文章基本信息

标题：Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network
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作者：Xin Zhou , Peixin Dong , Jianping Xing ; Peijia Sun
期刊名称：Future Internet
电子版ISSN：1999-5903
出版年度：2019
卷号：11
期号：12
页码：1-7
DOI：10.3390/fi11120247
出版社：MDPI Publishing
摘要：Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.
关键词：bus arrival time prediction; dynamic factors; recurrent neural network; attention mechanism bus arrival time prediction ; dynamic factors ; recurrent neural network ; attention mechanism