A Novel Approach for Fare Prediction Using Machine Learning Techniques


Kunal Khandelwal
Atharva Sawarkar
Dr. Swati Hira


A survey suggests that the Flight and Cab fares vary according to various factors like location, time of the day, etc. The airline companies put into effect dynamic pricing for the flight tickets. Also, it changes with the festival, holiday season and weekends. So, what’s an excellent time to buy a flight ticket? The same can be seen with cabs as well, where the fare depends upon the number of passengers, traffic, etc. The seller has information about all of the factors, but the buyers are able to access the information that is limited through which we cannot predict the tariffs. Considering the characteristics like time of departure, the number of days left for departure and time of the day, it’ll give the prime time to purchase the ticket. Likewise, the cab companies like Uber and Ola use factors like traffic in a particular location, demand and supply factors, for example when the demand for cabs is high there is a hike in prices but when the demand for cabs is not high the prices are calculated normally according to their algorithm. Availability of drivers and type of car to travel in are also crucial factors in determining the cab fares. The motive of the paper is to analyse the factors that influence the deviation within the tariffs and the way they’re associated with the change within the prices. The impetus for the research paper is to inspect the elements which have an impact on the deviations in the tariffs and how they could be related to the variation within the prices. Using this data, build an algorithm that can assist buyers to buy a ticket at an optimal time when they get the maximum benefits and minimum fares.


Author Biographies

Kunal Khandelwal, Shri Ramdeobaba College of Engineering and Management, Nagpur

Kunal Khandelwal is currently pursuing Computer Science Engineering \& is a Final Year Student at Shri Ramdeobaba College of Engineering \&Management.He is a Beta Microsoft Learn Student Ambassador who loves learning new technologies. At his college, he has conducted various technical events as he was a Core Team Member at Google DSC RCOEM \& is an Executive Member of ACM Society of RCOEM. He is an active member of IAENG. Lately, he explored the field of Machine Learning \& ever since has enjoyed building various projects in the same.

Atharva Sawarkar, Shri Ramdeobaba College of Engineering and Management

Atharva Sawarkar is currently studying in the final year of Computer Science Engineering at Shri Ramdeobaba College of Engineering and Management, Nagpur. In his college days, he has developed coding skills and has been actively participating in various coding challenges because he loves problem-solving. He is also interested in learning new technologies through which real-life problems can be solved. From the past year, he has shown some interest in the field of Machine Learning.
E-mail: [email protected]

Dr. Swati Hira, Shri Ramdeobaba College of Engineering and Management

Dr. Swati Hira is currently Assistant Professor at Shri Ramdeobaba College of Engineering and Management,
India. She did her M.Tech. in Computer Science from D.A.V.V, Indore and PhD from Visvesvaraya National
Institute of Technology, Nagpur. She has published several research papers in reputed SCI/SCIE/Scopus journals in areas as Multidimensional Modelling, Statistical Data mining, and Machine Intelligence. She is an active member of ACM and IAENG. She is also a reviewer of some journals as Knowledge-based systems, Computers in Biology and Medicines.
E-mail: [email protected]

How to Cite
Khandelwal, K., Sawarkar, A. ., & Hira, S. (2021). A Novel Approach for Fare Prediction Using Machine Learning Techniques. International Journal of Next-Generation Computing, 12(5). https://doi.org/10.47164/ijngc.v12i5.451


  1. W. Groves & M. Gini, —An agent for optimizing airline ticket purchasing,12th International
  2. Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), St. Paul, MN, May
  3. - 10, 2013, pp. 1341-1342
  4. T. Janssen, -A linear quantile mixed regression model for prediction of airline ticket prices,‖
  5. Bachelor Thesis, Radboud University, 2014
  6. Wohlfarth, T. Clemencon, S.Roueff,-A Data mining approach to travel price forecasting,
  7. th international conference on machine learning Honolulu 2011.
  8. Dominguez-Menchero, J.Santo, Riviera, ‖optimal purchase timing in airline markets‖,2014
  9. L. Breiman, —Random forests,‖ Machine Learning, vol. 45, pp. 5- 32, 2001.
  10. Data science- Public datasets, [Online] Available: https://www.kaggle.com
  11. Sebastian Raschka, Model Evaluation, Model Selection, and Algorithm Selection in Machine
  12. Learning, [Online] Available https://arxiv.org/abs/1811.12808,2016
  13. Weijie Wang & Yanmin Lu, Analysis of the Mean Absolute Error (MAE) and the Root
  14. Mean Square Error (RMSE) in Assessing Rounding Model, ICMEMSCE, IOP Publishing, 324
  15. (2018), doi:10.1088/1757-899X/324/1/0120
  16. Vanajakshi, L., S. C. Subramanian, and R. Sivanandan. ”Travel time prediction under
  17. heterogeneous traffic conditions using global positioning system data from buses.” IET intelligent
  18. transport systems 3.1 (2009): 1-9.
  19. Biagioni, James, et al. ”Easytracker: automatic transit tracking, mapping, and arrival
  20. time prediction using smartphones.” Proceedings of the 9th ACM Conference on Embedded
  21. Networked Sensor Systems. ACM, 2011.
  22. Yildirimoglu, Mehmet, and Nikolas Geroliminis. ”Experienced travel time prediction for
  23. congested freeways.” Transportation Research Part B: Methodological 53 (2013): 45-63.
  24. Wu, Chun-Hsin, Jan-Ming Ho, and Der-Tsai Lee. ”Travel-time prediction with support
  25. vector regression.” IEEE transactions on intelligent transportation systems 5.4 (2004): 276-281.
  26. Van Lint, J. W. C., S. P. Hoogendoorn, and Henk J. van Zuylen. ”Accurate freeway travel
  27. time prediction with state-space neural networks under missing data.” Transportation Research
  28. Part C: Emerging Technologies 13.5 (2005): 347-369.
  29. Kelareva, Elena. ”Predicting the Future with Google Maps APIs.” Web blog post. Geo
  30. DevelopersBlog,https://maps-apis.googleblog.com/2015/11/predicting-future-with-google-maps-apis.html
  31. Accessed 15 Dec. 2016.
  32. Uber Revenue And Usage Statistics: https://www.businessofapps.com/data/uber-statistics/
  33. Logistics: https://www.statista.com/statistics/588028/passengers-boarded-by-type-by-indianair-carriers/ [17]Python Libraries:https://towardsdatascience.com/top-10-python-libraries-for-datascience-cd82294ec266
  34. Introduction to Machine Learning with Python: A Guide for Data Scientists: Book by
  35. Andreas C. M¨uller and Sarah Guido
  36. Zhao, Z., You, J., Gan, G. et al. Civil airline fare prediction with a multi-attribute dual-stage attention mechanism. Appl Intell (2021). https://doi.org/10.1007/s10489-021-02602-0
  37. Upadhyay, Rishabh, and Lui, Simon. (2017). Taxi Fare Rate Classification Using Deep
  38. Networks.