Traffic Condition Uncertainty Quantification under Nonnormal Distributions
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 10
Abstract
Uncertainty quantification is important for making reliable decisions in transportation planning and operations. In the field of short-term traffic condition forecasting, uncertainty quantification methods include primarily distribution-based approaches and nondistribution-based approaches. For the former, the generalized autoregressive conditional heteroscedasticity (GARCH) model has been widely applied to model and quantify traffic condition uncertainty in terms of prediction interval under normality assumption. However, this normality assumption has not been systematically investigated yet. Therefore, this paper attempts to investigate this normality assumption and thereby quantify traffic condition uncertainty, using a method with steps of residual calculation and investigation, normality investigation, distribution estimation, uncertainty quantification, and performance measurement. Using real-world traffic flow data, the distributions of the selected samples are shown to be nonnormal using the Kolmogorov-Smirnov test and normal probability plot. Distribution estimation using nonnormal models shows that the location-scale distribution and generalized error distribution (GED) can be used to model traffic condition uncertainty. Uncertainty quantification using GARCH under these nonnormal distributions further show that nonnormal models outperform the normal model, with the GARCH model under location-scale distribution yielding the best performance. Future studies are recommended to promote the investigation into traffic condition uncertainty quantification and application.
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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 upon reasonable request.
Acknowledgments
We would like to thank the Minnesota Department of Transportation for providing the data used in this study. The views presented in this paper are entirely ours, and we hold all the responsibility for the results and analyses presented in this work.
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Received: Dec 17, 2021
Accepted: Jun 9, 2022
Published online: Aug 9, 2022
Published in print: Oct 1, 2022
Discussion open until: Jan 9, 2023
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Cited by
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