交通流量预测是智能交通系统研究的一个重要课题。
Traffic flow forecasting is one of the important issues for the research of Intelligent Transportation System.
交通流量预测是智能交通系统(its)研究的一个重要课题。
Traffic flow forecasting is one of the important issues for the research of Intelligent Transportation System (ITS).
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。
The precondition and key of intelligent control of urban traffic is real time and exact traffic flow prediction.
第二章主要阐述交通流的理论相关知识、当前常用的交通流量预测方法。
In the second chapter, correlative knowledge of the traffic flow theory and current traffic flow forecast methods are introduced.
交通流量预测与分析是公路建设项目可行性研究或后评价的重要组成部分。
Traffic volume forecast and analysis is an important proportion of highway construction feasibility study and post-evaluation.
最后提出一种根据短期交通流量预测结果的人工智能解决交通拥堵的方案。
By comparing with those results, the neural network is the best in terms of exactness. Finally a method of intelligent to solve traffic jam is presented.
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。
In response to various characteristics of the present road traffic flow prediction, a combined prediction is presented in this article.
通过对2009年上海城市交通流量预测结果的分析，证明该方法可提高预测准确度。
By analyzing the forecasting results of Shanghai's traffic flow in 2009, it is shown that the proposed method can improve the accuracy of forecast.
船舶交通流量预测是将水运工程技术和经济预测技术有效结合的起来的一个新兴的学科领域。
Ship traffic volume forecasting is a new domain which combines water traffic engineering with economic forecasting.
由于影响交通流量的因素众多，这就给交通流量预测，尤其是短时的交通流量预测带来了困难。
There are many factors that can influence the traffic flow, all of these results in the difficulties of real-time traffic flow forecasting.
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。
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.
全面、准确的采集交通信息是实现交通智能化的基本保障，交通流量预测的准确性也取决于数据样本的准确性。
Collecting the exact and complete traffic information is the basic ensure of finishing intelligent traffic. Besides, the veracity of data samples decides the veracity of predicting traffic flow.
本文通过实地调查获取的交通流量数据，分别采用移动平均法、指数平滑法、AR模型法三种交通流预测方法进行短时交通流量预测。
Through the data obtained by fieldwork, the paper forecasts the short-term traffic by three methods: moving-average method, index-smoothing method, AR model method.
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。
Method named BAYESIAN combined neural network model is proposed for short term traffic flow prediction in this paper.
基于四阶段法，采用双重力模型，预测全国空域的交通流量OD分布。
The four stage method and a double gravity model were used to forecast the OD(origin-destination) distribution of air traffic flow in the whole airspace of China.
因此很容易预测交通流量通过网状网络因为他们对整个路径计算一次。
Therefore it is very easy to predict the traffic flows through the meshed network since they are calculated once for the entire path.
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。
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.
最后将其转换为交通流量数据，得到预测结果。
At last, the traffic flow forecasting data were obtained by an inverse transform.
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。
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.
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。
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.
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。
Accordingly, this paper proposes a fusion-prediction model of traffic-flow in urban road-intersection based on integrated ANN (Artificial Neural Network).
提出了一种预测交通流量的动态组合建模方法。
A combined dynamic method of forecast traffic volume time series is proposed.
没有人能预测将来交通流量会增长到什么程度。(定语从句倒译。)
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。
Road traffic noise in a sample housing estate was assessed using prediction method and based on collected traffic flow and vehicle type data.
在交通流诱导中，交通流量的预测是研究热点。
The traffic flow forecasting is the hot spot in the research of the traffic flow guidance.
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 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。
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.
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。
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。
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.
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