Two distinct ways of using kalman filters to predict urban arterial travel time

Two distinct ways of using Kalman filters to address the problem of short-term urban arterial travel time prediction have been presented in this paper. One is to train a neural network by incorporating the extended Kalman filter. This approach utilizes the extended Kalman filter to find the optimal weight parameters of neural networks. The other is to use the extended Kalman Filter to solve a state space model which is used to describe the dynamic changes of urban transportation systems, and obtain accurate state estimation of traffic variables. The former one can be treated as data-driven approach without more comprehensive knowledge of traffic theories, while the latter is model-based approach requiring general formulation of traffic systems. An empirical data set collected from an urban street in Holland is used to compare the performance of these two ways