《计算机应用研究》|Application Research of Computers

基于recurrent neural networks的网约车供需预测方法

Prediction method of supply and demand for online car based on recurrent neural networks

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作者 安磊,赵书良,武永亮,陈润资,李佳星
机构 河北师范大学 a.数学与信息科学学院;b.河北省计算数学与应用重点实验室,石家庄 050024
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文章编号 1001-3695(2019)03-023-0756-06
DOI 10.19734/j.issn.1001-3695.2017.10.0979
摘要 以网约车订单等真实数据为数据源,结合TensorFlow深度学习框架,利用循环神经网络(recurrent neural networks)方法,预测网约车在未来某时间某地点的订单需求量。提出改进LSTM RNN(长短时记忆循环神经网络)模型,经过对其优化和训练,能够有效预测网约车未来某时间某地点的供需量。对数据源进行可视化分析,排除不相关数据源干扰,以此为基础设计仿真实验。仿真实验表明,该模型的正确率比反向传播神经网络(BPNN)、回归决策树(DTR)、非线性回归支持向量机(SVR)以及随机漫步(RW)等模型高,同时,对长短间隔不同的历史数据有较好的记忆能力,在测试数据上有较强的泛化能力。
关键词 长短时记忆循环神经网络;网约车数据;交通优化调度;TensorFlow;深度学习
基金项目 国家自然科学基金资助项目(71271067)
国家社科基金重大项目(13&ZD091)
河北省高等学校科学技术研究项目(QN2014196)
河北师范大学硕士基金资助项目(CXZZSS2017048)
本文URL http://www.arocmag.com/article/01-2019-03-023.html
英文标题 Prediction method of supply and demand for online car based on recurrent neural networks
作者英文名 An Lei, Zhao Shuliang, Wu Yongliang, Chen Runzi, Li Jiaxing
机构英文名 a.CollegeofMathematic&InformationScience,b.HebeiKeyLaboratoryofComputationalMathematics&Applications,HebeiNormalUniversity,Shijiazhuang050024,China
英文摘要 Ordered from online car as data sources, this paper used TensorFlow and recurrent neural networks to predict the supply and demand for online car at a certain point in the future. This paper presented the model of LSTM RNN, which was optimized and trained to effectively predict the supply and demand of the online car at a certain point in the future. Visual analysis of data source, help excluding uncorrelated data source, which was the basic to design simulation experiment. Simulation experiments show that the accuracy of the proposed model is higher than back propagation neural network (BPNN) and decision tree regression(DTR) , nonlinear support vector regression machine (SVR) and random walk (RW) , at the same time, it has the excellent memory capability of different length of historical data, and the excellent generalization capability on the test set.
英文关键词 long short-term memory recurrent neural networks; online car; traffic optimization; TensorFlow; deep learning
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收稿日期 2017/10/24
修回日期 2017/12/26
页码 756-761
中图分类号 TP391
文献标志码 A