Deep Representation Learning for Trajectory Similarity Computation

Trajectory similarity computation is fundamental functionality with many applications such as animal migration pattern studies and vehicle trajectory mining to identify popular routes and similar drivers. While a trajectory is a continuous curve in some spatial domain, e.g., 2D Euclidean space, trajectories are often represented by point sequences. Existing approaches that compute similarity based on point matching suffer from the problem that they treat two different point sequences differently even when the sequences represent the same trajectory. This is particularly a problem when the point sequences are non-uniform, have low sampling rates, and have noisy points. We propose the first deep learning approach to learning representations of trajectories that is robust to low data quality, thus supporting accurate and efficient trajectory similarity computation and search. Experiments show that our method is capable of higher accuracy and is at least one order of magnitude faster than the state-of-the-art methods for k-nearest trajectory search.

[1]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[2]  Geoffrey E. Hinton,et al.  Grammar as a Foreign Language , 2014, NIPS.

[3]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[4]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Yoshua Bengio,et al.  On Using Very Large Target Vocabulary for Neural Machine Translation , 2014, ACL.

[6]  Alexandr Andoni,et al.  Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[7]  Alan Edelman,et al.  Julia: A Fresh Approach to Numerical Computing , 2014, SIAM Rev..

[8]  Jae-Gil Lee,et al.  MoveMine: Mining moving object data for discovery of animal movement patterns , 2011, TIST.

[9]  Ralf Hartmut Güting,et al.  Realm-based spatial data types: The ROSE algebra , 1995, The VLDB Journal.

[10]  Christian S. Jensen,et al.  Discovery of convoys in trajectory databases , 2008, Proc. VLDB Endow..

[11]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[12]  Yannis Theodoridis,et al.  Index-based Most Similar Trajectory Search , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[13]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[14]  Lei Chen,et al.  Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.

[15]  Wang-Chien Lee,et al.  Clustering and aggregating clues of trajectories for mining trajectory patterns and routes , 2015, The VLDB Journal.

[16]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[17]  Xing Xie,et al.  Reducing Uncertainty of Low-Sampling-Rate Trajectories , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[18]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[19]  Alex Graves,et al.  Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.

[20]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[21]  Sriram Raghavan,et al.  Inferring Uncertain Trajectories from Partial Observations , 2014, 2014 IEEE International Conference on Data Mining.

[22]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[23]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[24]  Aapo Hyvärinen,et al.  Noise-contrastive estimation: A new estimation principle for unnormalized statistical models , 2010, AISTATS.

[25]  Jürgen Schmidhuber,et al.  Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks , 2006, ICML.

[26]  Sanja Fidler,et al.  Skip-Thought Vectors , 2015, NIPS.

[27]  Christian S. Jensen,et al.  Effective Online Group Discovery in Trajectory Databases , 2013, IEEE Transactions on Knowledge and Data Engineering.

[28]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[29]  Arnold P. Boedihardjo,et al.  Model-driven matching and segmentation of trajectories , 2013, SIGSPATIAL/GIS.

[30]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[31]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[32]  Lei Chen,et al.  On The Marriage of Lp-norms and Edit Distance , 2004, VLDB.

[33]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[34]  Heng Tao Shen,et al.  Discovering popular routes from trajectories , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[35]  P J Webros.  BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .

[36]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[37]  Kai Zheng,et al.  Calibrating trajectory data for similarity-based analysis , 2013, SIGMOD '13.

[38]  Shazia Wasim Sadiq,et al.  An Effectiveness Study on Trajectory Similarity Measures , 2013, ADC.

[39]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[40]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[41]  Sriram Raghavan,et al.  Indexing and matching trajectories under inconsistent sampling rates , 2015, 2015 IEEE 31st International Conference on Data Engineering.