基于LSTM-BP组合模型的短时交通流预测  

Short-Term Traffic Flow Forecasting Model Based on LSTM-BP

作　　者：李明明 雷菊阳[1] 赵从健 LI Ming-Ming;LEI Ju-Yang;ZHAO Cong-Jian(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学机械与汽车工程学院

出　　处：《计算机系统应用》2019年第10期152-156,共5页Computer Systems & Applications

摘　　要：为减轻日益严重的交通拥堵问题,实现智能交通管控,给交通流诱导和交通出行提供准确实时的交通流预测数据,设计了基于长短时记忆神经网络(LSTM)和BP神经网络结合的LSTM-BP组合模型算法.挖掘已知交通流数据的特征因子,建立时间序列预测模型框架,借助Matlab完成从数据的处理到模型的仿真,实现基于LSTMBP的短时交通流精确预测.通过与LSTM\BP\WNN三种预测网络模型的对比,结果表明LSTM-BP预测的时间序列具有较高的精度和稳定性.该模型的搭建,可对交通分布的预测、交通方式的划分、实时交通流的分配提供依据和参考.In order to alleviate the increasingly serious traffic congestion problem, realize intelligent traffic control, provide accurate real-time traffic flow prediction data for traffic flow induction and traffic travel, an LSTM-BP combined model algorithm based on long-short-time memory neural network (LSTM) and BP neural network is designed. Mining the characteristic factors of known traffic flow data, establishing the framework of time series prediction model, and using Matlab to complete the simulation from the data processing to the model simulation to realize the accurate prediction of short-term traffic flow based on LSTM-BP. Compared with the three prediction network models of LSTM\BP\WNN, the results show that the time series predicted by LSTM-BP has higher accuracy and stability. The construction of the model can provide basis and reference for the prediction of traffic distribution, the division of traffic modes, and the distribution of real-time traffic flow.

关 键 词：智能交通系统 LSTM-BP模型 时间序列 短时交通流预测 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]