篇名：An Arrival Time Prediction Method for Bus System
作者：陈志华（Chi-Hua Chen）
来源：IEEE Internet of Things Journal
年份：2018
DOI:10.1109/JIOT.2018.2863555
文章摘要：
This letter proposes random neural networks (RNNs) to randomly train several neural network (NN) models for the promotion of traditional NN. Moreover, an arrival time prediction method (ATPM) based on RNNs is proposed to predict the stop-to-stop travel time for motor carriers. In experiments, the results showed that the average accuracies of RNNs are 94.75% for highway and 78.22% for urban road, respectively. Furthermore, the accuracies of the proposed ATPM are higher than previous data mining methods. Therefore, the proposed ATPM is suitable to predict the stop-to-stop travel time for motor carriers.
本文提出了随机神经网络（RNN）来随机训练几个神经网络（NN）模型，以提升传统的神经网络。此外，提出了一种基于RNNs的到达时间预测方法（ATPM）来预测汽车运输公司的“站到站”行程时间。实验结果表明，RNNs对公路和城市道路的平均精度分别为94.75%和78.22%。此外，所提出的ATPM的准确度高于以前的数据挖掘方法。因此，建议的ATPM适用于预测汽车运输公司的“站到站”行程时间。
