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Title	Travel Time Prediction: Comparison Of Machine Learning Algorithms In A Case Study
ID_Doc	58952
Authors	Goudarzi F.
Year	2019
Published	Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
DOI	http://dx.doi.org/10.1109/HPCC/SmartCity/DSS.2018.00232
Abstract	Travel time prediction has important applications within the field of intelligent transportation, such as vehicle routing, congestion and traffic management. A challenging task in travel time prediction is obtaining data that is not readily available, as a clear majority of links in roads network are not equipped with traffic sensors. In this paper, data of travel time is collected for a link using Google Maps Application Programming Interface (API). Then, travel times are predicted for short horizons of up to one hour on the link by applying machine learning algorithms. The Mean Absolute Error (MAE) of predictions are compared. The study indicates that a shallow Artificial Neural Network (ANN) can provide more accurate prediction than the other algorithms. © 2018 IEEE.
Author Keywords	Google maps API; Linear regression; Machine learning; Nearest neighbor; Neural network; Travel time prediction

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