Fitting and Characteristics Analysis of Travel-Time Fluctuations on an Urban Road Network
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 10
Abstract
Detailed knowledge about the times at which links in a network suffer travel time instability is of great significance for route decision making and traffic management. With a data set of 1,170 links in a large-scale urban road network, this paper introduces advanced algorithms to fit the travel time volatility (TTV) and analyze its characteristics. TTV is prone to clustering, and its distribution exhibits leptokurtosis and a heavy tail. These characteristics suggest iterative cumulative sums of squares (ICSS) and the autoregressive conditional heteroskedasticity family models (ARCHs) are applicable to analyze TTV. ICSS finds the structural change points to locate the time and determine the intensity of the travel time variances fluctuate. ARCHs are employed to fit the TTV series, and the best model is determined by comparison. ICSS and ARCHs are combined to improve the fitting model. With TTV, TTR is analyzed from a dynamic and temporal perspective at intervalwise and linkwise levels.
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Data Availability Statement
All data 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. All models and code that support the findings of this study are available from the corresponding author upon reasonable request, including ICSS and ARCHs.
Acknowledgments
This research was financially supported by the National Natural Science Foundation of China (61773288). GAIA Open Dataset Initiative (https://gaia.didichuxing.com) offered all data for the experiments. The authors want to thank anonymous reviewers for their insightful comments on this paper.
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Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 10 • October 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jan 30, 2021
Accepted: May 21, 2021
Published online: Jul 23, 2021
Published in print: Oct 1, 2021
Discussion open until: Dec 23, 2021
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