论文标题
火车延误和冬季气候和大气糖节影响的统计学习
Statistical learning for train delays and influence of winter climate and atmospheric icing
论文作者
论文摘要
这项研究调查了连续冬季在瑞典北部高速客车的到达延迟下的气候效应。采用了新的统计学习方法，包括不均匀的马尔可夫链模型和分层的COX模型，以说明火车延迟的随着时间的变化风险。到达延迟的不均匀马尔可夫链建模使用了多个协变量，包括天气变量，火车操作方向以及通过分层COX模型的主要延迟分析的发现。结果表明，天气变量，例如温度，雪深，冰/积雪和训练方向，会显着影响到达延迟。通过步行前卫验证方法评估了拟合不均匀的马尔可夫链模型的性能。在0.088的水平上获得了预期速率和观察到的到达延迟速率之间的平均平均绝对错误，这意味着大约9％的火车可能被错误地分类为在火车线上的测量点上拟合模型的到达延迟。
This study investigated the climate effect under consecutive winters on the arrival delay of high-speed passenger trains in northern Sweden. Novel statistical learning approaches, including inhomogeneous Markov chain model and stratified Cox model, were adopted to account for the time-varying risks of train delays. The inhomogeneous Markov chain modelling for the arrival delays has used several covariates, including weather variables, train operational direction, and findings from the primary delay analysis through stratified Cox model. The results showed that the weather variables, such as temperature, snow depth, ice/snow precipitation, and train operational direction, significantly impact the arrival delay. The performance of the fitted inhomogeneous Markov chain model was evaluated by the walk-forward validation method. The averaged mean absolute errors between the expected rates and the observed rates of the arrival delay over the train line was obtained at the level of 0.088, which implies that approximately 9% of trains may be misclassified as having arrival delays by the fitted model at a measuring point on the train line.
