Traffic Data Imputation using Deep Convolutional Neural Networks

• URL: http://arxiv.org/abs/2002.04406v1
• Date: Tue, 21 Jan 2020 12:52:58 GMT
• Title: Traffic Data Imputation using Deep Convolutional Neural Networks
• Authors: Ouafa Benkraouda, Bilal Thonnam Thodi, Hwasoo Yeo, Monica Menendez, and Saif Eddin Jabari
• Abstract summary: We show that a well trained neural network can learn traffic speed dynamics from time-space diagrams. Our results show that with vehicle penetration probe levels as low as 5%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds.
• Score: 2.7647400328727256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 NGSIM. 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.

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