TechRxiv preprint: machine learning framework predicts bus arrival times, random forest shows lowest error

[TechRxiv] A TechRxiv preprint by Ramanayake and co-authors presents a machine learning-based framework for predicting vehicle arrival times when origin and destination are not fixed. The study developed and evaluated Linear Regression, Artificial Neural Networks, Long Short-Term Memory networks, Decision Trees and Random Forests, using data collected at fixed intervals from public transport routes in Sri Lanka, with Mean Absolute Error (MAE) used to assess performance. According to the authors, Linear Regression showed the highest error and Random Forest the lowest, and model effectiveness was directly influenced by both the data collection methodology and the choice of features. The authors said that with further improvements in dataset quality and model design, the framework has strong potential for practical deployment in applications where accurate arrival time prediction is critical, such as public transportation management and taxi services. The paper is a preprint and has not been peer-reviewed.