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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
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)

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.

Keywords

High-level traffic analysis Traffic data Trajectory Embedding Feature extraction 

Notes

Acknowledgments

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

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Woosung Choi
    • 1
  • Jonghyeon Min
    • 1
  • Taemin Lee
    • 1
  • Kyeongseok Hyun
    • 1
  • Taehyung Lim
    • 1
  • Soonyoung Jung
    • 1
  1. 1.Department of Computer Science and EngineeringKorea UniversitySeoulSouth Korea

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