In:
Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2676, No. 4 ( 2022-04), p. 194-209
Abstract:
Reasonable and accurate forecasts can be used by the highway maintenance management department to determine the best maintenance timing and strategy, which can keep the highway performing well and maximize its social and economic benefits. A Grey–Markov combination model is established in this paper to predict highway pavement performance accurately based on the Grey GM (1, 1) model (a single-variable Grey prediction model with a first-order difference equation) and revised by the Markov model. The advantages of the short-term forecast Grey model and the probabilistic Markov model, which considers the fate of pavement performance prediction, are comprehensively applied to the combined forecasting model. The Grey GM (1, 1), Grey–Markov model and Liu-Yao model are adopted to predict the pavement condition index (PCI) based on the actual PCI values measured in Shanxi, Chongqing, and Shaoguan. The average relative errors of the above three models’ predicted values in Shanxi are 0.73%, 1.18%, and 0.67%, respectively, from 2012 to 2014. Thus, the prediction errors of the three models are relatively close. The average relative errors of the prediction values predicted by the three models are 3.89%, 0.67%, and 0.50%, respectively, from 2015 to 2019. The latter two errors are more minor than the Grey GM (1, 1) model. Two other regions have similar conclusions. The results show that the prediction accuracy of the combination Grey–Markov prediction model established in this paper is feasible to predict asphalt pavement performance in China.
Type of Medium:
Online Resource
ISSN:
0361-1981
,
2169-4052
DOI:
10.1177/03611981211057527
Language:
English
Publisher:
SAGE Publications
Publication Date:
2022
detail.hit.zdb_id:
2403378-9
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