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A Data-Driven Two-Stage Prediction Model for Train Primary-Delay Recovery Time
International Journal of Software Engineering and Knowledge Engineering ( IF 0.9 ) Pub Date : 2020-08-27 , DOI: 10.1142/s0218194020400124
Bowen Gao 1 , Dongxiu Ou 2 , Decun Dong 1 , Yusen Wu 1
Affiliation  

Accurate prediction of train delay recovery is critical for railway incident management and providing passengers with accurate journey time. In this paper, a two-stage prediction model is proposed to predict the recovery time of train primary-delay based on the real records from High-Speed Railway (HSR). In Stage 1, two models are built to study the influence of feature space and model framework on the prediction accuracy of buffer time in each section or station. It is found that explicitly inputting the attribute features of stations and sections to the model, instead of implicit simulation, will improve the prediction accuracy effectively. For validation purpose, the proposed model has been compared with several alternative models, namely, Logistic Regression (LR), Artificial Neutral Network (ANN), Support Vector Machine (SVM) and Gradient Boosting Tree (GBT). The results show that its remarkable performance is better than other schemes. Specifically, when the error is extended to 3[Formula: see text]min, the proposed model can achieve up to the accuracy of 94.63%. It proves that our method has high value in practical engineering application. Considering the delay propagation of trains is a complex process, our future study will focus on building delay propagation knowledge base and dispatcher experience knowledge base.

中文翻译:

列车初级延迟恢复时间的数据驱动两阶段预测模型

列车延误恢复的准确预测对于铁路事故管理和为乘客提供准确的旅程时间至关重要。在本文中,提出了一种两阶段预测模型,用于基于高铁(HSR)的真实记录来预测列车一次延误的恢复时间。在第一阶段,建立两个模型,研究特征空间和模型框架对每个路段或站点缓冲时间预测精度的影响。研究发现,将台站和路段的属性特征显式输入模型,而不是隐式模拟,将有效提高预测精度。出于验证目的,将所提出的模型与几种替代模型进行了比较,即逻辑回归 (LR)、人工中性网络 (ANN)、支持向量机 (SVM) 和梯度提升树 (GBT)。结果表明,其显着的性能优于其他方案。具体来说,当误差扩大到3[公式:见正文]min时,所提出的模型可以达到高达94.63%的准确率。证明了我们的方法在实际工程应用中具有很高的价值。考虑到列车延误传播是一个复杂的过程,我们未来的研究将侧重于建立延误传播知识库和调度员经验知识库。
更新日期:2020-08-27
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