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Bus Arrival Time Prediction with LSTM Neural Network

机译:LSTM神经网络的公交车到站时间预测

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Arrival time is a key aspect of passenger information systems. Provision of accurate bus arrival information is essential for delivering an attractive service and necessary to passengers for reducing their waiting time and bus stops and choosing alternative routes. Recently, the same information is used in smart-phone trip planners. In this paper, we explore an LSTM neural network model for bus arrival time prediction. We take into account heterogeneous information about the transport situation, directly or indirectly affecting the prediction travel time. We evaluate the proposed models with bus operation data from Samara, Russia. Evaluation results show that the proposed model outperforms some typical prediction algorithms.
机译:到达时间是乘客信息系统的关键方面。提供准确的公交车到站信息对于提供有吸引力的服务至关重要,对于减少乘客的等候时间和公交站台以及选择其他路线而言,这也是必不可少的。最近,智能手机旅行计划者使用了相同的信息。在本文中,我们探索了用于公交车到达时间预测的LSTM神经网络模型。我们考虑了有关运输情况的异类信息,这些信息直接或间接地影响预测旅行时间。我们使用来自俄罗斯萨马拉的公交车运行数据评估提出的模型。评估结果表明,该模型优于某些典型的预测算法。

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