Abstract

Road traffic flow models allow the development and testing of intelligent transportation solutions. Macroscopic intersection models are especially relevant for the simulation of large traffic networks. In this article, we study four first-order signalized and unsignalized intersection models. The two unsignalized approaches are the first-in-first-out (FIFO) model (roundabout-type intersection) and an optimal non-FIFO model (highway-type intersection). The optimal control operates upstream for the first signalized intersection model. It occurs downstream for the second signalized model. All four models satisfy the expected physical constraints of vehicle conservation, traffic demand, and assignment. The models are minimal and allow a comprehensible analysis of the results. We determine mathematical relationships between the intersection models and empirically analyze the performances using Monte Carlo simulations. The numerical simulations assume random demand, supply, and assignment. Besides average performances, the approach accounts for the flow ranges of variation. A benchmark analysis compares the intersection models. We observe that the optimal signalized intersection models overcome the performances of the FIFO model in congested states. They may even reach the performances of the idealistic non-FIFO model. Further applications for the four intersection models are discussed.

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