【摘要】 将主成分分析和支持向量机回归相结合,进行道路网多断面的短时交通流量预测研究。首先,整理分析路网中多个断面交通流量数据进行主成分分析,得到主成分数据序列;其次,根据主成分数据序列建立训练集训练支持向量机,并利用遗传算法优化参数;最后,输入支持向量机所需数据,得到主成分预测结果,转化为断面交通流量数据,从而预测道路网短时交通流量。采用城市快速路多断面数据进行实例分析,结果表明,该模型比单一断面预测方法的效果更好。更多还原

【Abstract】 A scheme to forecast short-term traffic volumes of a road network is presented.This scheme combines the principal component analysis and the support vector regression.First,the traffic volume data of multi-road-cross-sections acquired from a road network are turned into several time series data by the principal component analysis.Then,these principal component data are used to train the support vector machines,and a genetic algorithm is applied to optimize the parameters of the support vector machines.After the training and optimization,by inputting required data to the support vector machines,the principal components are obtained as the outputs.These outputs are then transformed into the forecasting data of the short-term traffic volumes.A case study is carried out to validate the scheme.The traffic volume data from seven road cross-sections of an urban express-way are used.The forecasting results by this scheme are much better than that by the schemes of single road cross-section.更多还原