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Temperature Prediction for Expressway Pavement Icing in Winter Based on XGBoost-LSTNet Variable Weight Combination Model
| 题名： | Temperature Prediction for Expressway Pavement Icing in Winter Based on XGBoost-LSTNet Variable Weight Combination Model |
| 正文语种： | eng |
| 作者： | Ning Zhang;Tianyi Mao;Haotian Chen;Lu Lv;Yangchun Wang;Ying Yan |
| 作者单位： | Shandong Key Laboratory of Highway Technology and Safety Assessment Shandong Hi-Speed Group Co. Ltd. Jinan 250098 China;College of Transportation Engineering Chang'an Univ. Xi'an Shaanxi 710064 China;College of Transportation Engineering Chang'an Univ. Xi'an Shaanxi 710064 China;Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji Univ. Shanghai 201804 China;Shandong Key Laboratory of Highway Technology and Safety Assessment Shandong Hi-Speed Engineering Test Co. Ltd. Jinan 250002 China;College of Transportation Engineering Chang'an Univ. Xi'an Shaanxi 710064 China |
| 关键词： | Transportation safety; Pavement temperature prediction; Expressway; Winter weather; Extreme gradient boosting (XGBoost)-long- and short-term time-series network (LSTNet) variable weight combination model |
| 摘要： | Ice cover on pavement may reduce the road adhesion coefficient and increase the crash risks, which might result in more traffic crashes. The primary factor utilized to assess whether the wet pavement is icy or not is the pavement temperature. Therefore, forecasting pavement temperature is an effective method to judge road conditions and improve traffic safety. This paper proposes a combination model based on the extreme gradient boosting (XGBoost) model and long- and short-term time-series network (LSTNet) model to predict pavement temperature. Pavement temperature and meteorological data were collected for the cities along the Shandong part of the Beijing-Taipei Expressway (G3). In this study, nine meteorological variables were used. Subsequently, after correlation analysis, five variables, including air temperature, dew point temperature, relative humidity, evaporation, and potential evaporation, were selected for prediction. The method proposed in this study comprises the following steps. First, the XGBoost and the LSTNet models are respectively formulated based on the time-varying characteristics of pavement temperatures. Then, using the preset weight of the variable, the XGBoost model is used for preliminary prediction to add features. Finally, the experimental analysis is performed on the Qihe data set after the two models have been integrated using the inverse variance method. As revealed by the experimental results, the mean absolute error (MAE) and root-mean-square error (RMSE) of the proposed XGBoost-LSTNet model are 0.8235 and 1.2412, respectively. Compared with the long short-term memory (LSTM) model, random forest (RF) model, XGBoost model, and LSTNet model, the XGBoost-LSTNet model proposed in this paper has higher accuracy. The study's findings can successfully increase wintertime expressway traffic safety and serve as a guide for managing maintenance and preventing icing-related accidents. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 7 |
| 页码： | 04023062.1-04023062.11 |
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