This paper provides a data-driven model of the vibration response of a railway crossing during vehicle passages. Many of the features of trains passing through instrumented crossing are extracted from measured data. Based on the feature selection process, speed, dynamic axle load and the number of wagons are found proper inputs in the prediction model. Train-crossing interaction response at a crossing due to passing trains is modeled from a data-driven Neuro-Fuzzy soft computing approach. Locally Linear Model Tree (LOLIMOT) is applied to predict the crossing nose acceleration. The model comparison against measurements shows that the ability to predict the extrapolation cases at off-range speeds has satisfactory compatibility. The monitored passing trains are ranked based on the LOLIMOT input space dimension cuts and extrapolation of the model up to higher train speeds. The influence of train factors (i.e. speed, dynamic axle load, number of wagons) on crossing response is demonstrated. Also, based on the analysis results, it is concluded that with a steady increase in train speeds, some trains show a greater amplification in vibration response than others. The results can be applied in data processing in the crossing vibration monitoring and detection of trains with crossing impact sensitive to speed increasing that can lead to proper operation policies to reduce damages and maintenance costs.

本文提供了一个数据驱动的铁路道口在车辆通过期间的振动响应模型。从测量数据中提取出经过仪表交叉路口的火车的许多特征。根据特征选择过程，可以在预测模型中找到速度，动轴负载和货车数量。通过数据驱动的Neuro-Fuzzy软计算方法来模拟由于通过列车而产生的交叉口处的列车交叉交互响应。局部线性模型树（LOLIMOT）用于预测交叉鼻子加速度。模型与测量结果的比较表明，在超范围速度下预测外推情况的能力具有令人满意的兼容性。根据LOLIMOT输入空间的尺寸削减和模型的外推，对监视的过往列车进行排名，直至达到更高的列车速度。证明了火车因素（即速度，动态轴荷，货车数量）对交叉响应的影响。同样，基于分析结果，可以得出结论，随着列车速度的稳定提高，某些列车的振动响应会比其他列车更大。该结果可应用于交叉振动监测和检测对速度敏感的交叉冲击的列车的数据处理中，从而可以制定适当的运行策略以减少损坏和维护成本。结论是，随着列车速度的稳步提高，某些列车的振动响应比其他列车更大。该结果可应用于交叉振动监测和检测对速度敏感的交叉冲击的列车的数据处理中，从而可以制定适当的运行策略以减少损坏和维护成本。结论是，随着列车速度的稳步提高，某些列车的振动响应比其他列车更大。该结果可应用于交叉振动监测和检测对速度敏感的交叉冲击的列车的数据处理中，从而可以制定适当的运行策略以减少损坏和维护成本。