关于城市交通流量论文摘要的翻译

城市道路交通流量研究
Urban road traffic flow research
摘 要
准确的短时段交通流量预测在良好的道路交通管理中越来越成为至关紧要的一个步骤，对我国城市发展具有重要的意义。

The accurate short time interval traffic flow forecast more and more becomes an extremely important step in the good road traffic management, has the vital significance to our country urban development.
本文中~~~~
In this article mentioned very many now already in the application and the existence forecast model, like ARIMA model; Based on kalman filtering theory traffic flow forecast mold; In view of the different transportation condition, in the set existing forecast model several kinds come to the traffic flow to carry on the forecast one kind of unified model. The present paper mainly attempted has carried on the traffic flow using the BP nerve network method the real-time forecast. Before through the certain disposal procedure, will be able automatically to act according to the traffic flow forecast future 5 minutes traffic flow magnitudes, thus the arrangement transportation will unblock, alleviates the road congestion condition. Designs the neuron needs 48 inputs pitch points and 48 outputs neurons. The network takes 48-20-48 structure. The network design will be for according to the correlation transportation flow rate forecast future 5 minute in traffic flow magnitude. First trains has a lower error 平方和 has the ideal input the network. After each training power vector took the next group inputs the vector training the starting value. All designs thought manifests in the procedure, network training is carries on with the auto-adapted study speed and attachment momentum method function traingdx.m. The training result through passes the current capacity forecast the training performance chart performance.
该方法具有很强的学习能力和自适应性,因而具有很好的应用价值。
This method has the very strong learning capability and auto-adapted, thus has the very good application value.

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