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Title	Travel Time Prediction On Long-Distance Road Segments In Thailand
ID_Doc	58951
Authors	Chawuthai R.; Ainthong N.; Intarawart S.; Boonyanaet N.; Sumalee A.
Year	2022
Published	Applied Sciences (Switzerland), 12, 11
DOI	http://dx.doi.org/10.3390/app12115681
Abstract	This study proposes a method by which to predict the travel time of vehicles on long-distance road segments in Thailand. We adopted the Self-Attention Long Short-Term Memory (SA-LSTM) model with a Butterworth low-pass filter to predict the travel time on each road segment using historical data from the Global Positioning System (GPS) tracking of trucks in Thailand. As a result, our prediction method gave a Mean Absolute Error (MAE) of 12.15 min per 100 km, whereas the MAE of the baseline was 27.12 min. As we can estimate the travel time of vehicles with a lower error, our method is an effective way to shape a data-driven smart city in terms of predictive mobility. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Author Keywords	GPS data analytics; machine learning; smart mobility; travel time prediction

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