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In this paper, we first present a centralized traffic control model based onthe emerging dynamic path flows. This new model in essence views the wholetarget network as one integral piece in which traffic propagates based ontraffic flow dynamics, vehicle paths, and traffic control. In light of thiscentralized traffic control concept, most requirements for the existing trafficcontrol coordination will be dropped in the optimal traffic control operations,such as the common cycle length or offsets. Instead, the optimal trafficcontrol strategy across intersections will be highly adaptive over time tominimize the total travel time and it can also prevent any short-term trafficcongestions according to the space-time characteristics of vehicle path flows.A mixed integer linear programming (MILP) formulation is then presented tomodel the propagations of path flows given a centralized traffic controlstrategy. Secondly, a new approximated Lagrangian decomposition framework ispresented. Solving the proposed MILP model with the traditional Lagrangiandecomposition approach faces the leveraging challenge between the problemtractability after decomposition and computing complexity. To address thisissue, we propose an approximate Lagrangian decomposition framework in whichthe target problem is reformulated into an approximated problem by constructingits complicating attributes into the objective function instead of inconstraints to reduce the computing complexity. The computing efficiency lossof the objective function can be mitigated via multiple advanced computingtechniques. With the proposed approximated approach, the upper bound and tightlower bound can be obtained via two customized dynamic network loading models.In the end, one demonstrative and one real-world example are illustrated toshow the validness, robustness, and scalability of the new approach.

Centralized adaptive traffic control strategy design across multiple  intersections based on vehicle path flows: An approximated Lagrangian  decomposition approach