Learning "Look-Ahead" Nonlocal Traffic Dynamics in a Ring Road
Chenguang Zhao Huan Yu
The macroscopic traffic flow model is widely used for traffic control and
management. To incorporate drivers' anticipative behaviors and to remove
impractical speed discontinuity inherent in the classic
Lighthill-Whitham-Richards (LWR) traffic model, nonlocal partial differential
equation (PDE) models with ``look-ahead" dynamics have been proposed, which
assume that the speed is a function of weighted downstream traffic density.
However, it lacks data validation on two important questions: whether there
exist nonlocal dynamics, and how the length and weight of the ``look-ahead"
window affect the spatial temporal propagation of traffic densities. In this
paper, we adopt traffic trajectory data from a ring-road experiment and design
a physics-informed neural network to learn the fundamental diagram and
look-ahead kernel that best fit the data, and reinvent a data-enhanced nonlocal
LWR model via minimizing the loss function combining the data discrepancy and
the nonlocal model discrepancy. Results show that the learned nonlocal LWR
yields a more accurate prediction of traffic wave propagation in three
different scenarios: stop-and-go oscillations, congested, and free traffic. We
first demonstrate the existence of ``look-ahead" effect with real traffic data.
The optimal nonlocal kernel is found out to take a length of around 35 to 50
meters, and the kernel weight within 5 meters accounts for the majority of the
nonlocal effect. Our results also underscore the importance of choosing a
priori physics in machine learning models.