Taxi Fare Prediction using Random Forest: A Data-Driven Approach based on Pickup/Dropoff Locations and Ride Attributes

The accurate estimation of taxi fares is critical for enhancing pricing fairness and optimizing operations within the transportation sector. This paper presents a Random Forest-based approach for predicting taxi fares, utilizing key factors such as the pickup and drop-off locations, timestamp of pickup, and passenger count. A sizable dataset of past taxi trip data is used to build the model, which enables the computer to discover intricate relationships and patterns among the many input variables. The efficiency of the model in producing accurate fare estimates is evaluated using common metrics, such as mean absolute error (MAE) and root mean squared error (RMSE). The Random Forest model provides a reliable solution for real-time fare prediction and exhibits considerable accuracy gains when compared to more straightforward techniques. The findings demonstrate how this strategy might increase fare transparency and operational effectiveness for taxi services, which would be advantageous to both passengers and service providers by providing more accurate and up-to-date fare estimates.