dc.description.abstract | In recent years, the developments of self-driving cars and smart cities have been studied. The prediction and simulation of traffic flow require more accurate, larger-scale, and longer-term forecasts. The current traffic flow prediction model comes from two classes: One is a mathematical model based on the theory of traffic flow, which can stably give long-term and accurate results under ideal conditions. The other one is a machine learning model based on training a model and analysis of data, which can give us some information beyond the theory of traffic. To take advantage of the methods from these classes to obtain a better prediction, we develop a hybrid method, namely the EKF-LSTM method, under the framework of the data assimilation system. The key ingredients of the proposed technique include a numerical prediction method based on the Godunov scheme for the Lighthill-Whitham-Richards equation with the MacNicholas model. We build an extended Kalman Filter (EKF) with the prediction model to reduce the observation error and obtain a better initial value. At the same time, due to the requirement of boundary data for the Godunov scheme′s discretization, we introduce the long short-term memory (LSTM) method, which is a deep learning method commonly used in prediction problems, to find the boundary data. The multiple full-range predictions given by LSTM are also assimilated into the Kalman filter to obtain the advantages of two prediction models. The Kalman filter can reduce the effect of observation error in the prediction of LSTM. In the experiment, %the Riemann Problem is used as the test case to analyze the extended Kalman filter and the numerical model that we build. In addition,
we use the data published by the Ministry of Transportation and Communications in Taiwan to build real-world experiments to test and analyze the EKF-LSTM method we develop. The results show the differences between the numerical model and the machine learning model in the prediction problem. Also, the LSTM-EKF method can successfully filter out the noise from observation errors and perform better than the traditional EKF and LSTM alone methods. | en_US |