基于贝叶斯网络多方法组合的短时交通流量预测

贝叶斯网络是处理不确定信息和进行概率推理的有力工具，针对短时交通流量预测的难题，提出一种基于贝叶斯网络的多方法组合预测模型. 首先建立几种基本预测模型并对交通流量进行预测，然后将预测的结果和实际结果按一定步长进行离散处理，把离散后的结果用贝叶斯网络进行学习，更新贝叶斯网络参数，通过联合推理求得各个基本预测模型预测结果组合下可能组合预测值的后验概率，把后验概率最大所对应的值作为预测值. 通过对实际道路交通流量的预测表明，本文提出的贝叶斯网络多方法组合预测模型的预测结果精度优于单一的预测模型，从而论证了本文提出的贝叶斯网络多方法组合预测模型具有一定的实用性.

Short-Term Freeway Traffic Flow Prediction Based on Multiple Methods with Bayesian Network

Bayesian network is one of the most efficient models in the uncertain knowledge and reasoning field.A method based on Bayesian networks of combination mode is put forward to solve the problem of short-term traffic flow prediction.First,several basic prediction models are used to predict the traffic flow.The prediction results and the actual traffic flow are discretized by certain step length.Then,the parameters of the Bayesian network are updated by learning those data.Through combination of reasoning,every...