Article

Artificial neural networks applied to taxi destination prediction

Authors:

Alexandre De Brébisson,

Étienne Simon,

Pascal Vincent,

Yoshua BengioAuthors Info & Claims

ECMLPKDDDC'15: Proceedings of the 2015th International Conference on ECML PKDD Discovery Challenge - Volume 1526

Pages 40 - 51

Published: 07 September 2015 Publication History

Abstract

We describe our first-place solution to the ECML/PKDD discovery challenge on taxi destination prediction. The task consisted in predicting the destination of a taxi based on the beginning of its trajectory, represented as a variable-length sequence of GPS points, and diverse associated meta-information, such as the departure time, the driver id and client information. Contrary to most published competitor approaches, we used an almost fully automated approach based on neural networks and we ranked first out of 381 teams. The architectures we tried use multi-layer perceptrons, bidirectional recurrent neural networks and models inspired from recently introduced memory networks. Our approach could easily be adapted to other applications in which the goal is to predict a fixed-length output from a variable-length sequence.

References

[1]

Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks. In International Conference on Artificial Intelligence and Statistics , pages 315-323, 2011.

[2]

Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. A neural probabilistic language model. The Journal of Machine Learning Research , 3:1137-1155, 2003.

[3]

Yoshua Bengio, Patrice Simard, and Paolo Frasconi. Learning long-term dependencies with gradient descent is difficult. Neural Networks, IEEE Transactions on , 5(2):157-166, 1994.

[4]

Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation , 9(8):1735-1780, 1997.

[5]

Alex Graves, Marcus Liwicki, Santiago Fernández, Roman Bertolami, Horst Bunke, and Jürgen Schmidhuber. A novel connectionist system for unconstrained handwriting recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on , 31(5):855-868, 2009.

[6]

Patrick Doetsch, Michal Kozielski, and Hermann Ney. Fast and robust training of recurrent neural networks for offline handwriting recognition. In Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on , pages 279-284. IEEE, 2014.

[7]

Alan Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. Speech recognition with deep recurrent neural networks. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on , pages 6645-6649. IEEE, 2013.

[8]

Hasim Sak, Andrew Senior, and Françoise Beaufays. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv preprint arXiv:1402.1128 , 2014.

[9]

Kelvin Xu, Jimmy Ba, Ryan Kiros, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, and Yoshua Bengio. Show, attend and tell: Neural image caption generation with visual attention. arXiv preprint arXiv:1502.03044 , 2015.

[10]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 , 2014.

[11]

Alex Graves, Santiago Fernández, and Jürgen Schmidhuber. Bidirectional lstm networks for improved phoneme classification and recognition. In Artificial Neural Networks: Formal Models and Their Applications-ICANN 2005 , pages 799-804. Springer, 2005.

[12]

Jason Weston, Sumit Chopra, and Antoine Bordes. Memory networks. arXiv preprint arXiv:1410.3916 , 2014.

[13]

Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research , 9(2579-2605):85, 2008.

[14]

Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian J. Goodfellow, Arnaud Bergeron, Nicolas Bouchard, and Yoshua Bengio. Theano: new features and speed improvements. Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012.

[15]

James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio. Theano: a CPU and GPU math expression compiler. In Proceedings of the Python for Scientific Computing Conference (SciPy) , June 2010. Oral Presentation.

[16]

B. van Merriënboer, D. Bahdanau, V. Dumoulin, D. Serdyuk, D. Warde-Farley, J. Chorowski, and Y. Bengio. Blocks and Fuel: Frameworks for deep learning. ArXiv e-prints , June 2015.

Cited By

Luca MBarlacchi GLepri BPappalardo L(2021)A Survey on Deep Learning for Human MobilityACM Computing Surveys10.1145/348512555:1(1-44)Online publication date: 23-Nov-2021
https://dl.acm.org/doi/10.1145/3485125
Chen CLiao CXie XWang YZhao J(2019)Trip2VecPersonal and Ubiquitous Computing10.1007/s00779-018-1175-923:1(53-66)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s00779-018-1175-9
Huang ZShan GCheng JSun J(2019)TRecNeural Computing and Applications10.1007/s00521-018-3728-231:1(209-222)Online publication date: 17-May-2019
https://dl.acm.org/doi/10.1007/s00521-018-3728-2
Show More Cited By

Index Terms

Artificial neural networks applied to taxi destination prediction
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

Recommendations

Artificial neural networks: learning algorithms, performance evaluation, and applications
Prediction of subsidence due to underground mining by artificial neural networks

Alternatively to empirical prediction methods, methods based on influential functions and on mechanical model, artificial neural networks (ANNs) can be used for the surface subsidence prediction. In our case, the multi-layer feed-forward neural network ...
Comparison of artificial neural networks using prediction benchmarking
ACMOS'11: Proceedings of the 13th WSEAS international conference on Automatic control, modelling & simulation

Artificial neural networks are commonly used for prediction of various time series, linear and nonlinear systems. Nevertheless, the choice of proper type of artificial neural networks is difficult task, because each class of artificial neural networks ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

ECMLPKDDDC'15: Proceedings of the 2015th International Conference on ECML PKDD Discovery Challenge - Volume 1526

September 2015

74 pages

Editors:
Adolfo Martínez-Usó
Universitat Politècnica de Valencia, Dept. of Computer Systems and Computation, Valencia, Spain
,
João Mendes-Moreira
Faculty of Engineering of University of Porto, Department of Informatics Engineering and Laboratory of Artificial Intelligence and Decision Support, INESC TEC, Porto, Portugal
,
Luís Moreira-Matias
NEC Laboratories Europe, Heidelberg, Germany
,
Meelis Kull
Intelligent Systems Laboratory, University of Bristol, UK
,
Nicolas Lachiche
Engineering, Computer and Imaging Sciences Laboratory, University of Strasbourg, France

Publisher

CEUR-WS.org

Aachen, Germany

Publication History

Published: 07 September 2015

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Luca MBarlacchi GLepri BPappalardo L(2021)A Survey on Deep Learning for Human MobilityACM Computing Surveys10.1145/348512555:1(1-44)Online publication date: 23-Nov-2021
https://dl.acm.org/doi/10.1145/3485125
Chen CLiao CXie XWang YZhao J(2019)Trip2VecPersonal and Ubiquitous Computing10.1007/s00779-018-1175-923:1(53-66)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s00779-018-1175-9
Huang ZShan GCheng JSun J(2019)TRecNeural Computing and Applications10.1007/s00521-018-3728-231:1(209-222)Online publication date: 17-May-2019
https://dl.acm.org/doi/10.1007/s00521-018-3728-2
Zhang HWu HSun WZheng B(2018)DeeptravelProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304222.3304276(3655-3661)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304222.3304276
Bodunov OSchmidt FMartin ABrito AFetzer C(2018)Real-time Destination and ETA Prediction for Maritime TrafficProceedings of the 12th ACM International Conference on Distributed and Event-based Systems10.1145/3210284.3220502(198-201)Online publication date: 25-Jun-2018
https://dl.acm.org/doi/10.1145/3210284.3220502

View Options

View options

Media

Figures

Other

Tables

View Table of Contents