Method named BAYESIAN combined neural network model is proposed for short term traffic flow prediction in this paper.
提出一种新的贝叶斯组合神经网络模型并将其应用于短期交通流量的预测。
Accurate short-term traffic flow forecasting is becoming a crucial step in its research, especially, for its Advanced traffic Management System and Advanced Traveler Information System research.
及时准确地进行交通流短时预测是智能交通系统，尤其是其先进的交通管理系统与先进的出行者信息系统研究的关键内容之一。
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.
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。
The short-term induced passenger flow includes the new passenger flow resulted from the trip distribution change and the trip frequency growth caused by traffic condition change.
近期诱增客流量包括由交通条件改变导致出行分布变化和出行频率增加引发的新客流量。
The results show that the GRNN model constructed in this way can precisely forecast urban short-term traffic flow.
研究结果表明，构建的神经网络模型能够很精确地实时预测城市道路短期交通流。
Among them, traffic flow prediction especially short-term traffic flow prediction is an important factor, which decided the road weights in dynamic path planning.
其中，交通流预测尤其是短时交通流预测是动态路径规划中决定道路权重的重要因子。
Results of the test using field data show that the spline fitting can make a better compromise of best fitting and smoothness, and the algorithm performs better in short-term traffic flow forecasting.
经过实测数据仿真试验表明，样条拟合能较好地兼顾最优拟合与曲线光滑度的选择，算法的预测效果良好。
Short—term traffic flow prediction is the basis of dynamic traffic control and guidance.
短时交通流预测是动态交通控制和诱导的前提。
Short—term traffic flow prediction is the basis of dynamic traffic control and guidance.
短时交通流预测是动态交通控制和诱导的前提。
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