Discovering Car-following Dynamics from Trajectory Data through Deep Learning
It addresses the need for interpretable models in traffic simulation and control, but appears incremental as it builds on existing deep symbolic regression methods.
This study tackled the problem of discovering mathematical expressions for car-following dynamics from trajectory data by proposing a deep symbolic regression framework with variable intersection selection and penalty terms, resulting in interpretable and parsimonious expressions.
This study aims to discover the governing mathematical expressions of car-following dynamics from trajectory data directly using deep learning techniques. We propose an expression exploration framework based on deep symbolic regression (DSR) integrated with a variable intersection selection (VIS) method to find variable combinations that encourage interpretable and parsimonious mathematical expressions. In the exploration learning process, two penalty terms are added to improve the reward function: (i) a complexity penalty to regulate the complexity of the explored expressions to be parsimonious, and (ii) a variable interaction penalty to encourage the expression exploration to focus on variable combinations that can best describe the data. We show the performance of the proposed method to learn several car-following dynamics models and discuss its limitations and future research directions.