Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : June 28, 2023 - July 01, 2023
Trip time prediction (TTP) have great importance in traffic analysys and management. Existing research related to traffic forecasting, including traffic speed, enable TTP. However, these studies mainly use aggregated data such as vehicle detectors which cause intergration error. Trip time is realised by the integration on predicted speed, but the accuracy of the actual trip time, which can be gathered by vehicle trajectory data, is not guaranteed. In this research, we propose the extension of Neural ODE which can minimise integration error. The proposed method can learn velocity field from ETC2.0 probe data that is a type of vehicle trajectory data. The experiment result using artificial dataset and large scale dataset shows superiority and learning stability of proposed method.
