Extension of a static into a semi-dynamic traffic assignment model with strict capacity constraints

To improve the accuracy of large-scale strategic transport models in congested conditions, this paper presents a straightforward extension of a static capacity-constrained traffic assignment model into a semi-dynamic version. The semi-dynamic model is more accurate than its static counterpart as it relaxes the empty network assumption, but, unlike its dynamic counterpart, maintains the stability and scalability properties required for application in large-scale strategic transport model systems. Applications show that, contrary to static models, semi-dynamic queue sizes and delays are very similar to dynamic outcomes, whereas only the congestion patterns differ due to the omission of spillback. The static and semi-dynamic models are able to reach user equilibrium conditions, whereas the dynamic model cannot. On a real-world transport model, the static model omits up to 76% of collective losses. It is therefore very likely that the empty network assumption influences (policy) decisions based on static model outcomes.