Citation

Li M, Fang L, Jia W, Guo J. J. Transp. Eng. A: Systems 2022; 148(10): e04022086.

Copyright

(Copyright © 2022, American Society of Civil Engineers)

DOI

10.1061/JTEPBS.0000744

PMID

unavailable

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

Keywords

Distribution estimation; Generalized autoregressive conditional heteroscedasticity (GARCH); Intelligent transportation system; Normality test; Short-term traffic forecasting; Traffic condition uncertainty quantification