Road congestion condition analysis method

一种道路拥堵情况分析方法

Abstract

本发明提出了一种道路拥堵情况分析方法,用于分析下一时刻的道路拥堵情况,具体包括:建立道路拥堵预测模型,所述道路拥堵预测模型是融合了BP神经网络模型和SVM(支持向量机)模型相融合的模型;采集交通流量数据并进行数据预处理;将交通流量数据输入道路拥堵预测模型,得到预测的道路拥堵情况:首先分别通过BP神经网络模型和SVM(支持向量机)模型预测交通拥堵情况;然后将上述两个模型的输出结果加权求平均值,作为最终的结果。本发明可以利用当前时刻相关的交通流量以及历史时刻相关交通流量,采用改进的神经网络模型进行交通流量的预测,提高了预测的效率和准确性。
The invention discloses a road congestion condition analysis method, and aims at analyzing the road congestion condition of the next moment. The method specifically comprises the following steps of: establishing a road congestion prediction model, wherein the road congestion prediction model fuses a BP neural network model and an SVM (support vector machine) model; acquiring traffic flow data and preprocessing the traffic flow data; and inputting the traffic flow data into the road congestion prediction model so as to obtain a predicted road congestion condition: firstly predicting a traffic congestion condition through the BP neural network model and the SVM (support vector machine) model, and then weighting output results of the two models to solve an average value and taking the average value as a final result. According to the method disclosed by the invention, traffic flow prediction can be carried out by utilizing a traffic flow related to the current moment and traffic flows related to history moments and adopting an improved neural network model, so that the prediction efficiency and correctness are improved.

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