提出了一种新的基于移动检测技术、神经网络和模糊判断方法的城市路网动态交通拥挤预测模型。
A model for urban road network traffic congestion forecast based on probe vehicle technology, fuzzy logic judgement and back-propagation (BP) neural network was proposed.
基于基态修正模型以及对象版本管理的思想，提出用于动态道路网络数据管理的时空数据模型。
A spatiotemporal data model is proposed to represent dynamic road networks based on base State with Amendments model and object-version management theories.
在构建道路网的数学模型中着重研究了基于动态随机时间的道路行程时间预测。
The forecast of the route travel time based on dynamic random time is stressed and studied in the mathematics model.
在此背景下，本文提出了基于二源数据的城市路网动态od估计模型与算法。
In this context, it develops time-varying od demands models and algorithms of urban road network based on dual sources data.
在此背景下，本文提出了基于二源数据的城市路网动态od估计模型与算法。
In this context, it develops time-varying od demands models and algorithms of urban road network based on dual sources data.
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