基于神经网络的交通流预测研究

河北工业大学

硕士学位论文

基于神经网络的交通流预测研究

姓名：彭进

申请学位级别：硕士

专业：模式识别与智能系统

指导教师：赵晓安

20081101

河北工业大学硕士学位论文

基于神经网络的交通流预测研究

摘要

作为智能交通系统的核心内容之一，智能交通控制与诱导系统一直是智能交通研究的热门课题。城市交通流控制与诱导系统的实现将有效地减少交通拥挤和城市环境污染，提高道路通行能力和改善交通安全状况。而实时、准确的交通流量预测正是这些系统实现的前提及关键，交通流量预测结果的好坏直接关系到交通控制与诱导的效果。交通控制与诱导系统需要在做出控制(诱导)变量决策的时刻对下一决策时刻乃至以后若干时刻的交通流量做出实时预测。

目前，我国普遍采用遥感微波检测器或环形线圈检测器检测实时交通流量。但是，对于一个完善的交通流诱导系统而言，采用实时检测设备检测的交通流信息具有滞后性。因此，实现城市交通流诱导系统的关键是道路交通状况的预测，也就是采用相应的技术，以有效地利用实时交通数据信息滚动预测未来一段时间内的交通状况。根据预测的交通流信息实现交通流的诱导，以避免交通拥挤，实现交通的畅通。

本文主要研究人工神经网络在实时交通流预测中的应用。在应用人工神经网络预测交通流量方面提出了有效的途径。本论文的主要研究工作为：

(1)介绍了交通流预测系统基本概念及理论框架，并提出了路段短时交通流预测

模型；

(2)利用BP神经网络的优势，提出了一种改进型BP网络算法。实验结果表明

该算法在路段短时交通流预测方面有着优良的效果；

(3)结合递归Elman网络和BP网络的优点，提出了一种综合型交通流预测算法。

该算法具有较强的非线性函数逼近能力和学习能力，为路段短时交通流预测

提供了一种有效的途径。

关键词: 交通流，人工神经网络，BP网络，Elman网络，预测

i

基于神经网络的交通流预测研究

RESEARCH ON TRAFFIC FLOW FORCASTING

BASED ON NEURAL NETWORK

ABSTRACT

As the core of one of the Intelligent Transportation System,Intelligent Traffic Control and Guidance System has been the hot topic of intelligent traffic study.Urban traffic control and induction system will be effective in reducing traffic congestion and urban pollution,to improve road traffic safety and the situation.The real-time and accurate traffic flow forecast is the prerequisite and key for the realization of these systems,traffic projections show that the outcome will have a direct bearing on the traffic control and induced effects.Traffic control and guidance system needed to make real-time forecast in the next moment and even some time after when it needs to make the variables of control or induce.

At present,China is commonly using remote sensing microwave detector coil or loop detector testing real-time traffic flow.However,to a well-induced traffic flow system,using real-time detection equipments detect traffic flow information with a lag.As a result,the key of achieving the object of urban traffic flow guidance system is road traffic conditions forecasting,that is,using the appropriate technology,effectivly using the real-time traffic datas to predict future traffic conditions rollinglly over a period of time.According to the forecasting traffic flow information to achieve the traffic flow-induced and avoid traffic congestion and the smooth flow of traffic.

This paper studies artificial neural network in real-time traffic flow forecast.Making a effective way to the application of artificial neural network in traffic prediction.The main research to this thesis:

（1）Introduced the basic concept and theoretical framework of the traffic flow foreca- sting system.Made a model of short-term traffic flow prediction;

（2）Used the advantages of BP neural network,maked an improved BP network algor- ithm.The result shows that the method has a good effection in the short-term forecast.

（3）Combined the advantages of the Elman network and the BP Network, made a other traffic flow prediction algorithm.The algorithm has highly nonlinear function approx- imation and learning ability,provides a effective way to the short-term traffic flow forecast- ing.

KEY WORDS: traffic flow, artificial neural network, bp network, elman network, forecast ii