In this study, a hybrid method combining extreme learning machine (ELM) and particle swarm optimization (PSO) is proposed to forecast train arrival delays that can be used for later delay management and timetable optimization. First, nine characteristics (e.g., buffer time, the train number, and station code) associated with train arrival delays are chosen and analyzed using extra trees classifier. Next, an ELM with one hidden layer is developed to predict train arrival delays by considering these characteristics mentioned before as input features. Furthermore, the PSO algorithm is chosen to optimize the hyperparameter of the ELM compared to Bayesian optimization and genetic algorithm solving the arduousness problem of manual regulating. Finally, a case is studied to confirm the advantage of the proposed model. Contrasted to four baseline models (k-nearest neighbor, categorical boosting, Lasso, and gradient boosting decision tree) across different metrics, the proposed model is demonstrated to be proficient and achieve the highest prediction accuracy. In addition, through a detailed analysis of the prediction error, it is found that our model possesses good robustness and correctness.

中文翻译：

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使用混合 ELM-PSO 方法预测列车到达延迟

在这项研究中，提出了一种结合极限学习机（ELM）和粒子群优化（PSO）的混合方法来预测列车到站延误，可用于后期的延误管理和时间表优化。首先，使用额外的树分类器选择和分析与列车到达延迟相关的九个特征（例如，缓冲时间、列车编号和车站代码）。接下来，开发了一个带有一个隐藏层的 ELM，通过将前面提到的这些特征作为输入特征来预测列车到达延迟。此外，与贝叶斯优化和遗传算法相比，选择 PSO 算法来优化 ELM 的超参数，以解决手动调节的艰巨问题。最后，通过一个案例来验证所提出模型的优势。与跨越不同指标的四个基线模型（k-最近邻、分类提升、套索和梯度提升决策树）相比，所提出的模型被证明是精通的，并实现了最高的预测精度。此外，通过对预测误差的详细分析，发现我们的模型具有良好的鲁棒性和正确性。