X.: Research on centrality of urban transport network nodes. In: AIP Conference Proceedings, 1839(1):020181. AIP Publishing LLC (2017).
Kastell, K.A.: Network planning for intelligent transportation systems based on existing wireless networks. In: 2013 5th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), pp. 28–32. IEEE (2013)
Figueiredo, L., Jesus, I., Tenreiro Machado, J.A., Ferreira, J.R., Martins De Carvalho, J.L.: Towards the development of intelligent transportation systems. In ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No. 01TH8585), pp. 1206–1211. IEEE (2001)
An, S-h., Lee, B-H,. Shin, D-R.: A survey of intelligent transportation systems. In: 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks, pp. 332–337. IEEE (2011)
Andersen, J., Sutcliffe, S.: Intelligent transport systems (its)-an overview. IFAC Proc. Vol. 33(18), 99–106 (2000)
Jain, S., Sinha, A.: Social network sustainability for transport planning with complex interconnections. Sustain. Comput.: Inform. Syst. 24(100351), 1–9 (2019)
Wild, G., Baxter, G., Srisaeng, P., Richardson, S.: Machine learning for air transport planning and management. In AIAA Aviation Forum. (3706), 1–9 (2022)
Yen, J.Y.: An algorithm for finding shortest routes from all source nodes to a given destination in general networks. Q. Appl. Math. 27(4), 526–530 (1970)
Nicholson, T., Alastair, J.: Finding the shortest route between two points in a network. Comput. J. 9(3), 275–280 (1966)
Jain, S., Sinha, A.: TriBeC: identifying influential users on social networks with upstream and downstream network centrality. Int. J. Gen. Syst. 52(3), 275–296 (2023)
Mills, G.: A decomposition algorithm for the shortest-route problem. Oper. Res. 14(2), 279–291 (1966)
Beardwood, J., Halton, J.H., Hammersley, J.M.: The shortest path through many points. In: Mathematical Proceedings of the Cambridge Philosophical Society, vol. 55, no. 4, pp. 299–327. Cambridge University Press (1959)
Golabi, M., Mazyar Ghadiri, N.: Intelligent and fuzzy UAV transportation applications oin aviation 4.0. In: Intelligent and Fuzzy Techniques in Aviation 4.0, pp. 431–458. Springer, Cham (2022)
Aarthy, C.C.J., Narayanan, M.B., Kumar, G.R., Jayasundaram, J., Saikrishna, S., Kumar, C.R.: Big data analytics and an intelligent aviation information management system. Turk. J. Comput. Math. Educ. 12(11), 4328–4340 (2021)
Mustapha, S., Chong, C. A., Mohammed, M. N.: Review on the usage of mixed reality and augmented reality assisted learning tool in aircraft maintenance. In: 2021 IEEE 9th Conference on Systems, Process and Control (ICSPC 2021), pp. 168–173 (2021)
Sun, X., Wandelt, S.: Robustness of air transportation as complex networks: Systematic review of 15 years of research and outlook into the future. Sustainability 13(11), 6446 (2021)
Chung, S.-H., Ma, H.-L., Hansen, M., Tsan-Ming, C.: Data science and analytics in aviation. Transp. Res. E. 134, 101837 (2020)
Yun, J., Youyuan, X.: Study about Intelligent aviation innovation service based on artificial intelligence technology. In: 2021 4th International Conference on E-Business, Information Management and Computer Science (EBIMCS 2021), pp. 375–378 . Association for Computing Machinery, New York (2022)
Abdul-Aziz, Q., Hashemi, H.H.: Innovating aircraft control systems with the use of artificial intelligence and electronics. Adv. Control Appl.: Eng. Ind. Syst. 4(3), e111 (2022). https://doi.org/10.1002/adc2.111
Chen, J., Zhang, Y., Teng, S., Chen, Y., Zhang, H., Wang, F. -Y.: ACP-based energy-efficient schemes for sustainable intelligent transportation systems. In IEEE Trans. Intell. Veh. 8(5), 3224–3227 (2023)
Zhou, Z., Cai, M., Xiong, C., Deng, Z., Yu, Y.: Construction of autonomous transportation system architecture based on system engineering methodology. In: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pp. 3348–3353. IEEE (2022)
Acknowledgements
The authors express gratitude to the institutional support provided in form of server and internetworking support for data analysis and visualization.
Funding
The authors have not received any financial assistance or grant for research in this manuscript.
Author information
Authors and Affiliations
Contributions
Conceptualization was done by S., A.A., R.J. and A.S.; methodology was proposed by S. and A.A.; literature suvey was conducted by M.G.; resources and data curation by R.J.; Code development and debugging by S., A.A., R.J.; formal analysis by A.S.; program validation by A.S., original draft preparation by S., A.A., R.J.; review and editing by A.S. and M.G.; supervision and project administration by A.S. All authors have read and agreed to the final version of the manuscript.
Corresponding author
Ethics declarations
Ethics Approval and Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Agarwal, A., Shreeji, Jain, R. et al. Smart Aviation with Customized Route Discovery Using Urban Transportation Analytics. Int. J. ITS Res. 22, 229–244 (2024). https://doi.org/10.1007/s13177-024-00390-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13177-024-00390-8