Deep representation of raw traffic data: An embed-and-aggregate framework for high-level traffic analysis

Woosung Choi; Jonghyeon Min; Taemin Lee; Kyeongseok Hyun; Taehyung Lim; Soonyoung Jung

doi:10.1007/978-981-10-7605-3_220

Deep representation of raw traffic data: An embed-and-aggregate framework for high-level traffic analysis

Woosung Choi, Jonghyeon Min, Taemin Lee, Kyeongseok Hyun, Taehyung Lim, Soonyoung Jung

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In Intelligent Transportation Systems (ITS), it is widely used to extract a fixed-size feature vector from raw traffic data for high-level traffic analysis. In several existing works, the statistical approach has been used for extracting feature vectors, which directly extracts features by averaging speed or travel time of each vehicle. However, we can achieve a better representation by taking advantage of state-of-the-art machine learning algorithms instead of the statistical approach. In this paper, we propose a two-phase framework named embed-and-aggregate framework for extracting features from raw traffic data, and a feature extraction algorithm (Traffic2Vec) based on our framework exploiting state-of-the-art machine learning algorithms such as deep learning. We also implement a traffic flow prediction system based on Traffic2Vec as a proof-of-concept. We conducted experiments to evaluate the applicability of the proposed algorithm, and show its superior performance in comparison with the prediction system based on the statistical feature extraction method.

Original language	English
Title of host publication	Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17
Editors	Gangman Yi, Yunsick Sung, James J. Park, Vincenzo Loia
Publisher	Springer Verlag
Pages	1383-1390
Number of pages	8
ISBN (Print)	9789811076046
DOIs	https://doi.org/10.1007/978-981-10-7605-3_220
Publication status	Published - 2018
Event	International Conference on Computer Science and its Applications, CSA 2017 - Taichung, Taiwan, Province of China Duration: 2017 Dec 18 → 2017 Dec 20

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	474
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Other

Other	International Conference on Computer Science and its Applications, CSA 2017
Country/Territory	Taiwan, Province of China
City	Taichung
Period	17/12/18 → 17/12/20

Bibliographical note

Funding Information:
Acknowledgments. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) NRF-2016R1A2B1014013).”

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) NRF-2016R1A2B1014013).”

Keywords

Embedding
Feature extraction
High-level traffic analysis
Traffic data
Trajectory

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-10-7605-3_220

Cite this

Choi, W., Min, J., Lee, T., Hyun, K., Lim, T., & Jung, S. (2018). Deep representation of raw traffic data: An embed-and-aggregate framework for high-level traffic analysis. In G. Yi, Y. Sung, J. J. Park, & V. Loia (Eds.), Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17 (pp. 1383-1390). (Lecture Notes in Electrical Engineering; Vol. 474). Springer Verlag. https://doi.org/10.1007/978-981-10-7605-3_220

Deep representation of raw traffic data: An embed-and-aggregate framework for high-level traffic analysis. / Choi, Woosung; Min, Jonghyeon; Lee, Taemin et al.
Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17. ed. / Gangman Yi; Yunsick Sung; James J. Park; Vincenzo Loia. Springer Verlag, 2018. p. 1383-1390 (Lecture Notes in Electrical Engineering; Vol. 474).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Choi, W, Min, J, Lee, T, Hyun, K, Lim, T & Jung, S 2018, Deep representation of raw traffic data: An embed-and-aggregate framework for high-level traffic analysis. in G Yi, Y Sung, JJ Park & V Loia (eds), Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17. Lecture Notes in Electrical Engineering, vol. 474, Springer Verlag, pp. 1383-1390, International Conference on Computer Science and its Applications, CSA 2017, Taichung, Taiwan, Province of China, 17/12/18. https://doi.org/10.1007/978-981-10-7605-3_220

Choi W, Min J, Lee T, Hyun K, Lim T, Jung S. Deep representation of raw traffic data: An embed-and-aggregate framework for high-level traffic analysis. In Yi G, Sung Y, Park JJ, Loia V, editors, Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17. Springer Verlag. 2018. p. 1383-1390. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-10-7605-3_220

Choi, Woosung ; Min, Jonghyeon ; Lee, Taemin et al. / Deep representation of raw traffic data : An embed-and-aggregate framework for high-level traffic analysis. Advances in Computer Science and Ubiquitous Computing - CSA-CUTE 17. editor / Gangman Yi ; Yunsick Sung ; James J. Park ; Vincenzo Loia. Springer Verlag, 2018. pp. 1383-1390 (Lecture Notes in Electrical Engineering).

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Deep representation of raw traffic data: An embed-and-aggregate framework for high-level traffic analysis

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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