将OD交通量和路段通行能力作为离散随机变量，基于用户平衡分配模型，用近似算法求解行程时间可靠性。
Od demands and link capacities are treated as discrete random variables. Based on user-equilibrium traffic assignment model, an approximating algorithm is used to estimate the travel time reliability.
由于城市道路交通问题具有不确定性和不精确性，故采用基于粗糙集的交通信息提取计算理论建立城市道路行程时间预测模型。
Since urban travel times are stochastic and uncertain, a model for addressing urban travel time prediction by using transport information granular computing theory based on rough set was proposed.
提出了一种基于状态空间神经网络(SSNN)和拓展卡尔曼滤波(ekf)的混合式行程时间预测模型。
This paper presents a hybrid model for urban arterial travel time prediction based on the so-called state space neural networks (SSNN) and the extended Kalman Filter (EKF).
通过分析行程时间时间序列的时变特性，利用指数平滑模型进行预测，最后提出合理的修正方法。
Then, we make prediction with moving exponential average model after the analysis of the travel time series. Finally, we present reasonable justification.
给出了一种新的信号控制干道行程时间实时估计模型。
A new model for estimating the real-time travel time on a signalized arterial is developed in this paper.
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.
在第一种模型中，假定出行者的广义出行费用定义为平均行程时间和行程时间均方差的线性加权和，同时出行者能够正确认识自己的广义出行费用。
In the first model users' generalized travel cost are assumed to be weighted sum of the standard deviation and the mean travel time, and users have perfect knowledge of the road network condition.
在第一种模型中，假定出行者的广义出行费用定义为平均行程时间和行程时间均方差的线性加权和，同时出行者能够正确认识自己的广义出行费用。
In the first model users' generalized travel cost are assumed to be weighted sum of the standard deviation and the mean travel time, and users have perfect knowledge of the road network condition.
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