Data-driven Adaptive Adjustment Strategy for Strong Wind Alarm in High-speed Railway
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摘要: 高速铁路在中国发展迅速，带来了全新的交通变革.较快的运行速度在带来效率提升的同时也增加了沿线强风对其运行安全的威胁.为了安全运行，铁路沿线部署了大量风速监测传感器，一旦监测到强风，将通过调度中心发出信号，调度沿线列车减速慢行甚至停车.在报警过程中，如何确定报警保持时间极具挑战.如果保持过短，则可能发生重复报警，增加处置次数，加重工作人员负担；若取消过晚，则影响轨道通过能力，带来不必要的效率损失.为此，本文提出一种高速铁路强风报警解除时间调整策略，用于改善这一问题.该策略通过轨道沿线部署的风速计装置，结合时空信息对短时未来强风情况进行预测，基于预测情况，自适应调整报警解除时间.该策略能够有效减少报警冗余时长，提高列车运行效率.Abstract: The rapid development of high-speed railways in China has changed the way people travel. The faster speed induces a growing threat of strong wind on safety. A large number of anemometers have been deployed alongside the railway for monitoring the strong wind. Dispatchers in the dispatch centers issue the scheduling instructions to the train drivers according to the measured wind speed. It is not trivial for the dispatchers to decide when to stop an alarm. If the alarm lasts too short, repeated alarms may occur, increasing the number of treatments and the burden on the staff. If it is stopped too late, track passing capacity will be affected and unnecessary efficiency loss may be caused. In this paper, an adjustment strategy for the stop time of high-speed railway alarm based on wind speed prediction is proposed to solve this emerged challenge. The strategy can effectively reduce the alarm redundancy time and improve operational efficiency.
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Key words:
- High-speed railway /
- wind speed prediction /
- strong wind prediction /
- alarm duration adjustment
1) 本文责任编委 董海荣 -
表 1 高速铁路不同风速下行驶速度规定
Table 1 Speed constraints for the high-speed train at different wind speeds
风速(m/s) 列车运行规定(km/h) 15~20 限速300 20~25 限速200 25~30 限速120 > 30 禁止通行 
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表 2 实验数据集
Table 2 Dataset for experiments
测量点 数量 均值(m/s) 最大值(m/s) 最小值(m/s) 测量点1 $1\, 209\, 600$ $3.64$ $20.0$ $-0.7$ 测量点2 $1\, 209\, 600$ $3.63$ $24.9$ $-0.4$ 测量点3 $1\, 209\, 600$ $3.63$ $29.9$ $-1.0$ 测量点4 $1\, 209\, 600$ $3.63$ $29.5$ $-1.2$ 测量点5 $1\, 209\, 600$ $3.62$ $22.7$ $-0.3$
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表 3 风速预测准确度
Table 3 Performances of the wind prediction
模型 MAE (m/s) RMSE (m/s) MAPE (%) ARIMA 1-step 2.02 3.46 1.35 5-step 2.14 3.50 1.36 10-step 2.24 3.57 1.37 LSTM(128) 1-step 1.21 1.60 0.65 5-step 1.39 1.87 0.69 10-step 1.51 2.25 0.75 STA-RNN 1-step 0.98 1.25 0.20 5-step 1.11 1.40 0.22 10-step 1.21 1.80 0.25
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表 4 强风预测效果
Table 4 Performances of the strong wind prediction
模型 精确度 召回率 $\rm F_{\rm score}$ STA-RNN 1.0 0.65 0.79 STA-RNN+SVM 1.0 0.73 0.84
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