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Taxi Fare and Ride Demand Prediction Using Machine Learning Techniques

D. NANDHINI., MCA

(Assistant Professor, Master of Computer Applications)

S. VASANTHAN., MCA

Christ College of Engineering and Technology

Moolakulam, Oulgaret Municipality, Puducherry – 605010.

Abstract—Urban transportation systems generate vast amounts of trip-related data through taxi and ride-hailing services. Accurate prediction of taxi fares and ride demand is essential for improving passenger satisfaction, optimizing driver allocation, and enhancing operational efficiency. Traditional fare estimation methods rely on fixed tariff rules and simple distance–time calculations, which fail to capture complex real-world factors such as traffic congestion, travel patterns, and temporal demand variations.

This paper proposes an intelligent Taxi Fare and Ride Prediction System using machine learning techniques to provide accurate and data-driven fare estimation and ride demand analysis. The system analyzes historical taxi trip data and key influencing features such as pickup and drop-off locations, trip distance, travel time, passenger count, traffic conditions, weather factors, and surge pricing. Data preprocessing techniques including data cleaning, normalization, and feature selection are applied to improve prediction accuracy. Supervised machine learning algorithms such as Linear Regression, Decision Tree Regression, Random Forest, and Gradient Boosting models are trained and evaluated using standard performance metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score.

The proposed system demonstrates reliable prediction performance and efficient processing, making it suitable for real-world transportation analytics. The results confirm that machine learning-based models significantly outperform traditional rule-based approaches and provide a scalable solution for intelligent transportation systems.

Keywords—Taxi Fare Prediction, Ride Demand Prediction, Machine Learning, Regression Models, Intelligent Transportation Systems, Predictive Analytics.