Vehicle Travel Time Estimation Using Sequence Prediction

Keywords: travel time estimation, recurrent neural networks, sequence prediction, intelligent transportation systems

Abstract

This paper proposes a region-based travel time and traffic speed prediction method using sequence prediction. Floating Car Data collected from 8,317 vehicles during 34 days are used for evaluation purposes. Twelve districts are chosen and the spatio-temporal non-linear relations are learned with Recurrent Neural Networks. Time estimation of the total trip is solved by travel time estimation of the divided sub-trips, which are constituted between two consecutive GNSS measurement data. The travel time and final speed of sub-trips are learned with Long Short-term Memory cells using sequence prediction. A sequence is defined by including the day of the week meta-information, dynamic information about vehicle route start and end positions, and average travel speed of the road segment that has been traversed by the vehicle. The final travel time is estimated for this sequence. The sequence-based prediction shows promising results, outperforms function mapping and non-parametric linear velocity change based methods in terms of root-mean-square error and mean absolute error metrics.

Author Biographies

Gültekin Gündüz, Sabanci University, Istanbul, Turkey

Gultekin Gündüz  received the B.Sc. degree from the Department of Computer Science and Engineering, Sabanci University, Istanbul, Turkey, in 2014, and is currently working toward the M.Sc. degree in computer engineering at Galatasaray University, Istanbul. His research interests include advanced driver assistance systems, driver behavior modeling, and machine learning.

Tankut Acarman, Faculty of Engineering and Technology, Galatasaray University, Istanbul, Turkey

Tankut Acarman received the Ph.D. degree in electrical and computer engineering from Ohio State University, Columbus, OH, USA, in 2002. He is a Professor and the Head of the Department of Computer Engineering, Galatasaray University, Istanbul, Turkey. He is a coauthor of the book titled Autonomous Ground Vehicles. His research interests include aspects of intelligent vehicle technologies, driver assistance systems, and performance evaluation of intervehicle communication.

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Published
2020-01-20
How to Cite
1.
Gündüz G, Acarman T. Vehicle Travel Time Estimation Using Sequence Prediction. PROMET [Internet]. 2020Jan.20 [cited 2020Feb.21];32(1):1-12. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/3008
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Articles