A Novel Approach for Fare Prediction Using Machine Learning Techniques

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Published Nov 26, 2021
https://doi.org/10.47164/ijngc.v12i5.451
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Volume 12, Special Issue 5, November 2021

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Kunal Khandelwal
Shri Ramdeobaba College of Engineering and Management, Nagpur
Atharva Sawarkar
Shri Ramdeobaba College of Engineering and Management
Dr. Swati Hira
Shri Ramdeobaba College of Engineering and Management

Abstract

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.

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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
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Scopus
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Europe PMC

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