Delay prediction with spatial–temporal bi-directional LSTM in railway network
Ke Yu, Chuiyun Kong, Limin Zhong, Junfeng Fu, Jie Shao
Abstract
Train delay prediction is a vital part of railway system, but due to uncertain factors such as the complexity of the railway system and spatial–temporal features, it is often difficult to predict train delay in practice. In this paper, we propose a Spatial–Temporal and Bi-directional Long Short-Term Memory (ST-BiLSTM) model to deal with the train delay prediction problem. The model contains spatial–temporal blocks to capture spatial and temporal features and a bi-directional Long Short-Term Memory (LSTM) block to introduce bi-directional information through an attention mechanism. Experiments demonstrate that ST-BiLSTM outperforms the existing baselines in two evaluation metrics.
Topics & Concepts
Computer scienceLong short term memoryBlock (permutation group theory)Term (time)Artificial intelligenceSpatial analysisArtificial neural networkRecurrent neural networkRemote sensingGeographyMathematicsPhysicsGeometryQuantum mechanicsTraffic Prediction and Management TechniquesRailway Systems and Energy EfficiencyRailway Engineering and Dynamics