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  • "Freight System Resiliency"
    Freight system resiliency is the ability of the freight system to adapt to changing conditions and withstand and rapidly recover from disruption due to emergencies. A resilient freight transportation system is responsive and able to provide reliable services when it encounters small disruptions and return to service quickly after large disruptions.
Start Date: January 2019
Funding: Marine Environmental Observation, Prediction and Response Network (MEOPAR)
Principal Investigator(s): Dr. Anne Goodchild
Other PI(s): PI: Stephanie Chang (University of British Columbia)Co-PI: David Bristow (University of Victoria)Co-PI: Floris Goerlandt (Dalhousie University)Co-PI: Ron Pelot (Dalhousie University)Co-PI: Cheng Lin (University of Victoria)Co-PI: Linda Zhou (University of Victoria)
Summary:
A catastrophic earthquake could disrupt marine transportation across coastal British Columbia, severely affecting supply chains to coastal communities and emergency response capabilities. This project seeks to better understand such risks and develop effective resilience strategies for different types of coastal communities. It inquires into how disaster events would likely affect ports, marine transportation routes, and the associated movement of people and resources in the emergency response phase, and what strategies would be effective to alleviate potential consequences.
Start Date: July 2017
Funding: Resource Systems Group; U.S. Department of Transportation - Federal Highway Administration
Principal Investigator(s): Dr. Anne GoodchildDr. Ed McCormack
Summary:
This project develops a national freight forecasting model for the FHWA. The model, the first of its kind at the national level, supports national freight policy making and planning. Urban Freight Lab researchers will identify the most useful and promising structures for a national model and are leading the evaluation of model components and their integration, and developing an approach to test the potential specifications for each model component and are contributing to the development of national sources of data for use in the model. The project will demonstrate the model in a software application.