  This paper introduces a new macroscopic perspective for simulating
transportation networks. The idea is to look at the network as connected nodes.
Each node sends an information package to its neighbors. Basically, the
information package contains a state change that a specific node experienced,
and it might affect the traffic network state in the future. Different types of
information can be counted in transportation network. Each information type has
different characteristics. It propagates through the network and interacts with
other IPs and nodes. As a result, the model enables implementing and analyzing
dynamic and inconvenient control strategies. This paper focus on flow dynamics
and demand routing information under complex environment. The flow dynamics
flows the LWR theory along the links. The demand routing follows a node
equilibrium model. The node model takes into account the users preferences and
choices under dynamic control agents such as dynamic tolling and sends the
needed information for other facilities to operate. A DNL model is developed
based on the IPM. Several case studies demonstrate the use IPM and its
potential to provide a reliable and results for real world complicated
transportation applications.
