Spatial–Temporal Graph-Enabled Convolutional Neural Network–Based Approach for Traffic Networkwide Travel Time
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 5
Abstract
It has been recognized that significant travel time estimation errors may be introduced using low-resolution GPS-based floating car trajectory data traditionally. Very few studies have been conducted to concentrate on spatial–temporal relationship identification among travel time measurements. In this study, an attention-based spatial–temporal graph convolutional networks on low-resolution data (AGCN-LR) approach was proposed to estimate more accurate travel time in urban traffic roadway networks using low-resolution GPS-based data measured by floating cars. Specifically, three models were developed in this AGCN-LR approach. Hour, day, and week were used to model the dynamic relationship among spatial–temporal traffic flow attributes, respectively. The same structures were adopted for these three models. Two spatial-temporal block (ST-block) models and one temporal convolutional model were included. Furthermore, one spatial graph convolutional model and one temporal attention mechanism model were embedded in a ST-block. AGCN-LR not only improved the efficiency and accuracy of travel time estimation through the framework optimization training process in a spectrum convolution network but also combined the three temporal components. The final estimation value was formed afterward. Experimental tests were conducted using the real data set from low-resolution floating car data in Harbin, China, in 2017. Results indicated that AGCN-LR outperforms the other state-of-the-art algorithms by reducing estimation mean absolute error (MAE) by about 50 s when it captured the relationship among dynamic spatial and temporal data from the data set. The AGCN-LR approach demonstrated great potential to become one of the important urban network-wide traffic management tools using low-resolution floating car data.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Youth Backup Plan of Technology Creative Person of Harbin (No. 2017RAQXJ093).
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© 2022 American Society of Civil Engineers.
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Received: May 17, 2021
Accepted: Dec 6, 2021
Published online: Feb 25, 2022
Published in print: May 1, 2022
Discussion open until: Jul 25, 2022
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