 The ability to quickly predict traffic patterns in different scenarios has become more urgent to support short-term operations and long-term transportation planning , emergency management, and resource allocation. Urban traffic exhibits a high spatial correlation in which links adjacent to a congested link are likely to become congested due to spillback effects. The spillback behavior requires modeling the entire metropolitan area to recognize all of the upstream and downstream effects from intentional or unintentional perturbations to the network.
<coherence>  The ability to quickly predict traffic patterns in different scenarios has become more urgent to support short-term operations and long-term transportation planning , emergency management, and resource allocation. Urban traffic exhibits a high spatial correlation in which links adjacent to a congested link are likely to become congested due to spillback effects. The spillback behavior requires modeling the entire metropolitan area to recognize all of the upstream and downstream effects from intentional or unintentional perturbations to the network.
 The ability to quickly predict traffic patterns in different scenarios has become more urgent to support short-term operations and long-term transportation planning . This requires modeling entire metropolitan areas to recognize the upstream and downstream effects from intentional or unintentional perturbations to the network.