Dynamic traffic models that reproduce the pattern of congestion propagation in urban road networks are essential for the evaluation of traffic management strategies and prediction of traffic states. Many approaches developed in the past simplified the movement of vehicles and the propagation of traffic waves. The overlooked problems can be particularly influential for the simulation of interrupted flow with traffic signal control due to the frequent accumulation and dissipation of the waiting queue on a road link. This study first reviews state-of-the-art macroscopic and mesoscopic link-level urban traffic flow models and provides a comparative discussion of their similarities, differences, and gaps. We then put forward an event-based mesoscopic model to simulate link-level interrupted flow traffic dynamics (LIFT). The model (i) simulates the transmission of vehicles between links based on the demand and supply of exit and entry events, (ii) monitors queue spillback through the consideration of backward traveling spaces, and (iii) adheres to first-in-first-out at intersections for congested situations. Taking the outcomes generated from microscopic simulation as ground-truth, the case studies show that LIFT outperforms the other models by accurately capturing the evolution of link densities and mean path speeds in congested conditions. It is reliable even in complex scenarios with diverge blocking phenomena and desired speed heterogeneity. Without having to simulate the interaction between individual vehicles, the model also becomes much more computationally efficient than microscopic simulation. It can be applied in simulation-based optimization or control problems which require the consideration of finer-level details that macroscopic models are unable to offer.
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