| E. Dritsas, M. Trigka, Ph. Mylonas |
| Big Data-Driven Trip Duration Prediction in Urban Transportation Systems |
| 16th International Conference on Information, Intelligence, Systems and Applications (IISA2025), 10-12 July 2025, Mytilene, Greece |
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ABSTRACT
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| This paper presents a machine learning (ML)-based approach to trip duration prediction using a large-scale mobility dataset from New York City (NYC) yellow taxi, incorporating both raw and engineered features to model spatiotemporal and fare-related factors that influence trip duration. Six regression models including Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP), were trained and evaluated using standard metrics namely, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2). The RF model achieved the best performance, with an MAE of 53.83 sec, an RMSE of 143.38 sec, and an R2 score of 0.929. These results demonstrate the suitability of ensemble tree-based models for predictive analytics in intelligent transport systems and outline future directions for enhancing performance using contextual and real-time data streams.
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| 10 July , 2025 |
| E. Dritsas, M. Trigka, Ph. Mylonas, "Big Data-Driven Trip Duration Prediction in Urban Transportation Systems", 16th International Conference on Information, Intelligence, Systems and Applications (IISA2025), 10-12 July 2025, Mytilene, Greece |
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