Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{161160,
        author = {AYESHA SIDDIQ and SHAMAMAH FIRDOUS},
        title = {CAB FARE PREDICTION BASED ON TIME SERIES WITH ML TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {2},
        pages = {852-856},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=161160},
        abstract = {In recent years, the taxi service industry has been booming and is expected to experience significant growth in the short term. Due to this growing demand, many companies have sprung up to offer cab rides to users. However, few companies charge higher fares for the same route. Therefore, customers have to pay an unwanted high amount even though the prices should be lower. The main objective is to estimate travel costs before booking a cab to have transparency and avoid unfair practices.
Our system is designed to allow individuals to estimate taxi trip fares by using various dynamic conditions such as: -
• Weather
• Cab availability
• Cab size and
• The distance between two locations.
The data that is already present helps in creating a mathematical model that records essential trends. This model is used to predict the future or suggest optimal outcomes. Different techniques and methods have been used to implement this system, e.g., Machine Learning, Supervised Learning, Regression Techniques, Random Forest, and parameter tuning (increasing model accuracy).
},
        keywords = {},
        month = {},
        }