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Table 8.44. Major statewide screenline volume/count ratio.
Screenline Model Volume Observed Count Volume/Count
North Central Statewide 26,559 30,016 0.88
Southeast Statewide 24,724 24,696 1.00
Table 8.45. RMSE summary for intercity freeways.
All Volume Groups 34.83%
Volume Group Great Than 5,000 Trucks 17.60%
Volume Group Less Than 5,000 Trucks 37.98%
· The existing and forecast table of daily truck trips derived decisions and produce interregional forecasts across the full
from the O-D table of annual tonnage by truck for 14 spe- length of the Cross-Cascades Corridor, from Seattle to
cific commodities; and Spokane, across all modes.
· The existing and forecast daily volumes of trucks moving The Cross-Cascades Corridor analysis focused on trans-
on the Florida highway system through assignment of the portation systems and the Washington economy, and
truck table to the highway network. provided a tool for forecasting passenger and freight trans-
portation demand from population and employment forecasts
and to use the transportation forecast demand to modify those
Performance Measures and Evaluation
population and employment forecasts in an iterative process.
Not developed in FISHFM. As shown in Figure 8.19, this project covered two east-west
highways (I-90 and SR 2), two railroad lines (the Burlington
8.10 Case Study Cross-Cascades Northern Santa Fe routes across Stampede Pass and Stevens
Corridor Analysis Project Pass), and the airways between Seattle and Spokane. This mod-
eling effort could signal a new approach to corridor and
Background statewide modeling across the state.
Context
Objective and Purpose of the Model
Washington State depends heavily on trade for its eco-
nomic well-being. Home to just 2% of the nation's popula- The purpose of the Cross-Cascades Corridor analysis was
tion, the state accounts for 7% of the nation's exports. As a to examine interregional passenger and freight travel between
result, Washington's economy is directly linked to its ability Seattle and Spokane and to construct a forecasting tool that
to move freight through its many ports. could be used in future corridor studies. WSDOT sought a
A number of organizations are responsible for freight tool that would:
mobility in Washington, most notably the Freight Mobility
Advisory Committee (FMAC) and the Washington State · Produce interregional passenger and freight forecasts and
Department of Transportation (WSDOT). The FMAC, cre- analysis;
ated in 1996 by the Legislative Transportation Committee, is · Integrate output from other models;
a 23-member body whose purpose is to advise the Washing- · Be transferable and expandable to other corridors;
ton State Legislature on freight issues. WSDOT's freight man- · Provide six-year and 20-year forecasts;
date was established in 1998, when the Legislature directed · Consider alternative modes of travel; and
the agency to focus on five primary goals, one of which was · Offer visual appeal and a user-friendly format.
freight mobility. The Legislature sought to ensure reliable
freight movement and transportation investments that sup- Today, WSDOT uses the Cross-Cascades model to test
ported Washington's strategic trade advantage. In January how corridor transportation system changes can affect mode
2001, the WSDOT reached an agreement with MPOs across choice, route choice, and travel time performance, and to
the state to develop a new planning and forecasting model forecast demands and analyze issues statewide. The model
that would integrate economic, land use, and transportation can be interfaced with urban models used in metropolitan
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Source: ESRI 2002, prepared by Cambridge Systematics, Inc.
Figure 8.19. Washington state counties and roadways.
areas. For MPO planning purposes, the Cross-Cascades A detailed description of the economic activity class of
model provides accurate external trips that pass through the model is provided in Section 6.5.
metropolitan areas along the corridor. For regional planning
purposes, the model provides detailed analysis of statewide
Modes
freight activity.
The modes available to make freight trips and shipments
include:
General Approach
· Air freight;
Model Class
· Rail freight;
The Cross-Cascades model is an economic class of model. · Heavy truck freight; and
The modeling approach selected in this case is known gener- · Medium truck freight.
ally as a spatial I-O model. It distributes household and eco-
nomic activity across zones, and uses links and nodes of a As an integrated passenger and freight model the following
transportation network to connect the zones and model the passenger modes also are included:
transportation system before calculating transportation flows
on the network. The location of households and economic · Air passenger;
activities can be thought of as the land use component of the · Amtrak (rail passenger);
model. · Coach (bus passenger);
The basic methodology allows the model to produce fore- · Private auto; and
casts of: · Work auto.
· Traffic volume assignments;
Markets
· Mode split;
· Population (household); and The Cross-Cascades model is intended to provide an
· Employment. analysis of general transportation and investment demand in
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the corridor and to prove the concept of an integrated spa- household data. Total households by zone were divided into
tial I-O model. While all passenger and freight activity is four income groups based on data from the 1990 Census.
calculated for 10 economic sectors and four ranges of house-
hold income, the level of geographic detail is limited. The EMPLOYMENT DATA
model uses 61 zones, 54 in Washington, 1 in Idaho, and 6
external. Washington and Idaho zones were generally County-level 1998 employment data by major industry
organized by county boundaries. Seven counties within the sector were developed from covered employment data
corridor were further subdivided into 2 to 4 zones, primarily and adjusted by industry to reflect total employment. The
in the Puget Sound area. MEPLAN model requires employment by workplace location.
BEA data could not be used directly because they are based on
place of residence. Hence, BEA data on total employment by
Framework industry and Labor Market Economic Analysis (LMEA) stud-
The modeling approach is known as a "spatial input-output ies of covered and noncovered employment were used in-
model" because it considers not only the level of transportation stead. Total employment by industry by county was allocated
and economic activities, but also their interaction and spatial to subcounty, with zones based on 1990 Census data.
distribution across the state. The approach combines the
disciplines of land use analysis, economic analysis, and trans- MEPLAN MODEL COEFFICIENT
portation planning process, as shown in Figure 8.20.
Washington State's economic activity reflects through
the MEPLAN model coefficient. The model coefficient in
Flow Units MEPLAN is defined as the amount of each type of employee
and household activity required to produce a single unit of
The Cross-Cascades Corridor model produces average economic activity for a certain industrial or household sector.
weekday passenger and freight vehicle volumes on the corri- These coefficients translate the industry and household num-
dor's transportation system. The model also produces mode bers to trips on the transport network.
splits for freight by highway, rail, and water. The intermedi- The data is provided for an internal zone structure that
ate results of the model produce economic activity (expressed includes:
in dollars) which can be converted to tonnage or vehicles.
· Twenty-five subcounty zones within the corridor (24 in
Data Washington and one in Idaho); and
· Thirty other county-level zones in Washington.
Forecasting Data
HOUSEHOLD DATA
The external markets for the Cross-Cascades model con-
sist of the following six external zones:
County-level 1998 household data were developed from
county population and household size statistics from the Wash- 1. Western Canada;
ington State Population Survey. County-level households were 2. Canada, East of Cascades;
split into smaller subcounty zones using 1990 U.S. Census tract 3. Northern Idaho, Montana, and East;
Land Use Transport
Land Use Demand for
Transport Travel/Freight
(Activities)
Prices Demand Time/Cost Demand
Land/Floor
Accessibilities Transport Systems
Space
Source: Cross-Cascades Corridor Analysis Project Sumary Report, Washington State Department of Transportation, 2001.
Figure 8.20. The Cross-Cascades Corridor spatial input-output approach.
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4. Eastern Oregon, Southern Idaho, and Southwest; while special links interface between highway, rail, air, and
5. West Oregon, California; and transit routes.
6. Non-United States
· The highway network data are derived primarily from the
As shown in Figure 8.21, three of the external zones are in WSDOT Travel Delay Methodology and the nodes as
the United States, two are in Canada, and one is overseas. All defined in the WSDOT's EMME/2 transportation net-
trip types considered in the model's internal zones are also work.26 Rail, air, and transit networks are based on national
forecast for these external zones. The model developers chose or carrier-specific data. The Cross-Cascades model used a
not to include eastern portions of North America based on variety of sources for additional data including: travel delay
their understanding of study area trade patterns. methodology highway link AADT (and truck percentage);
synthesized highway O-D from Washington traffic counts;
Modal Networks Washington State Freight Rail Study 1996 rail ton-
miles/mile by rail segment; and MPO congested travel
FREIGHT MODAL NETWORKS
times between their external zones.
The transportation network in the Cross-Cascades Corri-
dor model includes all Washington highways of statewide INTERMODAL TERMINAL DATA
significance, the Burlington Northern Santa Fe (BNSF) rail
lines across Stevens Pass and Stampede Pass, and the airways Truck, rail, and air freight terminals are explicitly coded
connecting Seattle, Wenatchee, Yakima, Moses Lake, the and included in the assignment and path identification
Tri-Cities area, and Spokane. Each of these networks also process. The use of multimodal paths through intermodal
includes connections to external zones. The road network connectors between the various model systems allows the
within the corridor was modeled in more detail than the inclusion of terminal transfer costs (parking and freight han-
remainder of the state. Highways and rail lines are described dling, see costs). Nodes in the transportation component of
in terms of links and nodes. Each link has assigned attributes the Cross-Cascades model include attributes of geographic
of length, speed, capacity, and toll charges, if applicable. location and connections for not only highway and rail nodes
Centroid connectors link the zones to the transport network, but also nodes with special identifier codes for airports, truck
terminals, and ports.
Model Development Data
Data sources utilized for freight model development and
calibration are shown below. Calibrations are primarily
focused on trip length and mode split data.
· 1997 Reebie TRANSEARCH O-D flows (tons);
· 1997 U.S. CFS Washington State Internal-External
(I-E)/Interstate (I-I) tons and trip lengths;
· 1995 Eastern Washington Intermodal Transportation
Study (EWITS) Internal-External Truck tons;
· 1996 Washington Freight Rail Study through (E-E)/E-I
tons; and
· Washington Airport Activity Statistics Cargo tonnage
enplaned/deplaned.
Other types of calibration data include O-D trip tables, link
volumes, and elasticity.
Conversion Data
Source: External Zones, Cross-Cascades Corridor Analysis Project Summary
Report. Washington State Department of Transportation, 2001.
The model converts annual tonnage to trucks trips using
Figure 8.21. External zones. load factors expressed as tons per vehicle. Heavy truck load
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factors were derived from the EWITS and FAST Trucks Model Development
weight classification by commodity combined with Reebie
Associates commodities and flow. Light and medium truck The model development effort, shown in Figure 8.22, was
load factors were derived by assuming an average cargo vol- initiated in January 2001 by WSDOT and MPO modelers for
ume of 100, 60, and 15 cubic yards for heavy, medium, and both the Cross-Cascades and I-15 corridors. This approach is
light trucks, respectively. generally known as a spatial I-O model. It distributes house-
hold and economic activity across zones, and uses links and
nodes of a transportation network to connect the zones and
Validation Data model the transportation system before calculating trans-
portation flows on the network. The model components that
Only minimal calibration and validation were possible forecast the location of households and economic activity are
within the Cross-Cascades project scope. Thus, the objective similar to the land use component of integrated transporta-
of the calibration/validation process, particularly as applied tion and land use models used in urban passenger modeling.
to a real-world example of the Cross-Cascades Corridor, was
to make initial model runs and understand the major issues
Software
of the model that would point to recommended next steps
regarding available target data, model parameters, and short- MEPLAN software, developed and distributed by ME&P
comings in model assumptions and structure. of Cambridge, England, is used to run the model.27 MEPLAN
Select Model
Approach
Designate
Traffic Analysis
Zones
Process Amtrak and
Intercity Ridership
Data
Build Highway, Test/Calibrate
Railroad, Air Model
Networks
Retrieve Results for
Build in ArcView Travel Time/Assignment,
to View Output Modal Split, etc.
Insert Improve Visual
Economic Data Output Display
Prepare Model
User's Manual and
Documentation Reference
Process Traffic
Count, Freight
Movement, and
Ridership Data
Run Model for
Highway Modes
Prepare Origin-
Destination Matrix
Source: Cross-Cascades Corridor Analysis Project Summary Report, Washington State Department of
Transportation, 2001.
Figure 8.22. Cross-Cascades Corridor model development review process.
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is based on the concept that, at any geographic level, land Output of the model includes:
use and transport affect one another. The location of
households in turn create demands for industrial land, · Average daily traffic volumes for the average weekday for
retail floorspace, and housing. The relationship of the sup- the corridor;
ply of land to the demand for development influences prices · Mode splits between highway, rail, intercity bus, and air for
for space in each location, and that pattern of prices in turn the corridor; and
influences where people choose to live and work. In addi- · Future employment allocation by industry and zone.
tion the mobility and access provided by transportation also
affects the demand and location of residents, employers,
Commodity Groups/Truck Types
and new developments.
The three major components of MEPLAN are as follows: Exogenous production is production related to sales
exported outside of the economic model area. Exogenous
1. The land use model component, processing economic and production is one of the inputs in the MEPLAN model, and
household data, including the I-O table and generating is shown by industry in Washington State in Table 8.46.
output data;
2. The transport assignment model, containing transporta-
Trip Generation
tion network and flow information; and
3. The interface model, relating land use and economic vol- The Cross-Cascades Corridor (see Figure 8.23) model as
umes. implemented in MEPLAN, uses an I-O structure of the econ-
omy to simulate economic transactions that generate trans-
Key outputs generated by MEPLAN include: portation activity. A spatial input/output model identifies
economic relationships between origins and destinations. For
· Land use and economic outputs, in terms of zonal charac- future years, the spatial allocation of economic activity, and
teristics (employment and households); thus trip flows, is influenced by the attributes of the transport
· Transportation volumes including O-D transportation network in previous years.
flow volumes, network link volumes, congested travel Together, the land use/economic components and the
times, network data, and other statistics; and location of the transportation network affect transportation
· Interface model including disutility (costs) of transporta- flows. Transportation cost, including the cost of congestion
tion between zone and pairs, flow volumes, and evaluation created by increasing travel demands, also influences the
statistics. location of households and businesses.
Table 8.46. Exogenous production by factor.
Groups Total Exogenous Percent Exogenous
Agriculture 122,398 97,432 80%
Mining 3,380 282 8%
Construction 155,869 42,289 27%
Manufacturing 407,455 185,695 46%
TCPU 145,334 59,150 41%
Wholesale Trade 163,227 15,759 10%
Retail Trade 506,920 28,023 6%
FIRE 143,288 47,205 33%
Services 761,001 233,870 31%
Government 501,340 229,043 46%
$0-15,000 Household Income 640,496 340,219 53%
$30,000-50,000 Household Income 544,471 127,394 23%
$50,000+ Household Income 595,022 54,754 9%
Imports 1,660
Source: Cross-Cascades Corridor Study Model Development Peer Review Session, June 1, 2001.
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Economy and Land Use Trip Generation Transportation Component
Distribution
· Structure of the economy · Network
· Location of the activity · Costs
Transportation · Mode Split
Availability and Cost · Trip Assignment
Source: Special Input-Outputs, Cross-Cascades Corridor Analysis Project, Summary Report, Washington State Department of
Transportation, 2001.
Figure 8.23. Trip generation and distribution structure.
The model is driven by exogenous economic activity gen- · STCC commodities 19 to 41 were produced by Manufac-
erated by exports and non-wage-based household income. turing;
It uses an iterative process to forecast the study area econ- · STCC commodities 42 to 50 were produced by Trans-
omy and transportation demands. By making alternative portation Communications and Public Utilities;
assumptions about economic growth, the transportation · Wholesale and retail goods production was assumed to be
network or travel demands, the model can evaluate the eco- 464 tons per employee (the average of the above industries);
nomic/land use and transport impact of various policy · External to internal truck trips were assumed to generate
choices. 2,116 tons/$1.0 million of imports as forecast by MEPLAN;
Trade-to-trip ratios translate economic activity and house- and
hold units into transportation flows in the form of trips and · Through truck tips assumed to generate 322 tons/$1.0 mil-
tons of freight. The rates were developed primarily using lion of IMPLAN imports.
Nationwide Personal Transportation Survey (NPTS) travel
data and Reebie Associates freight data and are provided as Using these classifications and the combined
inputs to the model.
TRANSEARCH/EWITS data for intrastate and internal-
external traffic, tons of each value to weight transport flow
INDUSTRY-BASED TRANSPORT FLOWS category were defined for these four industries. These tons
Trip rates for industry transport flows used Reebie Associ- were divided by the Washington Labor Market Economic
ates and East Washington Intermodal Transportation Study Analysis employment in each industry to generate tons pro-
flow data for through trips combined with Washington State duced per employee.
employment levels by industry. The following assumptions A key feature of MEPLAN is the ability of the transport
were made as supported by Table 8.47. model to provide feedback to the land use model. The trans-
port model generates travel disutility (costs) for each zone
· STCC commodities one to nine were produced by Agricul- pair that in turn influences business and household location
ture Forestry and Fishing industries; decisions. In future year iterations of the model, a nested logit
· STCC commodities 10 to 14 were produced by the Mining model is used to determine the location of business and hous-
industry; ing changes in response to these travel costs.
Table 8.47. Freight trip rates 1995 U.S. National Personal Transportation Survey.
Transportation
Communications,
Agriculture Mining Manufacturing Public Utilities
1997 I-E/I-I Tons 1 2 3 4
Value/Weight Low 9,265,423 10,820,524 77,089,686 35,423,068
Medium 203,008 0 49,939,463 3,877,568
High 0 0 5,586,221 43,346,426
1998 Employees 122,398 3,380 407,455 145,334
Tons/Employee 77.36 3,201.40 325.45 270.73
Source: Federal Highway Administration, 1995 Nationwide Personal Transportation Survey.
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Trip Distribution The modes available for these passenger trips include:
Under the Cross-Cascades Corridor model structure, trip · Air passenger;
generation, and distribution are handled together as described · Amtrak (rail passenger);
above. · Coach (bus passenger);
· Private auto; and
· Work auto.
Commodity Trip Table
Not applicable. Commodity tables, TRANSEARCH and Transportation volumes for each mode and link were
others were used indirectly to support the development of determined by first calculating the desired flows that result
model parameters. from the economic transactions and then assigning them to
modes and routes. In the Cross-Cascades model, mode choice
is calculated based on monetary values of time, distance, and
cost. The mode split disutility function structure and coeffi-
Mode Split
cients are defined with cost functions. Costs (disutility) are
The freight transport flows defined by the model include: related to mode choice through a nested logit function with
linear utility. The function distributes trips stochastically
· Three freight flows (low, medium, and high value-to- rather than assigning all trips to the least cost route.
weight); and There are two types of cost functions: passenger and
· Two external truck trip types (external-external and freight. In this section freight cost functions will be discussed.
external-internal).
FREIGHT COST FUNCTIONS
Modes available to make these freight trips and shipments
Freight costs were assumed to consist of a distance-based
include:
charge (paid by the shipper to the carrier), a time cost, and a
terminal handling fee. A range of distance (per ton-mile)
· Air freight; costs was assumed as follows:
· Rail freight;
· Heavy truck freight; and · $18.80/hour for passenger drivers; and
· Medium truck freight. · $16.50/hour for commercial drivers.
In addition the passenger component of the model Terminal handling costs use the distance-based rates and as-
includes sume a $75 fee for a local (20-mile) medium truck trip. This re-
sults in a terminal handling cost of $20.50 for medium trucks.
· Four personal passenger trip categories (commuter, shop- The handling cost is increased by 25% for heavy trucks. Rail
ping, visit friends and relatives, and recreation/other); and handling fees are calculated assuming that medium truck and
· Two business passenger trip categories (services and busi- rail trips are competitive for distances over 250 miles. The han-
ness promotion). dling costs used in the model are shown in Table 8.48.
Table 8.48. Freight rate function.
Distance Rate Range
(including Dollars/Ton-Mile
Mode Terminal Cost terminal cost) Assumed
Work Drive
Light Truck $0 $0.04-$0.10/ton-mile $0.10
Medium Truck $20.50 $1.25-2.50/mile $0.08
Heavy Truck $25.63 $0.10
Rail Freight $37.50 $0.02-$0.04/ton-mile $0.03
$2.20-2.73/mile
Air Freight $70.00 $4.90-7.50/ton-mile $3.00
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Flow Unit and Time Period Conversion and by mode split by flow. Passenger targets were derived
from a weighting of ATS (trips greater than 100 miles) and
Truck load factors are used to convert tons to truck trips as
NPTS data (all trips) for Washington State, while freight
shown in Table 8.49.
targets relied on the Washington State Reebie Associates
freight data.
Assignment
The Cross-Cascades model handles mode and route choice Model Application
simultaneously in a manner that distributes trips stochasti- The model has not yet been applied.
cally rather than assigning all trips to the least cost route.
Freight and passenger trips also are handled simultaneously.
Performance Measures and Evaluation
The model was tested by running four hypothetical scenar-
Model Validation
ios designed to demonstrate its various capabilities and out-
A formal process was adopted for calibration of the Cross- puts. The results of the scenarios form initial validation of the
Cascades model. Various data items were identified as targets predictive capability of the model. The scenario results are
and an algorithmic process was used to adjust parameters to useful primarily for demonstration purposes until additional
attempt to meet those targets. This process identified param- base year calibration can be completed.
eter values, and provided a framework for investigating lack- In testing the model each of the scenarios was evaluated by
of-fit and guidance in changes to the model assumptions and comparing impact on:
model structure.
A set of targets of historical observations for the Cross- · Employment by zone;
Cascades Corridor were collected for calibration. The targets · Household by zone; and
are generally transportation demand-related, describing the · Traffic volumes on I-90 and SR 2.
volume of travel by different modes over different distances
or origin-destination pairs. The collected targets span the The conclusion of the scenario testing found that the
following types of data: model is working and responds to the proposed scenario
policy questions in its predictions of future economic and
· Trip length distributions; travel activity.
· Mode splits; However, like most of the states, the nature of freight in the
· O-D trip tables; state of Washington is complex, and the Cross-Cascades Cor-
· Demand elasticity; and ridor model might not cover all the issues. In freight models,
· Road or station counts. logistics and fares of freight travel, intermodal connections,
and port activities need to be considered carefully. More
MEPLAN calibration software was used to calibrate the direct representation of the various freight movements,
model. The base year Cross-Cascades Corridor MEPLAN rather than average cost and shipment size, can be made by
model calibration efforts were intended to match passenger using a statistical distribution to more accurately reflect
and freight targets of average trip lengths by flow and mode, actual freight diversity.
Table 8.49. Truck load factors.
User Mode Flow Tons/Vehicle
Light Truck Mid value-to-weight 3.60
High value-to-weight 3.41
Medium Truck Low value-to-weight 15.50
Mid value-to-weight 14.41
High value-to-weight 13.64
Heavy Truck Low value-to-weight 25.92
Mid value-to-weight 24.02
Freight Truck Low value-to-weight 75.95
Mid value-to-weight 68.23
