Traffic Data Imputation Using Deep Convolutional Neural Networks

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We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from the Next Generation Simulation (NGSIM) program. Our results show that with probe vehicle penetration levels as low as 5 & x0025;, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model & x2019;s reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation. We also provide a comparison against a widely used adaptive smoothing technique used for the same purpose and demonstrate the superiority of the proposed approach, even with probe vehicle lower penetration levels.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-05
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.8, pp.104740 - 104752

ISSN
2169-3536
DOI
10.1109/ACCESS.2020.2999662
URI
http://hdl.handle.net/10203/275506
Appears in Collection
CE-Journal Papers(저널논문)
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