The invention discloses a short-time traffic flow prediction system based on an SARIMA-GA-Elman combined model, and belongs to the technical field of traffic flow prediction of an intelligent traffic system. The method is realized through the five steps of modeling of an SARIMA model, Elman-RNN prediction, SARIMA-GA-Elman combined prediction, linear prediction and nonlinear prediction. The invention provides a novel optimization method to solve the problem that training of the neural network often consumes a large amount of time cost. The method comprises the following steps: firstly, training an Elman-RNN weight and a threshold value based on GA to obtain an interval near value of an optimal solution, and then continuously training by using an Elman-RNN gradient descent algorithm to obtain a final weight and a final threshold value; the optimization mode is very similar to transfer learning, and the training efficiency of the model can be improved. According to the method disclosed by the invention, the Elman-RNN nonlinear model is constructed by utilizing a prediction error subjected to SARIMA prediction. The prediction error excludes the interference of a linear factor and a daily cycle factor, and comprises the nonlinear characteristics of the traffic flow. The two models fully consider periodic, linear and nonlinear characteristics of traffic flow.
本发明公开了基于SARIMA‑GA‑Elman组合模型的短时交通流量预测系统,属于智能交通系统车流量预测技术领域。本发明通过SARIMA模型的建模、Elman‑RNN预测、SARIMA‑GA‑Elman组合预测、线性预测和非线性预测这五个步骤来实现。本发明训练神经网络往往要耗费大量时间成本,本发明提出了一种新颖的优化方法来解决这个问题。首先,基于GA训练Elman‑RNN的权值和阈值得到最优解的区间临近值,然后使用Elman‑RNN的梯度下降算法继续训练得到最终的权值和阈值。这种优化方式和迁移学习非常相似,可以提高模型的训练效率。本发明利用经过SARIMA预测后的预测误差构建Elman‑RNN非线性模型。预测误差排除了线性因素和日周期因素的干扰,包含交通流的非线性特征。两个模型充分考虑了交通流的周期性,线性和非线性特征。
Short-term traffic flow prediction system based on SARIMA-GA-Elman combined model
基于SARIMA-GA-Elman组合模型的短时交通流量预测系统
2021-05-07
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES |
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