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Expressway Pavement Temperature Forecast Based on LSTM and Prior Knowledge
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摘要: 为了精准预报高速公路路面温度, 为车辆安全行驶提供气象保障, 采用2019—2022年南京市绕城高速公路上9个交通气象站及ERA5-land再分析数据, 通过构建时间序列特征工程、引入物理机制相关数据两类方法结合先验知识, 运用长短期记忆神经网络模型建立研究区域内4个交通气象站未来3 h逐10 min路面温度多步预报模型并进行验证; 在此基础上, 将已建立的模型应用于其他交通气象站, 探究模型的适用性。结果表明: 结合先验知识后, 模型预报性能明显提高, 准确率在85%以上, 且随着预报时效的延长, 性能提升更为明显, 准确率最高提升36%; 模型能较为准确地预报路面极端低温发生的时间和极值, 且在预报时效较短时对路面极端高温的预报也具有一定参考价值; 利用已建立的模型对其他交通气象站的路面温度进行预报时, 准确率在62%以上, 在预报时效较短时效果较好, 准确率在80%以上, 且交通气象站所处的下垫面背景类型对模型的选择起关键作用。Abstract: The variation of road surface temperature along highways is a crucial indicator for traffic meteorological conditions and constitutes a significant focus in the research on meteorological disasters related to transportation. Accurate forecast of pavement temperature, timely issuance of pavement condition warnings, and alerting relevant personnel to take defensive measures are of paramount importance for ensuring the safety of people's lives and property. Observations from 4 expressway meteorological stations along Nanjing City Ring Expressway and the corresponding ERA5-land reanalysis data from 2019 to 2022 are analyzed. Utilizing feature engineering techniques that consider the daily and seasonal temperature variations as well as temperature trends, a long-short-term memory (LSTM) neural network model, incorporating prior knowledge, is established for multi-step pavement temperature forecasting at 10 min intervals for the next 3 hours. The models are validated under different scenarios including extreme high and low pavement temperature conditions. They are further transferred and applied to 5 additional meteorological stations to investigate the model universality. This approach addresses the challenge of pavement temperature forecasting for stations with limited historical data due to new construction or equipment maintenance. Results indicate that the incorporation of prior knowledge facilitates a more comprehensive consideration of environmental influences by maximizing the feature extraction capabilities of LSTM. All forecasting performance metrics of the model exhibit significant improvements, with the accuracy exceeding 85%. As the forecast lead time extends, the enhancement in various forecast metrics becomes more pronounced, reaching a maximum accuracy improvement of 36%. The model accurately predicts the occurrence time and extremities of extreme low temperatures, but it exhibits relatively weaker capabilities in forecasting extreme high temperatures, with approximately 1 h advance in occurrence time and an underestimation of about 4 ℃. Despite this generally lower forecasting efficacy, the model still provides valuable information. When applying models to forecast pavement temperatures at other meteorological stations, the accuracy exceeds 62%. The forecast performance is better for short lead times, with the accuracy surpassing 80%. The underlying surface type plays a crucial role in the selection of different models. The suburban station model performs relatively optimally for urban meteorological stations and suburban meteorological stations, while the rural station model performs relatively optimally for rural meteorological stations.
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图 1 南京市绕城高速公路交通气象站地理位置分布
Fig. 1 Geographical location distribution of traffic meteorological stations along Nanjing Ring Expressway
图 2 4个交通气象站不同方案预报能力评估
Fig. 2 Evaluation of forecasting performance using different schemes at 4 traffic meteorological stations
图 3 2022年交通气象站M9522 1 h预报时效5种方案模型预报效果及对比
(a)方案1，(b)方案2，(c)方案3，(d)方案4，(e)方案5，(f)各方案预报能力评价指标对比(各指标均进行归一化处理)
Fig. 3 Forecasting performance and comparison of 1 h lead time for M9522 traffic meteorological station in 2022
(a)Scheme 1, (b)Scheme 2, (c)Scheme 3, (d)Scheme 4, (e)Scheme 5, (f)normalized comparison of forecasting performance for each scheme
图 5 2022年2月21日路面极端低温个例路面温度观测值与方案5预报值对比
Fig. 5 Comparisons between observation and forecast of Scheme 5 for extreme low pavement temperature cases on 21 Feb 2022
图 6 2022年8月8日路面极端高温个例路面温度观测值与方案5预报值对比
Fig. 6 Comparisons between observation and forecast of Scheme 5 for extreme high pavement temperature cases on 8 Aug 2022
表 1 模型方案设定
Table 1 Model scheme setting
模型方案 交通气象站观测数据 物理机制相关变量 特征变量 方案1 √ 方案2 √ 方案3 √ √ 方案4 √ √ 方案5 √ √ √ 
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表 2 低温和高温路面状况下方案5准确率和TS评分
Table 2 Accuracy rate and threat score of Scheme 5 under low and high pavement temperature conditions
路面状况 交通气象站 1 h时效 2 h时效 3 h时效 准确率/% TS评分 准确率/% TS评分 准确率/% TS评分 低温 M9518 100 0.64 99.77 0.63 99.77 0.60 M9520 100 0.56 100 0.52 100 0.53 M9522 100 0.53 98.41 0.49 94.42 0.42 M9526 99.40 0.71 100 0.74 99.28 0.69 高温 M9518 27.82 0.41 11.55 0.17 6.45 0.07 M9520 58.10 0.56 25.05 0.28 13.75 0.16 M9522 67.67 0.52 45.65 0.39 22.02 0.21 M9526 51.27 0.62 43.79 0.51 28.74 0.41
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表 3 路面极端低温和极端高温个例中方案5模型评估
Table 3 Evaluation of Scheme 5 in extreme high and extreme low pavement temperature cases
个例 交通气象站 1 h时效 3 h时效 准确率/% 平均绝对偏差/℃ 均方根误差/℃ 准确率/% 平均绝对偏差/℃ 均方根误差/℃ 极端低温 M9518 100 0.80 0.88 97.91 0.96 1.24 M9520 100 0.53 0.71 100 0.94 1.18 M9522 100 0.69 0.84 93.75 1.42 1.75 M9526 97.92 0.77 1.08 92.36 1.11 1.50 极端高温 M9518 69.44 2.27 2.95 53.47 4.19 5.64 M9520 88.89 0.92 1.40 68.06 2.68 3.27 M9522 90.27 1.01 1.50 67.36 2.97 3.72 M9526 79.86 1.63 2.84 61.80 3.88 5.94
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表 4 模型迁移应用预报效果评估
Table 4 Evaluation of model transfer application forecasting performance
模型站 模型站类型 验证站 验证站类型 1 h时效 3 h时效 准确率/% 平均绝对偏差/℃ 均方根误差/℃ 准确率/% 平均绝对偏差/℃ 均方根误差/℃ M9521 城市 86.09 1.47 2.30 73.10 2.40 3.53 M9126 城市 87.17 1.47 2.31 72.12 2.54 3.67 M9518 城郊 M9293 城郊 80.18 1.86 2.92 67.95 2.88 4.23 M9513 乡村 78.87 1.99 2.97 59.44 3.86 5.65 M9516 乡村 80.39 1.93 2.93 59.95 3.80 5.62 M9521 城市 88.20 1.43 2.05 70.22 2.60 3.75 M9126 城市 87.35 1.49 2.18 68.28 2.83 4.06 M9520 城郊 M9293 城郊 84.11 1.65 2.41 66.88 2.92 4.24 M9513 乡村 83.78 1.65 2.71 62.80 3.37 5.01 M9516 乡村 85.32 1.65 2.87 62.82 3.39 5.12 M9521 城市 86.10 1.58 2.22 70.23 2.77 4.08 M9126 城市 85.29 1.72 2.35 66.55 3.06 4.34 M9522 城市 M9293 城郊 80.97 1.90 2.73 65.96 3.24 4.86 M9513 乡村 81.92 1.81 2.82 57.21 4.01 6.03 M9516 乡村 81.35 1.85 2.80 55.69 4.06 6.02 M9521 城市 86.39 1.52 2.17 58.39 3.36 4.61 M9126 城市 85.09 1.63 2.31 57.56 3.62 5.15 M9526 乡村 M9293 城郊 81.69 1.79 2.68 64.73 3.12 4.51 M9513 乡村 84.17 1.70 2.44 63.65 3.20 4.55 M9516 乡村 84.18 1.75 2.48 62.16 3.12 4.38
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