Dynamic Estimation of OD Flow and OD travel Time Based on Measurement Data

1）OD旅行时间和OD流量的动态估计·神经卡尔曼滤波器（NKF）模型通过集成宏观交通模拟模型到卡尔曼滤波器估计OD旅行时间和流量·考虑交通状态的几个时间步骤之前·OD旅行时间和流量的模拟估计2）宏观交通流量模拟模型的模型参数的识别·非-使用箱式复合体方法的线性最低平均平方法（3）根据反馈技术估计使用交通状态变量的旅行时间的间接估计（3）根据卡尔曼过滤技术估计使用交通状态变量的间接估计（3）与NKF模型的间接方法的比较）。对两种自由流动和聚合状态的间接方法的验证4）使用探测器数据对高速公路交通状态的估计;通过汇编探测器数据将探测器数据收集到固定探测器数据中的卡尔曼过滤方法的形成;对各种交通状况的四个估计场景的数值测试;改进估计准确度的开发模型的成功;与只有一个估计的宏观交通流量模型进行比较。探测器车辆百分比和取样间隔的量化

1) Dynamic Estimation of OD Travel Time and OD Flow・Development of a Neural Kalman Filter (NKF) model by integrating a macroscopic traffic simulation model into Kalman filter to estimate OD travel time and flow・Consideration of traffic states several time steps before・Simultaneous estimation of OD travel time and flow2) Identification of Model Parameters of a Macroscopic Traffic Flow Simulation Model・Comparison of non-linear least mean squared method with Box Complex method3) Estimation of Travel Time Using Traffic State Variables Estimated by a Feedback Technique・Indirect estimation of travel time using traffic state variables estimated by Kalman Filtering technique・Comparison of the indirect method with the NKF model.・Validation of the indirect method for both free flow and congested states4) Estimation of Traffic States on Freeway Using Probe Vehicle Data・Formulation of Kalman filtering method by assembling the probe vehicle data into the fixed detector data・Numerical examination of four estimation scenarios for various traffic situations・Success of the developed model in improving the estimation accuracy by 30 to 80% compared with the one estimated by only the macroscopic traffic flow model.・Quantification of the probe vehicle percentage and the sampling interval