Traffic Propagation in Road Network from a Data-Driven Analysis Perspective
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 2
Abstract
With the continuous enrichment of traffic Internet-of-Things data acquisition methods, more and more spatiotemporal data on road networks is collected in real time by various sensors and multimedia devices. The data-driven deep learning approach can make full use of real-time data from a road network to predict future traffic status. By mining the spatiotemporal relationships between road units, the ability to predict network evolutionary behaviors is improved, which provides a new method of traffic management. There are strong semantic relations between road intersections or road sections in terms of traffic evolution. Modeling the network only from a shallow spatial topological perspective ignores the important intrinsic association of the dynamic network. In this paper, we propose a semantic associative neural network (SANN) for traffic evolution analysis by modeling the propagation effects and similarity patterns between road units. Considering the inadequacy of the fixed adjacent matrix, graph convolution is used to encode the semantic features of a road network and embed them in a bidirectional recurrent neural network for sequence prediction. Finally, the experiments are conducted based on speed data sets to prove the effectiveness of the proposed method. The model achieved a well-predicted accuracy of 95.33% and 84.08% on Pems-Bay and Los Angeles data sets.
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Data Availability Statement
All data sets used to support the findings of this study are available from pems.dot.ca.gov/ and github.com/liyaguang/DCRNN. Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
This research is supported by research grants from the Beijing Natural Science Foundation (No. 4202004) and the National Natural Science Foundation of China (No. 62072016).
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 2 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Nov 3, 2021
Accepted: Jul 7, 2022
Published online: Nov 21, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 21, 2023
ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Data analysis
- Data collection
- Engineering fundamentals
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Methodology (by type)
- Network analysis
- Neural networks
- Research methods (by type)
- Traffic analysis
- Traffic engineering
- Traffic management
- Transportation engineering
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