Advances in Big Data Applications for transportation: airline, highway, and railway


  • Babek Erdebilli AYBU
  • Emine Nur NACAR Ankara Yıldırım Beyazıt University



Big data, transportation, airline, highway, railway


Aim: The purpose of this article is to present the latest advances in big data applications in the industries of the transportation sector such as airline, highway, and railway. It is difficult to analyze data in transportation because there is continuous real-time data flow. Since the improvements made are fast with the same logic, it is necessary to catch up with the new developments. Data should be analyzed with the big data concept because data stacks highly contain non-structural data types in transportation data. Although the mentioned industries are complementary to each other, the applications differ depending on the needs of the industry. Thus, solutions to specific problems in different industries using big data applications should be addressed.

Design / Research methods: In accordance with the purpose of the study, big data studies that provide added value to the transportation sector were examined. Studies have been filtered through some criteria which are whether the application is adaptable to the industry, the study is available online in full-text, and its references are from respectable sources.


Conclusions / findings: All the big data application studies in the academy are not adaptable in real-life problems or suitable for all situations. For this reason, trying all of the applications will lead to moral and material losses for firms. This study is a guideline for companies to follow the developments in the big data concept and to choose the one that suits their problems. Thus, the gap between academia and industry was tried to close.

Originality / value of the article: Although studies are referring to big data applications in the transportation sector, this study differs from others in terms of specifically analyzing big data applications in different industries such as airline, highway, and railway in the transportation sector


Attoh-Okine N. (2015), Big data challenges in railway engineering, in: Proceedings – 2014 IEEE International Conference on Big Data, doi: 10.1109/BigData.2014.7004424, pp. 7-9.

Boubiche S., Boubiche D.E., Bilami A., Toral-Cruz H. (2018), Big data challenges and data aggregation strategies in wireless sensor networks, IEEE Access, [09.06.2021].

Campos-Cordobés S., Del Ser J., Laña I., Olabarrieta I.I., Sánchez-Cubillo J., Sánchez-Medina J.J., Torre-Bastida A.I. (2018), Big data in road transport and mobility research, in: Ingelligent vehicles. Enabling technologies and future developments, Jiménez F. (ed.), Butterworth-Heinemann, Madrid, pp. 175-205.

Chen S., Huang Y., Huang W. (2016), Big data analytics on aviation social media. The case of China Southern Airlines on Sina Weibo, Proceedings – 2016 IEEE 2nd International Conference on Big Data Computing Service and Applications, doi: 10.1109/BigDataService.2016.51, pp. 152-155.

Cox M., Ellsworth D. (1997), Application-controlled demand paging for out-of-core visualization, in: Proceedings of the IEEE Visualization Conference, doi: 10.1109/visual.1997.663888, pp. 1-11.

Craver K.W. (2019), School libraries in a time of change. How to survive and thrive, Libraries Unlimited, Santa Barbara.

Denning P. (1990), The science of computing: saving all the bits, “American Scientist”, vol. 78 no. 5, pp. 402-405.

Ding H., Liu S., Cai S., Xia Y. (2019), Big data analysis of structural defects and traffic accidents in existing highway tunnels, in: International Conference on Information Technology in Geo-Engineering, Springer, Cham, pp. 189-195.

Dinis D., Barbosa-Póvoa A., Teixeira Â.P. (2019), A supporting framework for maintenance capacity planning and scheduling. Development and application in the aircraft MRO industry, “International Journal of Production Economics”, vol. 218, pp. 1-15.

Fumeo E., Oneto L., Anguita D. (2015), Condition based maintenance in railway transportation systems based on big data streaming analysis, “Procedia Computer Science”, vol. 53 no. 1, pp. 437-446.

Ghofrani F. et al. (2018), Recent applications of big data analytics in railway transportation systems: a survey, “Transportation Research Part C: Emerging Technologies”, vol. 90, pp. 226-246.

Hausladen I., Schosser M. (2020), Towards a maturity model for big data analytics in airline network planning, “Journal of Air Transport Management”, vol. 82, p. 101721.

Jamshidi A., Faghih-Roohi S., Hajizadeh S., Núñez A., Babuska R., Dollevoet R., Li Z., De Schutter B. (2017), A big data analysis approach for rail failure risk assessment, “Risk Analysis”, vol. 37 no. 8, pp. 1495-1507.

Kasturi E., Prasanna Devi S., Vinu Kiran S., Manivannan S. (2016), Airline route profitability analysis and optimization using bıg data analyticson aviation data sets under heuristic techniques, “Procedia Computer Science”, vol. 87, pp. 86-92.

Kim S., Shin D.H. (2016), Forecasting short-term air passenger demand using big data from search engine queries, “Automation in Construction”, vol. 70, pp. 98-108.

Li H., Quian B., Parikh D., Hampapur A. (2013), Alarm prediction in large-scale sensor networks. A case study in railroad, in: Proceedings – 2013 IEEE International Conference on Big Data, doi: 10.1109/BigData.2013.6691771, pp. 7-14.

Liang Y., Wu D., Liu G., Li Y., Gao C., Ma Z.J., Wu W. (2016), Big data-enabled multiscale serviceability analysis for aging bridges, “Digital Communications and Networks”, vol. 2 no. 3, pp. 97-107.

Ma J., Tse M., Wang X., Zhang M. (2019), Examining customer perception and behaviour through social media research. An empirical study of the United Airlines overbooking crisis, “Transportation Research Part E: Logistics and Transportation Review”, vol. 127, pp. 192-205.

Mital R., Coughlin J., Canaday M. (2015), Using big data technologies and analytics to predict sensor anomalies, in: Proceedings of the advanced Maui Optical and Space Surveillance Technologies Conference, Ryan S. (ed.), The Maui Economic Development Board, Maui.

Núñez A., Hendriks J., Li Z., De Schutter B., Dollevoet R. (2014), Facilitating maintenance decisions on the Dutch railways using big data. The ABA case study, in: Proceedings – 2014 IEEE International Conference on Big Data, IEEE, Washington D.C., pp. 48-53.

Nunez S.G., Attoh-Okine N. (2015), Metaheuristics in big data. An approach to railway engineering, in: Proceedings – 2014 IEEE International Conference on Big Data, doi: 10.1109/BigData.2014.7004430, pp. 42-47.

Odarchenko R., Hassan Z., Zaman A. (2019), Use of big data in aviation, in: Automated systems in the aviation and aerospace industries, doi: 10.4018/978-1-5225-7709-6.ch017, pp. 436-452.

Oneto L., Fumeo E., Clerico G., Canepa R. (2018), Train delay prediction systems. A big data analytics perspective, “Big Data Research”, vol. 11, pp. 54-64.

Pan L.I.U., Bo D. (2019), Design of integrated management and control system for mechanical and electrical equipment of new generation highway (road) tunnel, “Tunnel Construction”, vol. 39 no. S1, pp. 478-485.

Stylianou K., Dimitriou L., Abdel-Aty M. (2019), Big data and road safety. A comprehensive review, in: Mobility patterns, big data and transport analytics, doi: 10.1016/B978-0-12-812970-8.00012-9, pp. 297-343.

Thaduri A., Galar D., Kumar U. (2015), Railway assets. A potential domain for big data analytics, “Procedia Computer Science”, vol. 53 no. 1, pp. 457-467.

Tilly C. (1984), The Old Social History and the New Social Sciences, Research Foundation of State University of New York, 7(3), pp. 363–406.

Traffic Volumes and Trends (2019), U.S. Department of Transportation, Bureau of Transportation Statistics (BTS) calculation from U.S. Department of Transportation, Federal Highways Administration, Traffic Volumes and Trends, [29.05.2020].

Wang S.J., Moriarty P. (2018), Big data for urban sustainability, Springer Nature.

Zhang X., Gong D. (2014), Application of big data technology in marketing decisions for railway freight, in: International Conference of Logistics Engineering and Management, Shanghai, pp. 1136-1141.

Zhong R.Y., Newman S.T., Huang G.Q., Lan S. (2016), Big data for supply chain management in the service and manufacturing sectors. Challenges, opportunities, and future perspectives, “Computers and Industrial Engineering”, vol. 101, pp. 572-591.