论文标题
近期铁路延迟预测的新型马尔可夫模型
A Novel Markov Model for Near-Term Railway Delay Prediction
论文作者
论文摘要
对于铁路运营和乘客的旅行经验，预测近乎未来的延迟是临近延迟的重要性。这项工作旨在设计基于荷兰铁路数据的火车延迟的预测模型。我们首先开发了卡方检验，以表明站点的延迟演变遵循一阶马尔可夫链。然后，我们提出了一个基于非均匀马尔可夫链的延迟预测模型。为了处理马尔可夫链过渡矩阵的稀疏性，我们提出了一种依赖于高斯核密度估计的新型矩阵恢复方法。我们的数值测试表明，这种恢复方法的预测准确性优于其他启发式方法。我们提出的马尔可夫链模型还表明，在解释性和预测准确性方面都比其他广泛使用的时间序列模型更好。此外，我们提出的模型不需要复杂的培训过程，该过程能够处理大规模的预测问题。
Predicting the near-future delay with accuracy for trains is momentous for railway operations and passengers' traveling experience. This work aims to design prediction models for train delays based on Netherlands Railway data. We first develop a chi-square test to show that the delay evolution over stations follows a first-order Markov chain. We then propose a delay prediction model based on non-homogeneous Markov chains. To deal with the sparsity of the transition matrices of the Markov chains, we propose a novel matrix recovery approach that relies on Gaussian kernel density estimation. Our numerical tests show that this recovery approach outperforms other heuristic approaches in prediction accuracy. The Markov chain model we propose also shows to be better than other widely-used time series models with respect to both interpretability and prediction accuracy. Moreover, our proposed model does not require a complicated training process, which is capable of handling large-scale forecasting problems.
