Abstract
Convolutional neural networks (CNN) have become due to their outstanding performance in the past few years rapidly the standard approach when it comes to processing 2D data as these can be found in the image recognition and classification domain. Recent research shows that CNN models can handle 1D data, such as temporal sequences (e.g., speech and text), with a similar high performance as well. This fact motivated our present idea to apply convolutional networks for modeling human semantic trajectories and predicting future locations. Our work consists of three parts. The first part evaluates the performance of a standard spatial CNN in comparison with a vanilla feed-forward, a recurrent and a long short-term memory network (LSTM) at two different semantic representation levels. In the second part, we explore in depth the impact of the kernel size and propose a multi-channel convolutional approach based on kernels of varied size. Finally, part three investigates the depthwise factorization of the convolutional layer with regard to training time and test accuracy. Altogether, it can be shown that convolutional networks are able to outperform the competition, with the channel number as well as the kernel size being the most significant hyperparameters.
Similar content being viewed by others
References
Alvares LO, Bogorny V, Kuijpers B, de Macedo JAF, Moelans B, Vaisman A (2007) A model for enriching trajectories with semantic geographical information. In: Proceedings of the 15th annual ACM international symposium on advances in geographic information systems. ACM, New York, p 22
Arriaga O, Valdenegro-Toro M, Plöger P (2017) Real-time convolutional neural networks for emotion and gender classification. CoRR arXiv:1710.07557
Ashbrook D, Starner T (2003) Using GPS to learn significant locations and predict movement across multiple users. Pers Ubiquitous Comput 7(5):275–286. https://doi.org/10.1007/s00779-003-0240-0
Bogorny V, Renso C, Aquino AR, Lucca Siqueira F, Alvares LO (2014) Constant—a conceptual data model for semantic trajectories of moving objects. Trans GIS 18(1):66–88
Cao X, Cong G, Jensen CS (2010) Mining significant semantic locations from GPS data. Proc VLDB Endow 3(1–2):1009–1020. https://doi.org/10.14778/1920841.1920968
Cart (2018) Site planning and revenue prediction: optimizing food truck locations in New York City (online). https://carto.com/blog/optimizing-food-truck-locations/. Accessed 29 July 2019
Chollet F (2017) Xception: deep learning with depthwise separable convolutions. Preprint arXiv:1610.02357
Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12(Aug):2493–2537
dos Santos C, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 69–78
Eagle N, Pentland AS (2006) Reality mining: sensing complex social systems. Pers Ubiquit Comput 10(4):255–268
Elragal A, El-Gendy N (2013) Trajectory data mining: integrating semantics. J Enterp Inf Manag 26(5):516–535. https://doi.org/10.1108/JEIM-07-2013-0038
Etter V, Kafsi M, Kazemi E (2012) Been there, done that: what your mobility traces reveal about your behavior. In: Proceedings of mobile data challenge by nokia workshop. 10th PerCom
Facebook (2018) Offline trajectories. United States Patent Application 20,180,352,383
Fan RC, Yang X, Fay JD (2003) Using location data to determine traffic information. US Patent 6,594,576
Gao Q, Zhou F, Zhang K, Trajcevski G, Luo X, Zhang F (2017) Identifying human mobility via trajectory embeddings. In: Proceedings of the 26th international joint conference on artificial intelligence. AAAI Press, New York, pp 1689–1695
Google (2016) Do i stay or do i go now? Google maps has the answer in one tap (online). https://www.blog.google/products/maps/do-i-stay-or-do-i-go-now-google-maps/. Accessed 29 July 2019
Graves A, Mohamed A, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, New York, pp 6645–6649
Gutfreund H, Mezard M (1988) Processing of temporal sequences in neural networks. Phys Rev Lett 61(2):235
Kaiser L, Gomez AN, Chollet F (2017) Depthwise separable convolutions for neural machine translation. Preprint arXiv:1706.03059
Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. Preprint arXiv:1404.2188
Karatzoglou A (2019) Evolutionary optimization on artificial neural networks for predicting the user’s future semantic location. In: Macintyre J, Iliadis L, Maglogiannis I, Jayne C (eds) Engineering applications of neural networks. Springer, Cham, pp 379–390
Karatzoglou A, Jablonski A, Beigl M (2018) A Seq2Seq learning approach for modeling semantic trajectories and predicting the next location. In: Proceedings of the 26th ACM SIGSPATIAL international conference on advances in geographic information systems, SIGSPATIAL’18. ACM, New York, pp 528–531. https://doi.org/10.1145/3274895.3274983
Karatzoglou A, Köhler D, Beigl M (2018) Semantic-enhanced multi-dimensional Markov chains on semantic trajectories for predicting future locations. Sensors 18(10):3582. https://doi.org/10.3390/s18103582
Karatzoglou A, Lamp SC, Beigl M (2017) Matrix factorization on semantic trajectories for predicting future semantic locations. In: 2017 IEEE 13th international conference on wireless and mobile computing, networking and communications (WiMob), pp 1–7. https://doi.org/10.1109/WiMOB.2017.8115810
Karatzoglou A, Schnell N, Beigl M (2018) A convolutional neural network approach for modeling semantic trajectories and predicting future locations. In: Kůrková V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I (eds) Artificial neural networks and machine learning—ICANN 2018. Springer, Cham, pp 61–72
Karatzoglou A, Sentürk H, Jablonski A, Beigl M (2017) Applying artificial neural networks on two-layer semantic trajectories for predicting the next semantic location. In: International conference on artificial neural networks. Springer, New York, pp 233–241
Kim Y (2014) Convolutional neural networks for sentence classification. Preprint arXiv:1408.5882
LeCun Y, Bengio Y et al (1995) Convolutional networks for images, speech, and time series. Handb Brain Theory Neural Netw 3361(10):1995
Lv J, Li Q, Wang X (2016) T-conv: a convolutional neural network for multi-scale taxi trajectory prediction. Preprint arXiv:1611.07635
Mathworks (2018) Convolutional neural network (online). https://www.mathworks.com/discovery/convolutional-neural-network.html. Accessed 19 Feb 2018
Newsroom TE (2017) A new business intelligence emerges: Geo.ai (online). https://www.esri.com/about/newsroom/publications/wherenext/new-business-intelligence-emerges-geo-ai/. Accessed 29 July 2019
Quercia D, Lathia N, Calabrese F, Di Lorenzo G, Crowcroft J (2010) Recommending social events from mobile phone location data. In: 2010 IEEE international conference on data mining, pp 971–976. https://doi.org/10.1109/ICDM.2010.152
Ratti C, Frenchman D, Pulselli RM, Williams S (2006) Mobile landscapes: using location data from cell phones for urban analysis. Environ Plan 33(5):727–748. https://doi.org/10.1068/b32047
Sifre L, Mallat S (2013) Rotation, scaling and deformation invariant scattering for texture discrimination. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1233–1240
Skyhook (2019) How IoT can benefit from location (online). https://www.skyhook.com/applications/iot. Accessed 29 July 2019
Song X, Kanasugi H, Shibasaki R (2016) Deeptransport: prediction and simulation of human mobility and transportation mode at a citywide level. In: Proceedings of 25th international joint conference on artificial intelligence, pp 2618–2624
Spaccapietra S, Parent C, Damiani ML, de Macêdo JAF, Porto F, Vangenot C (2008) A conceptual view on trajectories. Data Knowl Eng 65(1):126–146. https://doi.org/10.1016/j.datak.2007.10.008
Uberbacher EC, Mural RJ (1991) Locating protein-coding regions in human dna sequences by a multiple sensor-neural network approach. Proc Natl Acad Sci 88(24):11261–11265
Ying JJC, Lee WC, Tseng VS (2014) Mining geographic–temporal–semantic patterns in trajectories for location prediction. ACM Trans Intell Syst Technol 5(1):2:1–2:33. https://doi.org/10.1145/2542182.2542184
Ying JJC, Lee WC, Weng TC, Tseng VS (2011) Semantic trajectory mining for location prediction. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, New York, pp 34–43
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Karatzoglou, A., Schnell, N. & Beigl, M. Applying depthwise separable and multi-channel convolutional neural networks of varied kernel size on semantic trajectories. Neural Comput & Applic 32, 6685–6698 (2020). https://doi.org/10.1007/s00521-019-04603-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-019-04603-0