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This paper offers an integrative data-driven physics-inspired approach tomodel and control traffic congestion in a resilient and efficient manner. Whileexisting physics-based approaches commonly assign density and flow trafficstates by using the Fundamental Diagram, this paper specifies the flow-densityrelation using past traffic information recorded in a time sliding window witha constant horizon length. With this approach, traffic coordination trends canbe consistently learned and incorporated into traffic planning. This paper alsomodels traffic coordination as a probabilistic process and obtains trafficfeasibility conditions using linear temporal logic. Model productive control(MPC) is applied to control traffic congestion through the boundary of thetraffic network. Therefore, the optimal boundary inflow is assigned as thesolution of a constrained quadratic programming problem.

An Integrative Data-Driven Physics-Inspired Approach to Traffic Congestion Control