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  • DynaMIT is a dynamic traffic assignment (DTA) system for traffic estimation and prediction developed at the MIT Intelligent Transportation Systems Laboratory

  • DynaMIT 2.0 is a next generation multi-modal traffic state prediction and network control platform, which enhances the original DynaMIT system through the simultaneous/sequential calibration of demand and supply parameters.

  • The system also includes a strategy optimization module that optimizes network control strategies in real time for congestion mitigation and other objectives.

DynaMIT 2.0 is a multi-modal multi-data source driven, simulation-based short-term traffic prediction system developed by SMART-FM. The system contains a collection of data modules and algorithms that estimate and predict network states in real-time. The main features of DynaMIT 2.0 include: a) the modeling of multiple modes including public transit, mobility-on-demand services such as Uber/Lyft, car and ride sharing, etc.; b) Online calibration of demand and supply parameters using heterogeneous data sources; c) context mining and the scenario analyzer for unstructured data, e.g. traffic incident reports; d) The strategy optimization module for network control strategy optimization. DynaMIT 2.0 performs state estimation and state prediction at each interval of the simulation based on a rolling horizon and is designed to reside within a traffic management system. In the state estimation phase, the current network condition is estimated with real-time traffic information; in the state prediction phase, future short term network conditions are predicted and consistent traffic guidance information and optimal control strategies are determined.


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