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OCR for page 12
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2
Multimodal
P&A
File
Trip Distribution
Multimodal
Matrix
3
Figure 4.3. Trip distribution.
such as the freight truck model, the flow in the trip table is The commodity-based freight models use gravity models
expressed in units that are common to all modes. When the for trip distribution. However, rather than being trip-specific,
freight trip table is a multimodal commodity table, it cus- the models are developed and applied for commodity groups
tomarily serves as input to the mode split model component. serving as the purposes for individual tables. Freight flows in
When the table is for a single mode it customarily serves as tonnage and by commodity group are distributed on an O-D
input to the assignment model component. However, the trip basis for an entire state, either at a district, county, or TAZ
tables themselves are useful in analyzing the markets for level. The primary impedance variables are average travel dis-
freight flow between geographic zones. tance, average travel time, or composite modal travel time.
The trip distribution models are used in statewide models The trip distribution component for the Florida Inter-
to forecast the volume of freight shipped between an origin modal Statewide Highway Freight Model described in
and a destination. All the state freight models surveyed use Section 8.9 uses a standard gravity model and distributes
gravity models for distribution. Gravity models distribute tons produced in one zone to tons consumed in another
trips by purpose between origins and destinations, based on zone using friction factors calibrated based on the average
the total tons produced at an origin, attracted to a destina- trip lengths identified from TRANSEARCH. In the Indiana
tion, and the relative impedance, in the form of friction Commodity Transport Model described in Section 8.8,
factors, of traveling between these zones. Gravity models cal- freight shipments are distributed by a gravity model cali-
culate this distribution for each O-D pair by purposes and brated using the CFS data. Special care is taken to match the
adjust the calculations iteratively based on the calculations of average shipping distance per ton for each commodity
all other pairs of the same trip purpose. group. This prevents any inappropriate weighting for many
Truck models use truck types as trip purposes. For the New short-distance lightweight deliveries versus a few long-
Jersey Statewide Truck Model described in Section 8.6, the trip distance heavyweight shipments that might be included in
purposes were light, medium, and heavy trucks. These were the same commodity group.
distributed from origins to destinations using the gravity
model technique, the same method used in any typical 4.4 Mode Split
automobile passenger model. The friction factor curves are
first derived from the Quick Response Freight Manual and later As shown in Figure 4.4, the mode split model component
adjusted to provide the best fit with average trip lengths derived uses a freight trip table, obtained either from the trip distri-
from observed truck survey data. The friction factors were bution or the commodity flow model components, to fore-
developed using the following equations from the manual: cast tables of freight flows between all geographic zones for
individual freight modes. The mode split component also
Light = exp (-0.08 * congested travel time)
requires some information about the relative benefits of the
Medium = exp (-0.10 * congested travel time)
utility of using each freight mode between all geographic
Heavy = exp (-0.03 * congested travel time)
zones. The modal trip tables of freight flow customarily serve
The New Jersey model employs different gravity models as inputs to the assignment model component. If the flow is
for internal, external, and through-trips for both medium not expressed as vehicles, but in flow units common for all
and heavy truck types. These models are calibrated to match modes such as tons, a conversion to vehicles may be made
target distributions based on a combination of observed data prior to using the tables in assignment. However, the trip
for trips in New Jersey where data are available, and data from tables themselves are useful in analyzing the markets for
other cities where local data are unavailable. freight flow between geographic zones by mode.
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3
Multimodal
Matrix
Mode Split
Truck Rail Water Air
Matrix Matrix Matrix Matrix
4 4 4 4
Figure 4.4. Mode split.
Mode split models, as they are used in statewide freight and commodity group to determine the mode split in the fore-
forecasting, convert future flows by all modes into flows by cast year. These base shares are usually not sensitive to factors
specific modes. (By definition the truck model class involves like travel times, travel costs, safety, and reliability. However, in
only a single mode.) A mode split model may use modal some instances mode-specific information from the commod-
shares from the base year commodity data by origin, destina- ity data is used to develop freight mode split models. A detailed
tion, and commodity group to determine the mode split in explanation of these methods is provided in the mode split sec-
the forecast year. These are usually not sensitive to factors like tion of the O-D factoring method (Section 6.2).
travel times, travel costs, safety, and reliability. By identifying The Indiana Commodity Transport Model uses the 1993
specific markets that may have the option to switch modes CFS data to project observed national modal shares into the
based on the distance traveled, the type of commodity, and future. The mode split model in the Florida model is based on
the size of the shipment, it may be possible to qualitatively an incremental logit choice model and historical mode split
adjust mode shares. If modal utility data is available, that percentages. The base year water and air mode splits for each
information can be used together with the base commodity commodity group are assumed to remain unchanged in the
flow data to develop freight mode split models. future. The choice model is applied to the splits between
Commodity flow tonnage is converted to vehicles based on truck, intermodal, and carload rail, which pivot about the
commodity-specific factors (tons per truck or railcar) so that base year percentages:
loaded truck and/or railcar trips can be assigned to the corre-
sponding networks. In most of the models, conversion to air Si exp ( U i )
and waterborne vehicles is not undertaken since assignment Si = J
to air and water networks is typically not performed. How- Sj exp(U j )
I =1
ever, the Texas statewide model does convert barge traffic to
waterborne tons.
where
For the O-D factoring class of models, in the process of fac-
Si = new share of mode i;
toring existing O-D commodity tables by modes, each exist-
Si = original share of mode i; and
ing modal table is often factored separately. Implicitly, this
Ui = utility of mode i in the choice set J (j = 1,2,3,. . .,J).
assumes that existing mode shares for each commodity will
continue in the future. This is not the only option for treat- The coefficients of the utility function were adopted from
ing the split into modes within the O-D factoring class of a study in New York and calibrated to the TRANSEARCH
models. How modal allocation is treated depends on the database for Florida.
modal-specific network information that is available. At the national level, the Vehicle Inventory and Use Survey
If no information is available on the travel times and costs (VIUS) data set provides a large sample that can be used to
for the competing modes, the traditional assumption that determine average payloads by commodity, operating radius,
existing mode shares will continue in the future is appropriate. vehicle size, and type of truck usage. This information is ap-
If qualitative but not quantitative information is available, it is plicable to long trips (greater than 200 miles), since these are
possible to use that qualitative information to change specific typically interstate movements. For shorter trips beginning
mode shares. Market segments of particular commodities, and ending within the state, average payloads should be esti-
O-D pairs by shipping distances, and shipment size may be mated from only those vehicles based in-state. This method
identified and expert opinion used to change the modal share. has been used widely in many statewide and regionwide
A typical commodity-based mode split model uses modal freight models. However, there are some exceptions where
shares from the base year commodity data by origin, destination, the freight tonnage is divided into an equivalent number of
