@article{ref1,
title="Modeling train operation as sequences: a study of delay prediction with operation and weather data",
journal="Transportation research part E: logistics and transportation review",
year="2020",
author="Huang, Ping and Wen, Chao and Fu, Liping and Lessan, Javad and Jiang, Chaozhe and Peng, Qiyuan and Xu, Xinyue",
volume="141",
number="",
pages="e102022-e102022",
abstract="This paper presents a carefully designed train delay prediction model, called FCLL-Net, which combines a fully-connected neural network (FCNN) and two long short-term memory (LSTM) components, to capture operational interactions. The performance of FCLL-Net is tested using data from two high speed railway lines in China. The results show that FCLL-Net has significantly improved prediction performance, over 9.4% on both lines, in terms of the selected absolute and relative metrics compared to the commonly used state-of-the-art models. Additionally, the sensitivity analysis demonstrates that interactions of train operations and weather-related features are of great significance to consider in delay prediction models.<p /> <p>Language: en</p>",
language="en",
issn="1366-5545",
doi="10.1016/j.tre.2020.102022",
url="http://dx.doi.org/10.1016/j.tre.2020.102022"
}