Institute of Transportation, MOTC

　　This research aims at integrating dynamic traffic assignment model DynaTAIWAN with the Kalman Filtering (KF) approach to construct the dynamic Origin-Destination (O-D) estimation and prediction model; the dynamic parameters based on the historical and real time data are generated to meet the dynamic traffic conditions. The contributions include: 1. the model takes the deviations of O-D flows from historical averages instead of O-D flows as the state vectors to increase the accuracy and normalization of estimations; 2. the time-dependent assignment matrix is gained in advance via C++, the historical O-D flows are assigned into DTA and the vehicle trajectory data accounts for calculating the assignment parameters; 3. the procedure of O-D estimation and prediction is decomposed into 7 steps, and the efficiency and accuracy can be improved step by step. Numerical experiments to illustrate the proposed model are conducted in three networks: a small test network, a signalized urban network and a 50-node mixed network, and several sensitivity analyses are performed. The measurement criteria includes RMSE and the chi-square test which are utilized to examine the results, the results show that the RMSE values are less than 2, 4, and 17 on the three networks respectively, and the chi-square tests reveal there are no differences between the estimated and real O-D flows. The numerical results indicate that estimated O-D values from the proposed model are reasonable and accurate.