Traffic data imputation using deep convolutional neural networks

Ouafa Benkrouda, Bilal Thonnam Thodi, Hwasoo Yeo, Monica Menendez, Saif Eddin Jabari

Research output: Contribution to journalArticlepeer-review


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 spatiooral 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%, 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'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.

Original languageEnglish (US)
Article number9107074
Pages (from-to)104740-104752
Number of pages13
JournalIEEE Access
StatePublished - Jan 1 2020


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