Expressway Pavement Temperature Forecast Based on LSTM and Prior Knowledge

Xiong Guoyu; Zu Fan; Bao Yunxuan; Wang Kexin

doi:10.11898/1001-7313.20240106

Vol.35, NO.1, 2024

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Xiong Guoyu, Zu Fan, Bao Yunxuan, et al. Expressway pavement temperature forecast based on LSTM and prior knowledge. J Appl Meteor Sci, 2024, 35(1): 68-79. DOI: 10.11898/1001-7313.20240106.

Citation:

Xiong Guoyu, Zu Fan, Bao Yunxuan, et al. Expressway pavement temperature forecast based on LSTM and prior knowledge. J Appl Meteor Sci, 2024, 35(1): 68-79. DOI: 10.11898/1001-7313.20240106.

Xiong Guoyu, Zu Fan, Bao Yunxuan, et al. Expressway pavement temperature forecast based on LSTM and prior knowledge. J Appl Meteor Sci, 2024, 35(1): 68-79. DOI: 10.11898/1001-7313.20240106.

Citation:

Xiong Guoyu, Zu Fan, Bao Yunxuan, et al. Expressway pavement temperature forecast based on LSTM and prior knowledge. J Appl Meteor Sci, 2024, 35(1): 68-79. DOI: 10.11898/1001-7313.20240106.

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Expressway Pavement Temperature Forecast Based on LSTM and Prior Knowledge

DOI: 10.11898/1001-7313.20240106

Xiong Guoyu^{1, 2},
Zu Fan²,
Bao Yunxuan^{1, 2, 3
,
,},
Wang Kexin^{1, 2}

1.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044
2.
Key Laboratory of Transportation Meteorology, CMA/Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210009
3.
Engineering Research Center for Internet of Things Equipment Super-Fusion Application and Security in Jiangsu Province, Wuxi University, Wuxi 214105

Received Date: 2023-10-15
Rev Recd Date: 2023-11-27
Publish Date: 2024-01-31

Abstract

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.
- expressway;
- pavement temperature;
- LSTM;
- prior knowledge;
- multi-step forecasting model

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References(41)

Figures and Tables

Fig. 1 Geographical location distribution of traffic meteorological stations along Nanjing Ring Expressway

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Fig. 2 Evaluation of forecasting performance using different schemes at 4 traffic meteorological stations

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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

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Fig. 4 The same as in Fig. 3, but for 3 h lead time

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Fig. 5 Comparisons between observation and forecast of Scheme 5 for extreme low pavement temperature cases on 21 Feb 2022

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Fig. 6 Comparisons between observation and forecast of Scheme 5 for extreme high pavement temperature cases on 8 Aug 2022

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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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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
路面状况	交通气象站	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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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
个例	交通气象站	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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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
模型站	模型站类型	验证站	验证站类型	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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