harm the state’s economy, the Washington State Department of Transportation
(WSDOT), in partnership with Transportation Northwest (TransNow) commissioned
researchers at the University of Washington and Washington State University to
undertake freight resiliency research to:
- Understand how disruptions of the state’s freight corridors change the way
trucking companies and various freight-dependent industries route goods,
- Plan to protect freight-dependent sectors that are at high risk from these disruptive
- Prioritize future transportation investments based on the risk of economic loss to
this research developed the first statewide multimodal freight model for Washington
State. The model is a GIS-based portrayal of the state’s freight highway, arterial, rail,
waterway and intermodal network and can help the state prioritize strategies that protect industries most vulnerable to disruptions.
- Predict how shipments will be re-routed during disruptions, and
- Analyze the level of resiliency in various industry sectors in Washington State
sectors and its dependence on the I-90 corridor, while showing how the state’s diesel
delivery system is highly resilient and isn’t linked to I-90.
WSDOT will be able to evaluate the impact of freight system disruptions on each of
them. This will improve WSDOT’s ability to develop optimal strategies for highway
closures, and prioritize improvements to the system based on the relative impact of the
organization building a state freight model. First, the researchers had to decide on the
level of spatial and temporal detail to include in the statewide GIS freight model. This
decision has significant consequences for data resolution requirements and results.
Including every road in Washington would have created a cumbersome model with a
large number of links that weren’t used. However, in order to analyze routing during a
disruption all possible connections must exist between origin and destination points in the model. While the team initially included only the core freight network in the model,
ultimately all road links were added to create complete network connectivity.
collection for the two case studies was resource intensive. Supply chain data is held by
various stakeholders and typically not listed on public websites, and it isn’t organized by
those stakeholders for use in a freight model. In most cases it’s difficult to assure data
quality. The team learned that the most difficult data to obtain is data on spatially or
temporally variable attributes, such as truck location and volume. So they developed a
method to estimate the importance of transportation links without commodity flow data.
origin and destination (O/D) pair in the state, and the shortest travel-time re-route for
each O/D pair after a disruption. The routing logic in the model is based on accepted
algorithms used by Google Maps and MapQuest. Phase III of the state’s freight
resiliency research was funded by WSDOT and will result in improved truck freight
routing logic for the model in 2011.
and that some supply chains have built flexibility into their operations and are resilient
while others are not, which leads to very different economic consequences. The results
of these case studies significantly contributed to WSDOT’s understanding of goods
movement and vulnerability to disruptions.
implement the research findings and complete the state freight model. In 2009, the
National Cooperative Freight Research Program (NCFRP) funded development of new
methodology to collect and analyze sub-national commodity flow information. This
NCFRP project, funded at $500,000, will be completed in 2010 and provide a mechanism for states to accurately account for corridor-level commodity flows. If funds are available to implement the new methodology in Washington State, the state’s freight
model will use the information to map these existing origin destination commodity flows
onto the freight network, evaluate the number of re-routed commercial vehicles, and their increased reroute distance from any disruption. This will allow WSDOT to develop
prioritized plans for supply chain disruptions, and recommend improvements to the
system based on the economic impact of the disruption.
Goodchild, Anne V., Eric L. Jessup, Edward D. McCormack, Derik Andreoli, S Rose, Chilan Ta and Kelly Pitera. “Development and Analysis of a GIS-Based Statewide Freight Data Flow Network.” (2009).