  This paper offers an integrative data-driven physics-inspired approach to
model and control traffic congestion in a resilient and efficient manner. While
existing physics-based approaches commonly assign density and flow traffic
states by using the Fundamental Diagram, this paper specifies the flow-density
relation using past traffic information recorded in a time sliding window with
a constant horizon length. With this approach, traffic coordination trends can
be consistently learned and incorporated into traffic planning. This paper also
models traffic coordination as a probabilistic process and obtains traffic
feasibility conditions using linear temporal logic. Model productive control
(MPC) is applied to control traffic congestion through the boundary of the
traffic network. Therefore, the optimal boundary inflow is assigned as the
solution of a constrained quadratic programming problem.
