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.
Highlights
Recent studies have shown that connected and autonomous vehicles (CAVs) can ease traffic flow instabilities at low CAV penetration levels [1]–[4]
In this paper, we address the problem of estimating dynamic traffic states from limited probe vehicle data
We proposed a convolutional encoder-decoder neural network model to learn traffic speed dynamics from space-time diagrams
Summary
Recent studies have shown that connected and autonomous vehicles (CAVs) can ease traffic flow instabilities at low CAV penetration levels [1]–[4]. To do so, these systems require accurate knowledge of traffic conditions. Most advanced traffic management and control tools require accurate inputs. Most traffic state estimation techniques in the literature have focused on reproducing traffic conditions at aggregate levels (averaged over segment lengths greater than 100 meters and time intervals greater than one minute [5], [6]). Advanced traffic management tools (e.g., adaptive traffic signal control) require knowledge of traffic conditions at a much finer
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
