Predicting Ride Costs With Advanced Regression Models

Abstract

Accurate Ride Cost Prediction Has Become Increasingly Important As Ride-hailing Businesses Continue To Grow, The Ability To Accurately Predict Rides Costs Becomes More Crucial Than Ever, As It Directly Influences Pricing Efficiency, Operational Planning, And Customer Satisfaction. The Existing Fare Estimation Techniques Are Often Inadequate To Embody The Complex And Dynamic Relationships Among Various Factors Like Trip Distance, Traffic Conditions, Weather, Demand Fluctuation And Travel Time, Which Necessitate More Sophisticated Predictive Solution. This Research Suggests The Introduction Of A Ride Cost Prediction System Leveraging A Machine Learning Approach With Advanced Regression Models Such As Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, XGBoost Regressor, And LightGBM Regressor. These Models Can Be Used To Capture Nonlinearities In Ride-hailing Data And To Make More Accurate Fare Predictions. Data Preprocessing Methods Include Handling Missing Values, Scaling Features, Transforming Features, And Feature Engineering To Improve Model Accuracy. Other Enhancements—such As Rush-hour Flags, Weather Classifications, And Distance-based Measurements—are Added To Boost Predictive Power. The Performance Of The Models Is Assessed Based On The Standard Regression Performance Metrics Such As Mean Absolute Error (MAE), Mean Squared Error (MSE) And R-squared (R²) Score. Experimental Results Show That Ensemble Learning Methods, Especially XGBoost And Gradient Boosting, Predict With A Higher Accuracy Than Classical Regression Methods. Moreover, Feature Importance Analysis Reveals The Most Important Factors Affecting The Rides' Cost, Giving Insights About The Mechanisms Of Fare Determination. The Study Overall Demonstrates How Well Advanced Regression Models Can Provide Accurate And Reliable Ride Cost Estimates, Helping Ride-hailing Platforms Optimize Pricing Strategies, Enhance Transparency, And Boost User Satisfaction.

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