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Travel Time Prediction on Long-Distance Road Segments in Thailand
Author(s)
Date Issued
June 1, 2022
Type
Article
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.
Citation
Applied Sciences Switzerland, 12(11), 2022