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。
The precondition and key of intelligent control of urban traffic is real time and exact traffic flow prediction.
城市交通的智能控制实现的前提和关键是实时准确的交通流量预测。
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。
In response to various characteristics of the present road traffic flow prediction, a combined prediction is presented in this article.
针对当前道路交通流量预测的多种不同特性的方法，提出了一种组合预测方法。
The key problems of vehicle navigation dynamic path planning are traffic network model, path planning algorithm and traffic flow prediction.
交通网络模型、路径规划算法以及交通流预测是车辆导航动态路径规划需要解决的重点问题。
The application results show that it is an efficient method for traffic flow prediction and the prediction accuracy can meet the actual requirements.
应用结果表明该方法可以有效地对交通流量进行预测，且预测精度可以满足实际交通诱导的需要。
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。
The prediction results will have direct effect on traffic control and traffic guidance. A traffic flow prediction model using support vector machines(SVMs) based method is proposed.
提出一种基于支持向量机的交通流量实时预测模型，通过采用序贯最小优化算法，能够实现对交通流量的有效预测。
Based on the introduction to neural network principle, two methods of solving free train schedule and traffic flow prediction in transportation system with neural network are analyzed.
在简要介绍神经网络原理的基础上，分析了采用神经网络解决交通系统中空车调度及交通流预测的原理及方法。
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.
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。
By analyzing short time traffic flow time sequence property, the gray system theory is used for short time traffic flow prediction and the scrolling GM (1, 1) prediction model is set up.
通过分析短时交通流时序特性，将灰色系统理论应用于短时交通流预测，建立了滚动GM(1,1)预测模型。
Practical prediction research of urban traffic flow shows that this model has famous predicted precision, and it can provide exact reference for urban traffic programming and control.
实际的城市交通流量预测研究表明，该模型具有较高的预测精度，可以为城市交通规划和控制提供准确的参考。
The present thesis provides a method to predict the traffic flow in a short period by combining several prediction models and artificial intelligence.
本文提出了一种将多种预测模型与人工智能技术相结合的短时交通流智能预测方法。
Considering the nonlinearity, complexity and randomicity of elevator traffic flow, the prediction model of elevator traffic flow based on wavelet support vector machines was proposed.
考虑到电梯交通流本身所存在的非线性、复杂性和随机性，提出了一种基于小波支持向量机的电梯交通流预测模型。
In this paper, the time - sequence model of traffic flow is based on the improved BP neural network, and this model can be used for short time prediction of traffic flow.
本文采用改进型BP神经网络建立起交通流的时间序列模型，该模型可用于短期内道路交通流量的预测。
Aiming at the issue about multi-step prediction of the traffic flow chaotic time series, a fast learning algorithm of wavelet neural network (WNN) based on chaotic mechanism is proposed.
针对交通流量混沌时间序列多步预测的问题，提出了一种基于混沌机理的小波神经网络(WNN)快速学习算法。
Accordingly, this paper proposes a fusion-prediction model of traffic-flow in urban road-intersection based on integrated ANN (Artificial Neural Network).
基于此，本文提出了基于集成神经网络的城市道路交通流量的融合预测模型。
The prediction of network traffic flow is a problem of great significance in the research work of resource allocation and congestion control.
在高速网络资源分配与拥塞控制研究中，网络业务流量的预报是一个具有重要意义的课题。
Traffic flow guidance system (TFGS) plays an important role in intelligent transportation system (ITS). The prediction of traffic flow is the core issue of TFGS.
交通流诱导系统是智能交通系统领域中一项重要的研究内容，而交通流量的预测问题则是交通流诱导系统的核心问题。
This paper makes approaches to Shanghai—Jiading Light railway traffic model and vehicle type selection, fundamental flow prediction of passengers, prediction technology and procedure etc.
围绕沪嘉轻轨交通的交通模式选择条件、车辆制式的选择以及客流预测基本方法的构思、预测技术方法与流程等进行了探讨。
Road traffic noise in a sample housing estate was assessed using prediction method and based on collected traffic flow and vehicle type data.
根据实测所得交通流量和车型分布，采用预测为主、实测为辅的方法对典型居住小区进行了交通噪声预测与评价。
Experimental results show that the proposed Volterra adaptive prediction model is capable of effectively predicting traffic flow time sequence and low-dimensional chaotic time sequence.
结果表明，该模型能够较准确地预测交通流量时间序列和低维混沌时间序列。
The application results show that the proposed method is effective for the prediction of traffic flow and the prediction accuracy can meet the actual requirements.
应用结果表明：该方法可以有效地对交通流量进行预测，且预测精度可以满足实际交通诱导的需要。
The application results show that the proposed method is effective for the prediction of traffic flow and the prediction accuracy can meet the actual requirements.
应用结果表明：该方法可以有效地对交通流量进行预测，且预测精度可以满足实际交通诱导的需要。
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