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CrowdFlows develops predictive models of travel demand by analyzing online event information and mobility data. This research aims to improve traffic predictions during unusual scenarios, enhancing transport planning for various events.
CrowdFlows explores the interactions between online information about public events and mobility data to build predictive models of travel demand under non-routine scenarios.
Such models will be implemented in a state of the art traffic prediction engine, currently being designed at MIT with collaboration from EU institutions.The project addresses the challenges of medium and long-term traffic prediction, which thus far has relied on historical patterns, largely failing in non-routine situations…
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