Investigation of Effects of Inherent Variation and Spatiotemporal Dependency on Urban Travel-Speed Prediction
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 5
Abstract
Urban traffic prediction is a challenging task due to the complexity of urban networks. Many studies have been conducted to improve the prediction accuracy, but the limitation still remains that their accuracy varies with location and time due to lack of understanding. To overcome this limitation, it is necessary to investigate in depth the various phenomena that change the traffic flow patterns. Among the phenomena, this study aims to analyze the effect of inherent variation in a link and spatiotemporal dependency between links in predicting travel speed in urban networks and to identify the factors that influence the two phenomena. The results show that the variation and dependency have significant differences according to locations. The results also indicate that the effects of the two phenomena vary depending on the prediction horizon of the prediction model and suggest to consider both the variation and dependency in short-term prediction but focus on only the variation in long-term prediction. The authors also identify the factors that affect the two phenomena and recommend guidelines for urban traffic prediction.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request (five-min aggregated link-speed data of the DSRC system in Daegu metropolitan area).
Acknowledgments
This work was supported by a grant (18TLRP-C149346-01) funded by the Ministry of Land, Infrastructure and Transport of Korean Government and by the 2019 Research Fund of Myongji University, Republic of Korea.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 5 • May 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Apr 3, 2019
Accepted: Oct 4, 2019
Published online: Feb 22, 2020
Published in print: May 1, 2020
Discussion open until: Jul 22, 2020
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