An Intelligent Machine Learning Framework for Public Transport Passenger Demand Forecasting Using Time Series and Regression Models
Authors-M.Swathi, Srishti Kulkarni
Keyword-Passenger Demand Prediction, Public Transportation, Machine Learning, Prophet Model, ARIMA, Random Forest, Decision Tree, Linear Regression, Time Series Forecasting, Smart Transportation.
Accurate prediction of passenger demand is essential for improving the efficiency, reliability, and resource management of urban public transportation systems. Reliable forecasting enables transport authorities to optimize vehicle scheduling, reduce passenger waiting times, and enhance the overall quality of service. This study presents a comparative machine learning framework for forecasting passenger demand across stations of Lima Metro Line 1 using five predictive algo-rithms: Prophet, Linear Regression, Random Forest, Decision Tree, and AutoRegressive Inte-grated Moving Average (ARIMA). Historical passenger records collected from the official OSITRAN transportation database are utilized to train and evaluate the forecasting models. Prior to model development, the dataset undergoes comprehensive preprocessing, including data inte-gration, cleaning, datetime transformation, and passenger count aggregation to ensure consisten-cy and reliability. The trained models are assessed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) to determine their predic-tive capability. Experimental analysis demonstrates that the Prophet model consistently achieves the highest forecasting accuracy, producing the lowest prediction errors and the highest R² value among all evaluated models. Although Decision Tree and Linear Regression require considera-bly less computational time, they exhibit lower predictive performance compared with Prophet. The findings indicate that Prophet effectively captures temporal demand patterns and seasonal variations present in passenger transportation data, making it a practical and reliable solution for intelligent public transport planning, demand forecasting, and operational decision support in modern urban transit systems.
Doi-[https://doi.org/10.5281/zenodo.21735729]