计算机科学
人工神经网络
Python(编程语言)
随机森林
变量(数学)
预测建模
短时记忆
机器学习
人工智能
循环神经网络
数学
操作系统
数学分析
作者
Chao Wen,Weiwei Mou,Ping Huang,Zhongcan Li
摘要
Abstract Delay prediction is an important issue associated with train timetabling and dispatching. Based on real‐world operation records, accurate forecasting of delays is of immense significance in train operation and decisions of dispatchers. In this study, we established a model that illustrates the interaction between train delays and their affecting factors via train describer records on a Dutch railway line. Based on the main factors that affect train delay and the time series trend, we determined the independent and dependent variables. A long short‐term memory (LSTM) prediction model in which the actual delay time corresponded to the dependent variable was established via Python. Finally, the prediction accuracy of the random forest model and artificial neural network model was compared. The results indicated that the LSTM model outperformed the other two models.
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