The rapid development of intelligent transportation system technologies and the policy emphasis on their deployment have increased the importance of predictive dynamic network flow models, especially so-called dynamic network loading and dynamic traffic assignment models. In this chapter the authors provide a critical review of analytic models used in predicting time-varying urban network flows. Specifically, the authors examine and compare four types of dynamics used as the foundation of dynamic network models: (1) dynamics based on arc exit-flow functions, (2) dynamics for which both exit and entrance flow rates are controlled, (3) dynamics based on arc exit-time functions, and (4) tatonnement and projective dynamics. The other assumptions attached to these dynamics to create dynamic network loading and dynamic traffic assignment models are then described.
Abstract