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

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 traveltime and flow Consideration of traffic states several time steps before Simultaneous estimation ofOD travel time and flow2) Identification of Model Parameters of a Macroscopic Traffic FlowSimulation Model （Comparison of non-linear least mean squared method with Box Complex method3）Travel Time Using Traffic State Variables Estimated by a Feedback Technique Indirecttravel time using traffic state variables by Kalman Filtering技术Comparison of the indirect method with the NKF model Validation of the indirect methodfor both free flow and congested states4) Estimation of Traffic States on Freeway Using ProbeVehicle Data（使用Kalman filtering method by assembling the probe Vehicle Data into the）fixed detector data Numerical examination of four estimation scenarios for various trafficsituations / Success of the开发oped model in improving the estimation accuracy by 30 to 80% comparedwith the one estimated by only the macroscopic traffic flow model. Quantification of the probevehicle percentage and the sampling interval

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