Probability prediction of pavement surface low temperature in winter based on bayesian structural time series and neural network

Li, Yueyan; Chen, Jiaqi; Dan, Han-Cheng; Wang, Hao

doi:10.1016/j.coldregions.2021.103434

Cited by 14 publications

(5 citation statements)

References 31 publications

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“…The probabilistic interval forecasting method is mainly based on Bayesian theory. The distribution and expectation of the forecast values can be derived by constructing a distribution model of the forecast quantities, while obtaining interval forecasting results at any confidence level (Li et al, 2022a , 2022b ). Gaussian Process Regression (GPR), a machine learning algorithm based on Bayesian theory, can predict the expected value of a location quantity and its distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In contrast, AI models represented by artificial neural networks (ANN) do not need to satisfy statistical assumptions and can effectively learn the nonlinear relationships in data (Abedin et al, 2021 ). Nowadays, AI models are widely used in the field of time series forecasting, such as credit risk prediction forecasting (Abedin et al, 2018 ; Chi et al, 2019 ), energy supply forecasting (Sun et al, 2022 ), and wind speed forecasting (Li et al, 2022a , 2022b ). Due to carbon prices’ nonlinear and nonstationary characteristics, AI models have better prediction performance and adaptability than statistical methods.…”

Section: Introductionmentioning

confidence: 99%

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An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction

Wang

Zhang

et al. 2022

Ann Oper Res

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction

Wang

Zhang

et al. 2022

Ann Oper Res

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“…Li et al proposed that the prediction of pavement surface temperature should not be a single value, but a probability distribution. They developed a prediction model for evaluating the probability distribution of pavement surface temperature in winter [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Attention-Based BiLSTM Model for Pavement Temperature Prediction of Asphalt Pavement in Winter

Bai

Yang

Zhang³

et al. 2022

Atmosphere

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Pavement temperature is the main factor determining road icing, and accurate and timely pavement temperature prediction is of significant importance to regional traffic safety management and preventive maintenance. The prediction of pavement temperature at the micro-scale has been a challenge to be tackled. To solve this problem, a bidirectional extended short-term memory network model based on the attention mechanism (Att-BiLSTM) was proposed to improve the prediction performance by using the time series features of pavement temperature and meteorological factors. Pavement temperature data and climatic data were collected from a road weather station in Yunnan, China. The results show that the MAE, MSE, and MAPE of the proposed Att-BiLSTM model were 0.330, 0.339, and 10.1%, respectively, which were better than the other baseline models. It was shown that 93.4% of the predicted values had an error less than 1 °C, and 82.1% had an error less than 0.5 °C, indicating that the proposed Att-BiLSTM model enables significant performance improvement. In addition, this paper quantified and analyzed the effects of parameters such as the size of the sliding window, the number of hidden layer neurons, and the optimizer on the performance of the prediction model.

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“…It is important to use estimation methods to determine the condition of the pavement according to future meteorological conditions and to take the necessary precautions. As some of the estimation methods used, the statistical approach method in estimating the road surface condition (Krsmanc et al, 2013;Bouilloud et al, 2009) Machine Learning method (Liu et al, 2018;Yang et al, 2020;Molavi Nojumi et al,2022), artificial neural network method (Xu et al, 2017;Li et al, 2022), Deep Learning method (Milad et al, 2021) have been used to predict the condition of the pavement.…”

Section: Introductionmentioning

confidence: 99%

Derin Öğrenme Yöntemi Kullanılarak Meteorolojik Parametrelere Dayalı Karayolu Kaplama Yüzey Durumunun Tahmini

Baykal

ERGEZER

Terzi

2022

Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi

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The condition of the pavement surface on highways is an important factor in ensuring traffic safety. The condition of the road pavements varies according to the climatic conditions of the road. To record the variability of road pavements according to meteorological factors, both sensors placed in the pavement and road meteorology information stations are installed on the roadsides. Within the scope of intelligent transportation systems, the establishment of road management information systems and the status of the road pavement in real-time can be observed with the data obtained from the sensors. With these sensor data, the road surface condition can be estimated with different artificial intelligence methods. Thus, important information is provided for decision-makers in taking precautions according to the dry, wet, and icy road surface condition. In this study, it is purposed to estimate the road surface condition based on meteorological parameters. For this purpose, deep learning models have been developed. Air temperature (tmp), dew point temperature (dwp), wind speed (sknt), wind direction (drct), wind gust (gust), pavement sensor temperature (tfs), and pavement sensor condition (cond) parameters were used in 65966 datasets. Accuracy was used in the evaluation of deep learning models. Consequently, the evaluation, the accuracy value of the best model was determined as 0.88. In addition, accuracy, recall, precision, and F1-score values of each class were calculated for the test set of the best model.

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Probability prediction of pavement surface low temperature in winter based on bayesian structural time series and neural network

Cited by 14 publications

References 31 publications

An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction

An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction

Attention-Based BiLSTM Model for Pavement Temperature Prediction of Asphalt Pavement in Winter

Derin Öğrenme Yöntemi Kullanılarak Meteorolojik Parametrelere Dayalı Karayolu Kaplama Yüzey Durumunun Tahmini

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