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qualitatively based on a market segmentation approach? Modal Assignment
Will a logit or other choice model be used? If so, what will
If there are modal assignment components, will they be
be the form of that model and how will its parameters and
validated? If so, how will they be validated?
coefficients be developed?
Flow Unit and Time Period Conversion Model Application
Will the model include a component to covert trip table What are the specific applications of the model? What out-
flow units and time periods prior to assigning those trip ta- puts will be obtained and how will they be used and evaluated?
bles to modal networks, such as converting annual ton flows
to daily truck flows? If so, what will be the form of this con- Performance Measures and Evaluation
version and where will the conversion factors be developed or
obtained? Will the model be used to support performance measures?
What performance measures are being supported? How
will they be developed? How will they be used? How will
Assignment
performance standards or thresholds be established? Will
Will the model include the ability to assign modal trip performance measures be developed that are not supported
tables to modal networks? What assignment process will by the forecasting model?
be used? Will other vehicles using the modal network be
included?
If there are modal assignment components, will they be 8.2 Case Study Minnesota Trunk
validated? If so, how will they be validated? Highway 10 Truck Trip
Forecasting Model
Model Application Background
What are the specific applications of the model? What out- Context
puts will be obtained and how will they be used and evaluated?
The Minnesota Department of Transportation (Mn/DOT)
has identified a system of major highways connecting regional
Performance Measures and Evaluation activity centers within the state and designated those highways
Will the model be used to support performance measures? as the Interregional Corridor System (IRC). Initially, Mn/DOT
What performance measures are being supported? How will chose seven highway corridors to be the focus of an Interre-
they be developed? How will they be used? How will perform- gional Corridor Management Plan. One of those seven is
ance standards or thresholds be established? Will performance Trunk Highway 10 (TH 10) from TH 24 (Clear Lake) to I-35W
measures be developed that are not supported by the forecast- (Mounds View).16 The TH 10 corridor is shown in Figure 8.1.
ing model? highway with trucks? How will these additional The IRC Management Plan process included a comprehen-
users be assigned in conjunction with freight vehicles? sive technical analysis and public involvement process in order
to evaluate existing and future travel conditions, identify defi-
Model Validation ciencies, and weigh the various improvement alternatives.
Current and future truck activity in the TH 10 corridor was
Trip Generation studied through analysis of historical truck data and develop-
ment of a truck traffic forecasting methodology that utilized his-
If there is a trip generation component, will it be validated?
torical truck count data, regional employment data, FHWA
If so, how will it be validated?
truck trip generation rates, and local truck trip-making activity.
The TH 10 study utilized direct flow factoring by applying
Trip Distribution economic activity indicators to project future truck volumes.
If there is a trip distribution component, will it be vali- This methodology is relatively straightforward and readily
dated? If so, how will it be validated? adaptable to other corridors in the Minnesota IRC system.
Mode Choice Objective and Purpose of the Model
If there is a mode choice component, will it be validated? Modal activity assessment is required under Mn/DOT's
If so, how will it be validated? Interregional Corridor Plans. The TH 10 Truck Trip Forecast-
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Source: Minnesota Department of Transportation, TH 10 Corridor Management Plan.
Figure 8.1. Trunk Highway 10 in Minnesota.
ing Model was developed specifically to assess current and fu- alternatives. Current and future truck activity in the TH 10
ture truck travel demand in the TH 10 corridor, but the corridor was studied through analysis of historical truck data
process is applicable to other Minnesota IRC corridors. and development of a truck traffic forecasting methodology
that utilized historical truck count data, regional employment
General Approach data, FHWA truck trip generation rates, and local truck trip-
making activity. This method is appropriate for corridors
Model Class where no network-based truck forecasting models exist.
The TH-10 model is a direct facility flow factoring class of
model. It uses economic variables and existing truck flows to Flow Units
directly factor those flows and produce future truck volumes.
The TH 10 Truck Trip Forecasting Model estimates daily
A detailed description of the direct facility flow factoring class
truck trips in the corridor.
of model is provided in Sections 4.1 and 6.1.
Data
Modes
Forecasting Data
The TH 10 model estimates only truck volumes on the TH
10 highway corridor. BASE AND FORECAST YEAR SOCIOECONOMIC DATA
Historical truck traffic data from 1992 through 1999 were
Markets obtained to estimate the growth trend in truck traffic along
The TH 10 model was specifically built for the TH 10 cor- the TH 10 corridor.
ridor, but the methodology is applicable to other corridors in Socioeconomic data included:
Minnesota.
· Industrial employment projections (19962006) for Cen-
tral Minnesota and the Twin Cities Metropolitan Area
Framework
from the Minnesota Department of Economic Security;
The IRC Management Plan process included a compre- and
hensive technical analysis and public involvement process · Labor projections (19902020) for counties within Central
designed to evaluate existing and future travel conditions, Minnesota and the Twin Cities Metropolitan Area ob-
identify deficiencies, and weight the various improvement tained from the Minnesota Department of Planning.
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The economic forecasts were used to project the number of 2020 truck volumes. Because 2025 was the desired study
of future employees by industrial sector within the corridor year, the 2020 projections were extrapolated to 2025.
study area. By applying the appropriate truck trip generation Using data from private vendors, businesses along or near
rate by sector (truck trips per employee), the associated num- the corridor that generate truck trips were identified and the
ber of trucks was estimated. associated number of future truck trips was estimated. Based
on future employment at these businesses and the adjusted
FHWA truck trip generation rates, the number of truck trips
EXTERNAL MARKETS
associated with each employer were estimated. By geocoding
No external market data was provided. the employment locations and the associated truck trips, high-
way segments with high truck volumes could be identified.
Modal Networks
Software
FREIGHT MODAL NETWORKS
The methodology developed for the TH 10 corridor relied
No travel demand models were used in the TH 10 Truck primarily on spreadsheet calculation (such as Microsoft
Trip Forecasting Model. Excel), GIS software such as Business Map by ESRI, and the
HarrisInfo database of manufacturers.
INTERMODAL TERMINAL DATA
Commodity Groups/Truck Types
No intermodal terminal data was provided.
Trip demand analysis was based on trip generation rates
from the Quick Response Freight Manual for 12 industrial
Model Development Data
sectors. No specific commodity groups or truck types were
No model coefficients or parameters were necessary in the specified.
TH 10 model. The economic forecasts were applied directly
to the existing truck volumes. Trip Generation
Trip generation is not included in the direct flow forecast-
Conversion Data ing model class. However, the TH-10 model used the Quick
No conversion data were necessary in the TH 10 model. All Response Freight Manual trip generation equations to develop
truck data are presented and estimated in daily truck trips. the growth rates to be applied to the truck volumes.
As shown in Table 8.1, appropriate daily truck trip rates
per employee (by sector) were identified using the Manual.
Validation Data To estimate truck trips generated within a county, these
The model uses existing truck counts directly therefore truck trip generation rates were applied to base and future
those truck counts could not also be used for validation. No county employment forecasts by sector.
other independent validation data was available.
Trip Distribution
Model Development Trip distribution is not included in the direct flow forecast-
ing model class. The TH-10 model geocoded the manufactur-
The model process was to gather and review historical truck ing employment along the corridor and applied the Quick
counts in the TH 10 corridor and develop a growth trend pro- Response Freight Manual rates to that location-specific em-
file. Projections of future truck trips were developed based on ployment to develop growth factors for individual sections
regional employment forecasts (year 2020) applied to the of the corridor.
truck trip generation rates from the Federal Highway Admin-
istration's Quick Response Freight Manual. The FHWA's truck
trip generation rates were applied to existing county employ- Commodity Trip Table
ment data to estimate existing truck trips in the corridor. This No commodity trip table was acquired or needed.
estimate was compared to observed truck counts, and the trip
generation rates were adjusted for use in future year trip esti-
Mode Split
mation. The adjusted forecast truck factors were applied to
2020 county employment projections to develop an estimate A mode split model is included in this class of models.
