The goal of this Pole is to use the latest developments in data science to bridge the gap between trade, transportation and their economic and environmental impacts. This multidisciplinary research Pole will use new data science methodologies and tools to leverage trade and transportation data to support decision making in the face of major supply chain challenges (optimization, resilience visibility, climate challenges...) and to support public policy. This cluster will focus on the intermodal transportation and trade ecosystem within the Great Lakes/St. Lawrence Seaway trade corridor. The size and scope of this trade corridor is matched only by the complexity of its multimodal freight systems and the increasing urbanization on both sides of the Canada-U.S. border. This complexity is exacerbated by the lack of data interoperability and effective collaborations among the various stakeholders within and across jurisdictions.
This collaborative network ("hub") will provide stakeholders with a forum for structured dialogue, exchange of expertise, cooperation among key interdepartmental, intergovernmental, public, industry and research interlocutors, and a secure virtual and physical platform for knowledge sharing, skills and talent development.