Discovering Car-following Dynamics from Trajectory Data through Deep Learning

There are two recent trends in transportation and the broader science/engineering fields, which make the headlines almost every day. The first one is the emergence of connected/automated vehicles (CAVs) that i) may introduce new, complex traffic dynamics and interactions in the current and future traffic streams, and ii) generate increasingly available and massive datasets from both vehicles and the infrastructure. The second trend is the rapid development and application of deep learning techniques that seem to revolutionize almost every aspect of technology, science, engineering, and the entire society. While there have been numerous studies and applications of deep learning in transportation, in the paper, we are interested in the question of whether deep learning can help discover traffic dynamics (car-following models in particular) from data directly with no or little human involvement. An affirmative answer to this question will not only help discover/develop traffic dynamics models in this era but also have important implications for other science/engineering fields where dynamical systems and their governing equations are widely used and studied. Car-following depicts the driving behavior of how a vehicle (driver) follows and interacts with the vehicle in front of it. It is one of the basic traffic models in revealing traffic dynamics characteristics at the microscopic traffic flow level Brackstone and McDonald [1999]. Car-following studies can be traced back to the 1950s and 1960s when Pipes [1953], Chandler et al. [1958], Kometani and Sasaki [1958], Gazis et al. [1959, 1961], and Helly [1959] initiated an era of modeling car-following and traffic dynamics.