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Technical Report

Cross Border Transportation Patterns at the Western Cascade Gateway and Trade Corridor: Implications for Mitigating the Impact of Delay on Regional Supply Chains

 
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Publication: Western Washington University Border Policy Research Institute
Publication Date: 2008
Summary:

This report presents a commercial vehicle profile of transportation patterns and a commodity profile of the primary border crossing along the Western Cascade border region of southwest British Columbia, Canada, and northwest Washington, United States, in particular the corridor between the urban areas of Vancouver, British Columbia, and Seattle, Washington.

Because of the larger trade volumes along the eastern portion of the U.S.-Canadian border between Michigan, New York, and Ontario, trade research on the northern U.S. border has typically focused on trade along the eastern portion of the border between Michigan, New York, and Ontario, as well as on immigration and customs issues along the southern border with Mexico. As a result, less attention has been given to the western portion of the U.S./Canada border.

This research begins to fill that gap with both a description of regional trade and a description of current delay patterns, consequences of delay, and causes of delay. Using four data sources for comparison—a Global Positioning System (GPS) freight carrier border delay data set, a commercial volume data set (BC MoT), a detailed border operations survey data set, and manifest sampling (WCOG)—the authors consider the linkages among volume, delay, border operations, commercial vehicle origin/destination, and commodities carried to create a commercial vehicle profile at the Cascade Gateway. The data also allow the authors to demonstrate transportation patterns at this gateway and along the trade corridor, and to show that they are very regional in nature.

This research will benefit both public and private stakeholders who are interested in understanding cross-border commercial vehicle commodity flows and transportation patterns in the Cascade gateway and trade corridor, as well as the profile of delay experienced at the Pacific Highway commercial border crossing. Such an understanding can aid in the development of solutions to mitigate border delay and its impacts.

Authors: Dr. Anne Goodchild, Li Leung, Susan Albrecht
Recommended Citation:
Goodchild, A., Albrecht, S., & Leung, L. (2008). Cross Border Transportation Patterns at the Western Cascade Gateway and Trade Corridor: Implications for Mitigating the Impact of Delay on Regional Supply Chains (Research Report No. 6).
Technical Report

Development and Analysis of a GIS-Based Statewide Freight Data Flow Network

 
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Publication: Washington State Department of Transportation
Publication Date: 2009
Summary:
In the face of many risks of disruptions to our transportation system, this research improves WSDOT’s ability to manage the freight transportation system so that it minimizes the economic consequences of transportation disruptions.
Faced with a high probability that major disruptions to the transportation system will
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
    events, and
  • Prioritize future transportation investments based on the risk of economic loss to
    the state
To accurately predict how companies will route shipments during a disruption,
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.
The report features two case studies showing the model’s capabilities: the potato growing and processing industry was chosen as a representative agricultural sector, and diesel fuel distribution for its importance to all industry sectors. The case studies are found in sections 5.2 and 5.3 in the report and show how the statewide freight model can:
  • Predict how shipments will be re-routed during disruptions, and
  • Analyze the level of resiliency in various industry sectors in Washington State
The two case studies document the fragility of the state’s potato growing and processing
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.
As origin-destination data for other freight-dependent sectors is added to the model,
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
disruption.
This research addressed several technical areas that would need to be resolved by any
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.
Second, as state- and corridor-level commodity flow data is practically non-existent, data
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.

Third, the freight model identified the shortest route, based on travel time, between any
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.
The two case studies showed how the state’s supply chains use infrastructure differently,
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.
In the future, Washington State will need corridor-level commodity flow data to
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.
This report summarizes 1) the results from a thorough review of resilience literature and resilience practices within enterprises and organizations, 2) the development of a GIS-based statewide freight transportation network model, 3) the collection of detailed data regarding two important industries in Washington state, the distribution of potatoes and diesel fuel, and 4) analysis of the response of these industries to specific disruptions to the state transportation network.
The report also includes recommendations for improvements and additions to the GIS model that will further the state’s goals of understanding the relationship between infrastructure availability and economic activity, as well as recommendations for improvements to the statewide freight transportation model so that it can capture additional system complexity.
Authors: Dr. Anne GoodchildDr. Ed McCormack, Eric Jessup, Derik Andreoli, Kelly Pitera, Sunny Rose, Chilan Ta
Recommended Citation:
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).
Student Thesis and Dissertations

Analysis of Intra- and Inter-Industry Trade Flows of U.S. State – Canadian Province Pairs: Implications for the Cost of Border Delay

Publication Date: 2009
Summary:

Intra-industry trade (IIT) occurs when trading partners import and export similar products. A high volume of IIT of horizontally differentiated goods implies a deep level of regional integration, stable regional trading patterns, and potentially significant consequences from border delay. In this paper, trade between Washington State and British Columbia, Canada (the Cascade gateway), is compared with trade between Michigan State and Ontario, Canada (the Great Lakes gateway). The Grubel–Lloyd index, which measures IIT, is used to analyze trade in these two corridors. Higher levels of IIT and regional integration within the Great Lakes gateway are shown. The paper argues that cross-border supply chains most exposed to higher cost from increasing border delays are composed of horizontally differentiated manufactured goods having high levels of IIT and relying heavily on truck transportation. These types of goods are more common in the Great Lakes gateway, and this region may therefore experience greater economic impacts from long and unpredictable delays than the Cascade gateway.

The value of trade between the United States and Canada is the highest of that between any two countries worldwide, and Canada is the largest foreign trading partner for 37 of the 50 U.S. states (1, 2). The border between the countries is 5,525 mi, making it the longest common border in the world, with 12 U.S. states bordering seven Canadian provinces (3). The commodities traded in different parts of this border are varied, and so is the nature of that trade. Most of this trade—almost 63% of the total value and 35% of the weight— is moved by trucks, which are often subject to long and unpredictable delays at the border crossings (4). This paper uses the Grubel–Lloyd (GL) index, a measure of intra-industry trade (IIT), to describe the nature of the trade along the U.S.–Canada border and its relation to trade corridors. It is argued that increasing delay for roadway vehicles crossing the borders has different impacts on intra-industry versus interindustry trade and that knowledge of these impacts should be considered in evaluating potential policy solutions to addressing border congestion.

Authors: Kristján Kristjánsson
Recommended Citation:
Kristjánsson, Kristján Árni. "Analysis of Intra-and Inter-industry Trade Flows of US State-Canadian Province Pairs: Implications for the Cost of Border Delay." PhD diss., University of Washington, 2009.
Thesis: Array