Traffic Data Imputation using Deep Convolutional Neural Networks

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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Cited by in corpus (8)

• Backpressure Control with Estimated Queue Lengths for Urban Network Traffic
• Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation
• Learning Traffic Speed Dynamics from Visualizations
• Generalized adaptive smoothing based neural network architecture for traffic state estimation
• Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning
• Generalized Adaptive Smoothing Using Matrix Completion for Traffic State Estimation
• Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach
• Calibrating Adaptive Smoothing Methods for Freeway Traffic Reconstruction