摘要
介绍了用于短期交通流预测的两大类模型:统计预测算法和人工神经网络模型。对其中各种模型的特征进行了比较,将历史平均模型、求和自回归滑动平均模型(ARIMA)、非参数回归模型、径向基函数(RBF)神经网络模型与贝叶斯组合神经网络模型,应用于一个真实路网的短期流量预测,比较了各模型的预测结果。结果表明,组合神经网络模型预测误差最小,可靠性最高,是一种对短期交通流预测的有效方法。
A large number of techniques have been applied into short-term traffic flow prediction, which can be classified into two groups: statistical models and artificial neural network model. The models and their application were discussed and compared. Several models, including historical average, ARIMA (auto regressive integrated moving average) model, nonparametric regression, RBF (radial basis function) neural network and Bayesian combined neural network model were applied into a numerical example of short-term traffic volume prediction in a field network, their prediction results and performances were compared. It was found that the error of hybrid neural network model is littlest, its prediction reliability is highest, it is the most effective method to predicte short-term traffic flow. 2 tabs, 2 figs, 17 refs.
出处
《交通运输工程学报》
EI
CSCD
2004年第4期68-71,83,共5页
Journal of Traffic and Transportation Engineering
关键词
交通工程
短期交通流
预测
方法
比较
Forecasting
Mathematical models
Motor transportation
Neural networks
Performance
Regression analysis
作者简介
史其信(1946-),男,北京人,清华大学教授,从事智能交通系统研究.
