Ride Sharing Demand Prediction System Using Machine Learning

• Author(s): Harsh Nagar; Dr. S. Balamurugan
• Paper ID: 1717922
• Page: 2386-2391
• Published Date: 19-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

Ride-sharing platforms such as Uber, Ola, and Lyft have transformed modern transportation systems by offering convenient and affordable mobility services. However, one of the major challenges faced by these platforms is the accurate prediction of ride demand across different locations and time periods. Inaccurate demand forecasting may lead to driver shortages, long passenger waiting times, surge pricing issues, and inefficient resource allocation. Traditional forecasting approaches often fail to adapt to rapidly changing urban transportation conditions influenced by weather, traffic congestion, public events, and peak travel hours. This research presents a machine learning-based ride-sharing demand prediction system designed to analyze historical ride data and forecast future ride demand with improved accuracy. The proposed system utilizes machine learning algorithms such as Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) models to identify hidden patterns in ride requests. Important features including date, time, weather conditions, pickup location, traffic density, and historical ride trends are used as input parameters for the predictive model. The performance of the proposed system is evaluated using multiple machine learning evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and prediction accuracy. Experimental results indicate that machine learning approaches significantly improve demand prediction performance compared to traditional statistical methods. The proposed system can assist ride-sharing companies in optimizing driver allocation, reducing passenger waiting time, minimizing operational costs, and improving customer satisfaction. This research contributes to the development of intelligent transportation systems and smart city infrastructure by providing a scalable and data-driven solution for urban ride demand forecasting.

Citations

IRE Journals:
Harsh Nagar, Dr. S. Balamurugan "Ride Sharing Demand Prediction System Using Machine Learning" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2386-2391 https://doi.org/10.64388/IREV9I11-1717922

IEEE:
Harsh Nagar, Dr. S. Balamurugan "Ride Sharing Demand Prediction System Using Machine Learning" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717922