Towards Predicting Traffic Shockwave Formation and Propagation: A Convolutional Encoder–Decoder Network
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 4
Abstract
Traffic management strategies have been relying on various congestion prediction methodologies. The prediction accuracy of these methodologies has improved over the years, offering reasonable short-term and midterm predictions of macroscopic traffic measures (i.e., flow, speed, and occupancy/density). Unfortunately, by relying on fixed infrastructure sensors and aggregated data, these prediction methodologies fail to include microscopic traffic flow dynamics in their prediction algorithms. Accordingly, they usually fail to capture the onset of congestion and can only predict the propagation of existing shockwaves. That is, in fact, critical for utilizing effective traffic management strategies because predicting the onset of congestion can significantly help with mitigating it. Addressing this shortcoming in traffic predcition algorithms, this study proposes a deep learning methodology to predict the formation and propagation of traffic shockwaves at the vehicle trajectory level. Assuming the existence of communications between vehicles and infrastructure, the time-space diagram of the study segment serves as the input of the deep neural network, and the output of the network is the predicted propagation of shockwaves on that segment. It is the capability to extract the features embedded in a time-space diagram that allows this methodology to predict the propagation of traffic shockwaves. The proposed approach was tested on both simulation and real-world data, and results show that it can accurately predict shockwave formation and propagation.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This material is based upon work supported by the National Science Foundation under Grant Nos. 1826410 and 2047937.
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Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Dec 20, 2021
Accepted: Oct 27, 2022
Published online: Feb 3, 2023
Published in print: Apr 1, 2023
Discussion open until: Jul 3, 2023
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