Real-time traffic forecasting with recent DTA methods

In this paper we revisit the real-time traffic forecasting problem. We review recently proposed Dynamic Traffic Assignment (DTA) methods and verify how they can improve the practice of traffic forecasting. In particular, we analyze: 1) the Gradient projection DTA model of Gentile (2016), 2) Day-to-day model by Watling and Cantarella (2016), 3) the Marginal Computation (MaC) method by Corthout et al. (2014), 4) dynamic origin-destination (O-D) demand estimation methods (Kostic and Gentile, 2015) and 5) the event rerouting model (Kucharski and Gentile, 2014). We discuss how these methods can be applied to improve short-term forecasting and, most importantly, if they are efficient and mature enough for practical, real-time implementations. We formulate the real-time DTA forecasting problem which searches for the solution using all of the above DTA methods. The main contribution of this paper can be seen as a review and synthesis of recently proposed DTA methods, summarized with conceptual real-time forecasting framework