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).
应用推荐