Office of Operations Freight Management and Operations

Quick Response Freight Manual II

4.0 Incorporating Freight into “Four-Step” Travel Forecasting

This section explains the various methods of incorporating freight into the traditional “four-step” travel forecasting process. The four steps include trip generation, trip distribution, mode choice, and trip assignment. These are explained in more detail in the ensuing sections. The focus will be at three levels of geography – urban, statewide, and site specific.

4.1 Introduction

The flow of freight can be measured in two forms – commodity and trucks. Figure 4.1 depicts the four steps to forecasting freight at any geographic level. As indicated in the initial steps, trip generation and distribution can either be in the form of commodities or trucks. The basic difference between commodity- and truck-based models is the form of the input data. However, for trip assignment purposes all forms of freight are converted to vehicles to be assigned onto a roadway network.

Figure 4.1 "Four-Step" Process of Freight Forecasting

Figure 4.1 depicts the four steps to forecasting freight at any geographic level.  The picture shows a flowchart starting with the Trip Generation process, which yields total tons. This is followed by the trip Distribution process, which assigns the tonnage to specific OD pairs. The third step is the Mode Split which assigns the tonnage to all available modes (truck, rail, barge, air for freight moves, or private auto, bus, subway, etc., for passengers). Finally these flows are assigned to a network in the last step.

The following subsections discuss the general issues of incorporating freight into tradition four-step transportation models, a topic that is discussed in detail in later sections as it applies to urban and state models.

4.1.1  Trip Generation

Trip generation uses economic variables to forecast freight flows/vehicle flows to and from a geographic area using equations. The trip generation equations are either borrowed from other sources or developed locally by using an existing commodity flow table or by estimating from vehicle surveys.  The outcome of trip generation is the amount of a commodity and/or the number of vehicles that comes into or goes from a particular geographic unit in a specified unit of time.

Trip generation models used in freight forecasting include a set of annual or daily trip generation rates or equations by commodity. These rates or equations are used to determine the annual or daily commodity flows originating or terminating in geographic zones as a function of zonal or county population and/or industry sector employment data. In other words, employment and/or population data are the essential input data required for computing freight trip generation.

The independent variables, such as employment and population, usually dictate the level of detail the freight flows can be generated using a trip generation model. This may be a county or a traffic analysis zone (TAZ). The travel demand models usually use TAZ data, and so a freight forecasting model can be developed at a TAZ level as long as the base and forecast year data at the required level of industry detail is available at that geographic unit.

Before trip generation models are estimated, trucks are first classified by type of truck and/or trip purpose/sector. The various types of classification of trucks include the FHWA classification system, gross vehicle weight (GVW) ratings, type of goods carried, number of tires/axles, and body type.

Normally, one set of regression equations for the productions and one set of regression equations for consumption are estimated. These regression equations are either developed for each commodity group or truck type. A commodity group is analogous to a “trip purpose” in passenger modeling. The intercept is almost always forced to zero, because there should be no freight in or out of a zone with no related economic activity. The observations used to estimate the regression model would be the inbound tons of the commodity or number of trucks and the independent variables are usually employment, industry type, population, etc. for each geographic area.

Truck trip generation rates can be developed from trip diary surveys using regression equations by regressing the number of commercial vehicles on the number of employees in various industries and household population. Trip rates also can be estimated for each individual land-use type based on the ratio between the truck trips coming into and going out of the land area and the employment associated with that land use. The 1996 Quick Response Freight Manual (QRFM) was developed by the FHWA and it provides default values that can be used in models. The QRFM rates were developed using regression models developed from a trip diary in Phoenix. The NCHRP Truck Trip Generation Synthesis (298) is another source for a complete reference list of potential trip rates.

The various steps required to determine trip rates are:

  1. Trip rates need to be estimated or identified (either through local surveys or using national default data);
  2. Socioeconomic data (employment by industry and households/population) by TAZ is applied to the rates to get generation by TAZ;
  3. The QRFM method assumes that productions equal attractions, but local data can be used to estimate separate production and attraction rates; and
  4. If there are freight centers (ports, intermodal terminals), they should be treated as special generators and have their own trip rates determined from surveys since employment rates would not apply.

Table 4.1 is borrowed from the Phoenix Metropolitan Urban Truck Model.  [Earl Ruiter; Cambridge Systematics, Inc.; Development of an Urban Truck Travel Model for the Phoenix Metropolitan Area; February 1992; Report Number FHWA-AZ92-314; prepared for Arizona Department of Transportation and the Federal Highway Administration.] There are far more four-tire truck trips per unit of activity than combination and large-truck trips, which is pretty typical in an urban area. It should be noted that households also do generate a lot of truck trips.

Table 4.1 Truck Trips Rates

Generation Variable
(Employment or Households)

Four-Tire Trucks

Single Unit Trucks
(6+ Tires)

Combination Trucks

Agriculture, Mining, and Construction

1.110

0.289

0.174

Manufacturing, Transportation/
Communications/Utilities, and Wholesale

0.938

0.242

0.104

Retail Trade

0.888

0.253

0.065

Office and Services

0.437

0.068

0.009

Households

0.251

0.099

0.038

4.1.2  Trip Distribution

In trip distribution, one determines the flow linkages between origin and destination for those commodity tons/truck trips that were developed in trip generation. Trip distribution uses those flows/trips to and from and independent variables on the transportation system to forecast the flows/trip interchanges between geography areas.

The trip distribution equations can be borrowed from other sources or developed locally by using an existing commodity flow table or local vehicle surveys. A gravity model can be constructed and calibrated at a prespecified geographic detail. The gravity model is a statistical process that has been found useful to explain the relationship between transportation zones. The considerations are the total trips that begin in the first zone, the number ending in the second zone, and the impedance or difficulty to travel (such as cost or time) between them.

The average trip lengths needed to obtain trip-length frequency distributions and friction factors are normally obtained from surveys. The degree of difficulty of travel, usually a function of some impedance variable used in the distribution model needs to match the survey data (free flow time, congested travel time) and there must be a source of the impedance variable. The calculation of the degree of difficulty is often called a friction factor. With limited survey data, the model is typically calibrated at the district level, and the friction factors developed are assumed to apply at smaller units of geography. However, it is sometimes difficult to get survey data for trip distribution, and friction factors are often borrowed from other sources.

The friction-factors are usually calculated as a negative exponential function of the average trip time from origin TAZ to destination TAZ. The parameters in the exponential function are calculated from the trip length frequency distribution, which describes the shape of the curve that is summarized by the average trip length.

The friction factor curves for the PSRC truck model [Cambridge Systematics, PSRC Model Improvements, 2002] were derived originally from the 1996 edition of the (QRFM [Cambridge Systematics, Inc., Quick Response Freight Manual, Federal Highway Administration, 1996] and adjusted to provide the best fit with the average trip lengths from the origin-destination survey of trucks. The light, medium, and heavy trucks are distributed from origins to destinations using this gravity model technique with different parameters. These friction factors were developed using impedance functions that also varied by trip distances, that is different parameters were used for short and long distances, as shown below:

  • Light impedance function:
    • exp (3.75 – 0.08 * light truck generalized cost skim) for less than 26 miles
    • exp (2.1 – 0.005 * light truck generalized cost skim) for greater than or equal to 26 miles
  • Medium impedance function:
    • exp (4.75 – 0.05 * medium truck generalized cost skim) for less than 27 miles
    • exp (4.2 – 0.003 * medium truck generalized cost skim) for greater than or equal to 27 miles
  • Heavy impedance function:
    • 1.0 for less than 7.5 miles
    • exp (5.0 – 0.009 * heavy truck generalized cost skim) for greater than or equal to 7.5 miles

Table 4.2 shows the average trip lengths from the PSRC truck model compared against the observed trip lengths.

Table 4.2 Average Truck Trip Lengths


Trip Length

Light Truck

Medium Truck

Heavy Truck

Observed Trip Length (Miles)

No data

27.51

30.81

Modeled Trip Length (Miles)

22.34

27.53

28.29

Another method that is less popular is the growth factor approach for trip distribution, also known as the Fratar method. This usually requires an existing base year trip table of freight flows or trip interchanges. The Fratar method assumes that the change in the number of trips in an O-D pair is directly proportional to the change in the number of trips in the origin and destination. The method lacks system sensitivity to the change in network-level characteristics such as congestion. Also, these methods allow preservation of observations as much as is consistent with information available on growth rates. If part of the base year matrix is unobserved, then this error is carried over in the forecasts. These methods cannot be used to fill in unobserved cells of partially observed trip matrices. Hence, they are of limited use to test new policy options.

4.1.3  Mode Split/Conversion to Vehicle Flows

Mode choice modeling is used if multimodal trip tables need to be prepared. This step allows the forecastability of mode splits as they change over time. The four major categories in which various factors that affect mode choice decision-making process fall into are:

  1. Goods Characteristics – These include physical characteristics of goods such as the type of commodity, the size of the shipments, and the value of the goods;
  2. Modal Characteristics – Speed of the mode, mode reliability, and the capacity;
  3. Total Logistics Cost – Inventory costs, loss and damage costs, and service reliability costs; and
  4. Overall Logistics Characteristics – Length of haul and the shipment frequency.

Figure 4.2 shows the major characteristics of each of the freight modes in a continuum/spectrum and shows how this relates to the types of goods that may be shipped by each mode. The rail and water modes have the highest capacity on this spectrum, while air and truck have the lowest capacity. The air and truck modes provide the highest level of service in terms of reliability and minimal loss and damage. So commodities that are needed for just-in-time production systems (like certain machinery parts) will need to use trucking and air. The mode associated with the highest cost is by air and, therefore, are only justified for high-value commodities such as electronics.

Figure 4.2 Goods and Modal Characteristics

Figure 4.2 shows the major characteristics of each of the freight modes in a continuum/spectrum and shows how this relates to the types of goods that may be shipped by each mode.  The spectrum is organized from left to right by modes with the highest service cost.  1) The first mode on the left is Air, which costs anywhere from $1 to $10,000 per pound of freight moved. Air freight is considered to be the fastest way to transfer goods, and the most reliable and visible. It is generally used for items with low weight and high value, and those which are time-sensitive.  2) Truck and Rail make up the second and third modes in the spectrum, with costs ranging generally from 3¢ to 10¢ per pound of freight. These modes are considered fast, reliable, and visible (with truck topping rail slightly on each of these), and are used for commodities with a wide range of weight and value.  3) Water is the last mode in the spectrum, with costs ranging from one-half to one cent per pound of freight. Barge moves are slower, and less visible and reliable than all other three modes, and is generally used for high weight/low value freight that is usually less time sensitive.

The two common methods of computing mode splits are the market segmentation method and the choice method. The market segmentation method is described in detail later in this section.

Choice Method

These methods are the most comprehensive as they examine the characteristics of each individual shipment and the available modes. The most common type of choice method is the discrete choice logit model. This formulation is very similar to the passenger mode choice modeling, but the variables and data sets used to estimate the parameters are very different. The logit discrete choice model shows the choices for individual shipments as a function of the utility that each mode provides to the shipper. Utility can be a function of any of the factors mentioned earlier in this section.

The logit model actually calculates the probability that each shipment will use a particular mode. Summing the probabilities across all of the shipments provides the overall mode share. Each modal alternative has a utility to the shipper that has a systematic component related to the factors we have described earlier and a random component that has to do with things like personal relationships. The coefficients in the utility function measure the relative importance of each factor in determining mode choice. The greater the utility that any alternative has, the higher the probability that this alternative will be selected.

Logit choice models are the most complete with respect to modeling all of the factors that affect mode choice. Thus, they can be applied to a wide range of policy and investment studies. However, they are complex to build and are very data intensive. Most of the data needed require the use of complex performance or simulation models. The truck surveys are helpful for estimating the choice parameters, but these surveys are expensive and time-consuming to conduct.

Truck Conversion

The freight trip tables after the mode split step are multimodal commodity flow tables in annual tons. That is, after allocating the tables among the modes, the flow units will still be in annual tons. The flow unit in almost all highway travel demand models is daily or peak-period vehicles. Therefore, to consider the interaction of freight trucks on the highway with all automobiles and all other vehicles, the time period must be made consistent and the annual truck tables in tons must be converted from annual tons to daily trucks. Payload factors (average weight of cargo carried) are used to convert tons to trucks. The annual trips are then converted to daily trips by assuming an average number of operating days per year. But most travel demand models use average weekday travel. Various data sources can be used to estimate fraction of truck tonnage on weekdays and then divide this tonnage by number of weekdays per year. This process is discussed in more detail in later in this section.

Payloads or truck loads are limited by weight and volume considerations. The commodities carried by trucks have different densities and, therefore, different payloads for the same volume. Because of handling and packaging needs, payloads also may differ by commodity. For example, large size trucks carry heavier loads even for the same commodity. If payloads are calculated for different truck classes, the commodity tonnage needs to be allocated to the different truck classes. Smaller trucks tend to be used more in shorter-haul service. To the extent that length of haul and truck size are correlated, length of haul (directly available from commodity flow data) can be used in calculating payload factors. Payload factors can be calculated for loaded trucks only (estimated truck volumes must then be adjusted to account for percent of empties) or they can average empty and loaded weights.

The various sources of payload factors are 1) shipper or carrier surveys that provide information about the tonnage and commodity being carried; 2) weigh stations that typically have weight information by truck type, but not by commodity; and 3) the VIUS is a part of the Economic Census and is collected every five years. ["VIUS" stands for Vehicle Inventory and Use Survey, U. S. Census Bureau, 2002.  The survey was first conducted in 1963, under the name of Truck Inventory and Usage Survey (TIUS).  It was renamed as VIUS in 1997.  The survey was discontinued after the 2002 survey year was processed.  It had been conducted for years ending in “2” and “7.”]

4.1.4  Network Assignment

The process of allocating truck trip tables or freight-related vehicular flows to a predefined roadway network is known as the traffic assignment or network assignment. There are many types of assignments that are dependent on a number of factors such as level of geography, number of modes of travel, type of study and planning application, data limitations, and computational power such as software. The various types of assignments and their applications are explained in detail later in this section.

In developing a truck trip assignment methodology, some of the key issues and model components that need to be addressed and evaluated are as follows:

  • Time-of-Day Factors – These distribution factors by truck type separate truck trips that are in motion during each of the four modeling time periods; these factors need to be examined through recent data.
  • Roadway Capacity and Congested Speeds – A single truck will absorb relatively more of the available capacity of a roadway than an automobile, and a given volume of trucks will often result in a much greater impact on congested speeds than a similar volume of automobiles. So passenger car equivalent (PCE) factors are required to convert the truck flows to PCEs before the assignment process.
  • Volume-Delay Functions – These functions are used to estimate average speeds as a function of volume and capacity may be different for trucks than for automobiles.
  • Truck Prohibitions – Some freeways and major principal arterials in the region have prohibitions for certain classes of trucks, and this needs to be addressed before the assignment. A truck network also may be built based on the local knowledge of truck prohibitions and truck routes.

4.2 Urban Freight and Commercial Trucks

4.2.1  Definition of Trucks

In order to capture trucks accurately in a truck-travel model system, the mode “truck” needs to be defined first. This can be accomplished by examining the different types of trucks and identifying the different types of truck classification variables in the region. This essentially involves the way a truck is defined by its physical characteristics. This section describes the various classification variables that have been widely used by various agencies.

Number of Axles

The total number of axles on the trucks are normally categorized into four axle categories – two axles with four tires, two axles with six tires, three axles, and four or more axles. This information on vehicles can be obtained by visual identification or manual counts, or the use of axle sensor-based counters that are often used to collect accurate truck counts. The number and spacing of axles is used to classify trucks into FHWA’s 13-category classification scheme. Most of the vehicle classification count studies across the country classify trucks into these 13 categories, as listed below:

  • Class 1:  Motorcycles (Optional) – All two- or three-wheeled motorized vehicles. Typical vehicles in this category have saddle type seats and are steered by handlebars rather than steering wheels. This category includes motorcycles, motor scooters, mopeds, motor-powered bicycles, and three-wheel motorcycles. This vehicle type may be reported at the option of the state.
  • Class 2:  Passenger Cars – All sedans, coupes, and station wagons manufactured primarily for the purpose of carrying passengers and including those passenger cars pulling recreational or other light trailers.
  • Class 3:  Other Two-Axle, Four-Tire Single Unit Vehicles – All two-axle, four-tire vehicles, excluding passenger cars. Included in this classification are pickups, panels, vans, and other vehicles such as campers, motor homes, ambulances, hearses, carryalls, and minibuses. Other two-axle, four-tire single-unit vehicles pulling recreational or other light trailers are included in this classification. Because automatic vehicle classifiers have difficulty distinguishing Class 3 from Class 2, these two classes may be combined into Class 2.
  • Class 4:  Buses – All vehicles manufactured as traditional passenger-carrying buses with two axles and six tires or three or more axles. This category includes only traditional buses (including school buses) functioning as passenger-carrying vehicles. Modified buses should be considered to be a truck and should be appropriately classified.
  • Class 5:  Two-Axle, Six-Tire, Single-Unit Trucks – All vehicles on a single frame, including trucks, camping and recreational vehicles, motor homes, etc., with two axles and dual rear wheels.
  • Class 6:  Three-Axle Single-Unit Trucks – All vehicles on a single frame, including trucks, camping and recreational vehicles, motor homes, etc., with three axles.
  • Class 7:  Four-or-More-Axle Single-Unit Trucks – All trucks on a single frame with four or more axles.
  • Class 8:  Four-or-Fewer-Axle Single-Trailer Trucks – All vehicles with four or fewer axles consisting of two units, one of which is a tractor or straight truck power unit.
  • Class 9:  Five-Axle Single-Trailer Trucks – All five-axle vehicles consisting of two units, one of which is a tractor or straight truck power unit.
  • Class 10:  Six-or-More-Axle Single-Trailer Trucks – All vehicles with six or more axles consisting of two units, one of which is a tractor or straight truck power unit.
  • Class 11:  Five-or-Fewer-Axle Multitrailer Trucks – All vehicles with five or fewer axles consisting of three or more units, one of which is a tractor or straight truck power unit.
  • Class 12:  Six-Axle Multitrailer Trucks – All six-axle vehicles consisting of three or more units, one of which is a tractor or straight truck power unit.
  • Class 13:  Seven-or-More-Axle Multitrailer Trucks – All vehicles with seven or more axles consisting of three or more units, one of which is a tractor or straight truck power unit.

Gross Vehicle Weight (GVW)

GVW is a unique characteristic of a vehicle that is the sum of the empty vehicle weight and its payload. GVW classification ratings are primarily used for air quality modeling purposes. GVW ratings of vehicles cannot be observed or measured but can only be determined while administering intercept surveys. Hence, it is hard to associate a vehicle of certain GVW to a particular FHWA vehicle configuration as it only gives an indication about probable body type or even vehicle configuration. EPA provides guidance on the mapping of FHWA vehicle classes to MOBILE 6 vehicle classes for air quality modeling. [http://www.epa.gov/ttn/chief/eiip/techreport/volume04/ (see PDF of Chapter 2).] The VIUS database also provides a correlation between number of axles and GVW, and the GVW classes included in VIUS are: less than 6,000 pounds; 6,001 to 10,000 pounds; 10,001 to 14,000 pounds; 14,001 to 33,000 pounds; and greater than 33,001 pounds.

  • Vehicle Configuration – This is primarily based on the physical appearance of a vehicle. The classification scheme adopted by FHWA separates vehicles into 13 categories depending on whether the vehicle carries passengers or commodities. Nonpassenger vehicles are further subdivided by number of axles and number of units both power and trailer units. The VIUS database also has information on vehicle configuration but it classifies vehicles into four more general categories than the FHWA 13 vehicle classes. It also provides information on the axle arrangement, i.e., truck type and number of axles on a particular truck and/or combination. This variable in VIUS has more detail to the tune of 72 vehicle classes. So the correlation between FHWA and VIUS classifications is not very strong in terms of a perfect match.
  • Length of Vehicle  The length of a vehicle also is an important variable of interest if it can be measured accurately. The counters recommended by the traffic monitoring guide use two inductance loops to estimate length of vehicles crossing the loops. These dual loop sensors are generally capable only to classify vehicles into fewer categories than the FHWA 13 vehicle classes. The VIUS database reports the overall length of the vehicle or vehicle and trailer as it was most often operated.
  • Body Type – This type of classification is based on the appearance of the body of the vehicle and the type of commodity it carries most often. The Department of Motor Vehicles (DMV) data classifies vehicles based on body type. The California DMV data from the California Energy Commission that was used for the Southern California Council of Governments (SCAG) Heavy Duty Truck (HDT) Model Update classifies vehicles into about 55 categories and has a correlation with the GVW ratings. The VIUS adopts a different body type classification (32 classes) that is quite different from the DMV database. This type of information can be gathered only by visual or manual observations. Also, the plethora of body types makes it hard to correlate it to any other classification system.

The definition and classification of trucks into appropriate categories are very important so that accurate and reliable data is modeled to produce good forecasts. Hence, a proper classification system that is consistent across all the data sources should be developed. It is not just enough if a proper classification system is identified when developing a truck model, but also should ensure that observed data within the same classification system can be collected to validate the truck model against.

The SCAG HDT model represents heavy-duty trucks only, that is, trucks that are over 8,500 pounds. The primary use of this model is for air quality purposes and so it uses the weight-based classification system. These are:

  • Light-heavy (8,500 to 14,000 pounds);
  • Medium-heavy (14,000 to 33,000 pounds); and
  • Heavy-heavy (greater than 33,000 pounds).

The PSRC truck model also classifies trucks based on weight but these categories also are loosely correlated to other defining characteristics of trucks for other purposes.  These are:

  • Light Trucks – Four or more tires, two axles, and less than 16,000 pounds (this also includes nonpersonal use of cars and vans);
  • Medium Trucks – Single unit, six or more tires, two to four axles and 16,000 to 52,000 pounds; and
  • Heavy Trucks – Double or triple unit, combinations, five or more axles, and greater than 52,000 pounds.

The San Joaquin Valley truck model in central California is designed to generate truck volumes based on truck classes that the California Air Resources Board defines as medium-heavy and heavy-heavy duty for regulatory purposes (more than 14,000 pounds gross vehicle weight rating). These are:

  • Medium-Heavy Duty Trucks – GVW rating between 14,001 and 33,000 pounds; and
  • Heavy-Heavy Duty Trucks – GVW rating of 33,001 pounds and more.

The current Maricopa Association of Governments (MAG) truck model is based on GVW as well that includes three classes – light (less than 8,000 pounds), medium (8,000 to 28,000 pounds), and heavy (greater than 28,000 pounds). As the vehicle classification counts are based on FHWA classes, and due to the difficulty in correlating the GVW classes to FHWA classes, the new MAG truck model will include three groups of trucks. These are based on the FHWA classification system, as shown below:

  • Class 3 – 2-axle, 4-tire commercial vehicles (“Light”);
  • Classes 5-7 – 3+ axle, 6+ tire, single unit commercial vehicles (“Medium”); and
  • Classes 8-13 – 3+ axle, 6+ tire, combination unit commercial vehicles (“Heavy”).

4.2.2  Trucks that Do Not Carry Freight

There is a unique segment of truck population that does not carry freight, which also is known as the service sector.  This includes trucks that are used in the utility sector and other services related to commercial and residential land uses (i.e., business and personal services). Data on this type of trucking activity is difficult to collect through conventional survey methods because of overlapping nature of these types of truck trips with other industry types. As part of the FHWA commercial vehicle study, a method was developed based on various data sources that are commonly available to an agency. This methodology is provided in this section.

Model Methodology

If a separate model is to be created for trucks that do not carry freight, then it may be necessary to conduct a survey of the activity of these types of trucks. Without such a survey, it may be extremely difficult to update or calibrate this part of the truck model. There was data collected as part of the FHWA research on accounting for commercial vehicles in urban transportation models [Cambridge Systematics, Inc., Accounting for Commercial Vehicles in Urban Transportation Models, prepared for Federal Highway Administration, February 2004] that identified the magnitude and distribution of service vehicles in four categories: safety, utility, public service, and business and personal service vehicles. Data from the California DMV was used to identify fleet sizes for these vehicles. Average daily trip lengths were identified for these vehicles from the 2002 VIUS, which was summarized for metropolitan areas. VIUS also can be summarized by state or metropolitan areas within a state, but this may be too small a sample size. A similar approach currently is being proposed in the Phoenix MPO, the MAG, truck study where the size and weight of the vehicles in this category will be determined from the MAG region DMV registration data. In the event of lack of DMV data, truck population data by FHWA classes will be derived from the most recent county-by-county estimates of trucks from MAG’s Air Quality Planning department.

The four types of service vehicles in an urban metropolitan area are:

  1. Safety vehicles;
  2. Utility vehicles;
  3. Public service vehicles; and
  4. Business and personal service vehicles.

Public service vehicles are publicly owned. Business and personal service vehicles are privately owned. Safety and utility vehicles may be either publicly or privately owned.

About 5.9 percent of the total vehicle miles traveled in the urban areas in the United States each year is attributable to vehicles in these four categories.  Business and personal-service vehicles alone contribute 3.6 percent of the total VMT in urban areas across the nation, while public-service vehicles contribute 1.6 percent of the total VMT and safety and utility vehicles contribute 0.4 percent each.

Many older urban transportation models currently do not include specifically include commercial service vehicles, although some models have identified a commercial vehicle trip purpose that is based on a fixed factor of personal nonhome-based travel. Some truck models also include delivery and service vehicles that are four-tire commercial vehicles, based on the inclusion of these vehicles in the 1996 edition of the Quick Response Freight Manual.

Data Sources

One of the key sources of information essential for estimating a model for this sector is the truck populations for the four categories of service vehicles. DMV registration data and commercial vehicle surveys have been usefud to estimate truck populations for this sector. These are described below.

Cambridge Systematics, Inc. (CS) created a dataset combining data on safety, utility, public service, and business and personal service vehicles.

  • Safety vehicles were derived from two sources: 1) California DMV data on police, fire and rescue vehicles, and tow trucks for Los Angeles, San Francisco, San Diego, and Sacramento; and 2) the Detroit commercial vehicle survey, which includes snow plows and tow trucks.
  • Utility vehicles were derived from two sources: 1) California DMV data on utility cars and trucks, water and irrigation trucks, and garbage trucks for Los Angeles, San Francisco, San Diego, and Sacramento; and 2) three commercial vehicle surveys that included utility and maintenance vehicles for the Detroit, Atlanta, and the Triad cities regions.
  • Public service vehicles were derived from a single source: California DMV data on city, county, state, Federal, other, and school and college cars for Los Angeles, San Francisco, San Diego, and Sacramento.
  • Business and personal service vehicles were derived from two sources: 1) California DMV data on “other commercial cars,” armored, panel and pickup trucks, vans and step vans for Los Angeles, San Francisco, San Diego, and Sacramento; and 2) three commercial vehicle surveys that included vehicles used for office, professional, or personal services in the Detroit, Atlanta, and Denver areas.

Data for four cities – Los Angeles, San Francisco, San Diego, and Sacramento – were compiled and analyzed because these were the only four cities with a comprehensive assessment of all commercial service vehicles. Demographic data for each city, including total population and employment by type (government, utility, business and personal services, and total), were derived from the 2000 Census.

For the new MAG truck model update, a new approach on deriving this data is being proposed due to the lack of DMV data. The truck population data and the VMT distributions at the county level is being prepared before estimating parameters for this sector. CS obtained the truck population data at the county level for all the counties in the State of Arizona. These data are at the 13 FHWA classes and will be disaggregated to the 28 MOBILE6 vehicle categories to get a better sense of the body type of trucks. This disaggregation process will be based on the VMT mix data for the 28 vehicle classes that already are derived for air quality modeling purposes at MAG. For the FHWA research project, CS developed a method that correlates body type of trucks to the use of the truck or industry sector. This method will be used here to identify those vehicle classes out of the 28 that fall under the service industry sector.

Aggregate Demand Method

The Aggregate Demand Method estimates service vehicle fleet size based on two demographic factors: total employment (possibly stratified by type) and population. A summary of the travel behavior characteristics is provided in Table 4.3. This summary includes estimates of fleet size, number of trips, and VMT calculated from a statistical analysis of the available data combined with demographic data. The only comprehensive data source (including both public and private sector data) is the motor vehicle registration data, so only these data are used in estimating rates of travel by commercial service vehicles. The data shown in Table 4.3 do not show trips per vehicle, so the commercial vehicle surveys from other cities are used to provide data on this variable for private sector vehicles only. The percent of vehicle miles traveled will be derived from MAG’s air quality modeling work.

Table 4.3    Travel Behavior Characteristics for All Commercial Service Vehicles
Using the Aggregate Demand Method

Travel Behavior Category

Description

Estimates

Fleet Size

Fleet size can be estimated as a function of population, based on data from truck populations.

0.05 per population (data from four cities).

Trip/Tour Length

Average mileages are consistent across different cities and categories, ranging from 29 to 49 miles per day.  National average miles traveled will be derived from VIUS data. Average mileage will be derived from other commercial vehicle surveys.

41 average miles traveled per day, average trip length is 14 miles (data from eight cities).

Trips

Trips per vehicle can be derived from a commercial vehicle and government vehicle survey.

Three daily trips per vehicle (data from four cities).

Vehicle Miles Traveled

Service vehicles typically range from 5 percent to 13 percent of total VMT (based on estimates from other cities derived from DMV and VIUS data).

5.9 percent of total VMT (data from four cities).

 

Network-Based Quick Response Method

Data on public and private service vehicles were available for only four cities: Los Angeles, San Francisco, San Diego, and Sacramento. No data was available for the number of vehicle trips or mileages for these four cities because the DMV data for those cities contains only fleet size. Data on vehicle trips and mileages are available from commercial vehicle surveys for private sector service vehicles for the cities of Atlanta, Denver, Detroit, and the Triad cities. Additional data are necessary to more accurately evaluate travel behavior for all service vehicles. Table 4.4 presents a summary of the travel behavior characteristics for the Network-Based Quick Response Method.


Table 4.4    Travel Behavior Characteristics for All Commercial Service Vehicles
Using the Network-Based Quick Response Method

Travel Behavior Category

Description

Trips/Tours

Cross-classification or regression models can be used with employment variables. Government, utilities, and business and personal services employment are the most likely variables. Trip rates will be based on the truck population data and the Bureau of the Census. Typically, there are 0.1 per total employment or 0.05 per population.

Distribution

All service vehicles are distributed widely throughout the region and could be distributed with a gravity model. National average miles traveled will be derived from VIUS data. Average trip lengths will derived from other commercial vehicle surveys.

Vehicle Type

Service vehicles are primarily light-duty vehicles, dominated by public service, business, and personal service types (all light-duty vehicles). Some safety and utility vehicles are medium- and heavy-duty trucks (fire trucks, ambulances, utility trucks, etc.). Of all the commercial service vehicles, 91 percent are light-duty vehicles and 9 percent are medium-/heavy-duty trucks (based on data from other cities).

Time of Day

The majority of private service vehicles operate between 9:00 a.m. and 3:00 p.m., based on private service vehicles from the commercial vehicle surveys. The majority of public service vehicles also operate in this period. Of all total trips, 11 percent occur in the a.m. peak, 23 percent in the p.m. peak, 53 percent in midday, and 14 percent at night (data from other cities).

Trip Assignment

Service vehicles operate on all facilities.

 

4.2.3  Integration of Trucks in Four-Step Passenger Models

The truck-trip generation process in a four-step travel model system is independent of the passenger modeling components. The socioeconomic and demographic (SED) data is often shared between these two models that serve as the basic input providing a host of independent variables to compute productions and attractions. After the truck Ps and As are computed, they are fed into the truck distribution process which requires skim data that may include either travel time or distance. These skim data are derived from the assignment process which is a common modeling component for the truck model as well as the passenger model. This is the first point of integration between the truck model and the four-step passenger model. More details about this process are described in the trip distribution section of this section.

After the trip distribution models, truck trip tables are produced that are ready to be assigned to the highway network along with other modes considered in the passenger model. Trucks are much larger in size than the passenger cars and the presence of these large and low-performance vehicles in the traffic assignment process results in a reduction of the roadway capacity.  The Highway Capacity Manual (HCM) cites that the reduction in roadway capacity is due to the fact that heavy vehicles such as trucks take up more space and have lower performance, especially on grades and during congestion. So the traffic volumes containing a mix of vehicle types of different sizes must be converted into an equivalent flow of passenger cars often referred to as the passenger car equivalents (PCE).

Different models use different PCE factors for trucks that are appropriate to the local region. It also depends on the different sizes and speeds of trucks in the model; the ideal way to calculate PCE factors is by collecting observed data. This can be done by gathering information on the vehicular composition at certain key segments of a region’s highway corridors that also includes speeds, travel times, grade, and congestion. As the data required for such an elaborate method is often scarce, most urban models assume these factors and calibrate them during the assignment process.

The PSRC truck model that includes three classes of trucks assumed light trucks to be equivalent to 1.5 passenger cars, medium trucks at 2.0, and heavy trucks at 2.5. After several rounds of calibration with more recent data, the PCE factors were updated and are now 1.0 for light, 1.5 for medium, and 2.0 for heavy trucks. Similarly, in the San Joaquin Valley truck model, there were no observed data available to support the development of PCE factors specific to the San Joaquin region. Therefore, the PCE factors used in the model based on guidelines provided by the Institute of Traffic Engineers were 2.0 for medium-heavy and 2.5 for heavy-heavy trucks.

The current SCAG HDT model includes a state-of-the-art PCE factor methodology that accounted for roadway grade, congestion levels, and percentage trucks in the traffic stream. The variable PCE factors have proven to be complex in their implementation and do not always represent the assignment process accurately. In the ongoing SCAG HDT model update, the variable PCE factor approach is being evaluated based on recent data to determine if it results in more accurate assignments. One area where the variable PCE factor does appear to provide improved assignments is the adjustment related to roadway grade. In the SCAG HDT model update, the locations where grade have been incorporated in the network are being reviewed for accuracy and additional locations with significant grade are being identified and incorporated in the highway network.

4.2.4  Data Requirement for Truck Models

In order to determine the data required to build a truck travel model, the first step is to assess the various truck parameters that need to be estimated. In statistical terms, these also are referred to as the dependent variables that depend on a host of explanatory or independent variables that often serve as the inputs to an urban truck model. The truck parameters of primary interest, but not limited to, are:

  • Truck productions and attractions by land use or sector or trip purpose;
  • Truck trips per day by truck type (GVW, FHWA class, etc.);
  • Truck trip lengths by truck type;
  • Truck trip time-of-day distributions; and
  • Truck volumes.

The aforementioned parameters are dependent on various inputs or independent variables that include, but not limited to:

  • SED Data or Employment Data – These data are essential to estimate truck production and attraction trip rates which are a function of observed truck trips coming into and going out of various land use types for which the SED or employment data are known beforehand. The observed truck trips are determined based on truck travel surveys. Different models use different types of employment data depending on the availability for the base and forecast years. Most of the current urban truck models use the two-digit SIC system of employment data. The level of aggregation or disaggregation of these into a finite number of categories depends on the variance of truck travel patterns associated with different land use types. The variance largely depends on the region’s economic activity that includes production and consumption of commercial goods. More recently, the NAICS system of employment data is being developed to better correlate and associate various employment categories to different types of businesses prevalent in an urban area.
  • Level of Service Data – These data include travel times and/or travel distances of vehicles in an urban area. This data is produced within a model system and is often known as the skim data. The skim data is an essential input to the gravity-based trip distribution models that estimates truck trip interchanges. The skim data is used as an independent variable to compute the travel impedances, which is then used to allocate the truck productions and attractions from the trip generation model to the appropriate origins and destinations in a region. This results in a truck trip table matrix, which is used in combination with truck travel distances to calculate the average truck trip lengths and frequency distributions.
  • Time-of-Day Factors – The truck travel surveys or classification counts are normally used to determine the time-of-day factors, which are proportions of truck trips occurring during a finite set of time periods. These time periods are decided beforehand depending on the level of detail necessary for an agency’s transportation planning purposes. The proportions or factors are applied to the daily trip tables coming out of the trip distribution model to produce trip tables by time period. These time period specific truck trip tables are then assigned to the traffic network along with the corresponding time period specific passenger trip tables.
  • Truck Classification Counts – The most important data that cannot be transferred or borrowed are the classification counts. Every model update includes the collection of these data. These are used to calibrate and validate the traffic assignment process that includes both passenger cars and trucks. Some agencies have a continuous traffic count program on key facilities such as freeways and expressways that are used in regular time intervals to update regional travel models. The level of detail of truck counts by various truck types or classes largely depends upon the truck model structure. Most count programs collect axle-based truck classification counts as these are easily captured by manual and machine counters. Agencies that use truck models based on GVW ratings convert the axle-based truck counts to appropriate GVW classes based on internally developed algorithms. The count locations also are important in the validation process of a truck model. These are usually collected on all the major facilities such freeways, expressways, and arterials. These also are collected at various points on a screenline and many screenlines are defined upfront of the count program. In addition to counts, other observed data that is necessary are truck speeds or travel times on key routes.
  • Level of Geography – Truck models are usually developed at the same level of geography as the passenger travel models. Almost all of the known urban area models use the TAZ-level geography. The primary reason for this being that all of the input data to a truck model is being developed at the TAZ level. There also are some aggregate levels of geography such as districts, super-districts, and counties that are often used to summarize truck model outputs during validation processes.
  • Roadway Networks – This forms the backbone of any model development effort that represents any region’s transportation infrastructure system. Truck models often use the highway networks that are developed for the passenger travel models and appropriate modifications are made based on the truck travel characteristics. These include coding truck only lanes, truck prohibition lanes, and/or truck priority lanes. The "truck" mode of travel also is coded as a separate mode to distinguish from other passenger travel modes and to determine truck travel volumes.

4.2.5  Special Generators at Intermodal Terminals

An intermodal terminal can be defined as a location for the transfer of freight from one transport mode to another such as between water and road (ports), road and rail (rail yards), or air and road (airports). The coordination of resources to achieve intermodal efficiency is a challenging task that involves government, the private sector, and various interest groups [http://www.doi.vic.gov.au/DOI/Internet/Freight.nsf/AllDocs/]. Intermodal terminals, which include seaports, airports, and rail terminals, serve as principal interchange points for both international and domestic freight movements.

The data collection efforts at intermodal terminals are always a challenge owing to the enormous time and costs associated. In addition, these data are specific to each type of intermodal terminal and cannot be transferred or borrowed. Specific models also are built based on the capacity and volume of traffic being handled at these facilities. The Southern California Association of Governments (SCAG) HDT model and Los Angeles Metropolitan Transportation Authority) LAMTA CubeCargo model are perhaps the only two models that capture the truck traffic coming out of and going into each of these three intermodal facilities in the region at the TAZ level.

Port Model

The port model for the SCAG HDT model included trip generation and distribution components. The port trip generation model was developed based on a detailed port area zone system and specialized trip generation rates for automobiles and trucks by type (Bobtail, Chassis, and Containers). The model generates three outputs – container terminal truck trips, container terminal automobile trips, and noncontainer truck trips. These three types of trips are usually the same across every seaport in the country. The Port of Long Beach (POLB) has a custom-built spreadsheet tool called the QuickTrip model that includes detailed input variables such as mode split (rail versus truck moves), time-of-day factoring, weekend moves, empty return factors, and other characteristics that affect the numbers of trucks through the gates. These factors vary by terminal at the ports, so a separate QuickTrip model is used for each terminal.

For trip distribution of port trips, a detailed and comprehensive truck-driver survey was undertaken at port marine container terminals. The surveys were used to develop detailed origin/destination “trip tables” for use in the port area travel demand model. The stated trip origin and destination from every valid survey was correlated with the travel demand model traffic analysis zone (TAZ) system. The survey results were then used to develop port truck origin/destination matrices by truck type for use in the model. The port trip matrices included a unique trip interchange percentage between every port marine container terminal and each of the model’s TAZs. This includes not only trips from marine terminals to land uses outside the ports, but also “interterminal” trips from one marine terminal to another marine terminal.

Rail Intermodal Facility

For LA MTA’s CubeCargo model, an innovative approach was used that yielded reliable information on the six rail facilities at a fraction of the investment in time and cost. The approach for the rail intermodal facilities began with contacts with the rail companies (BNSF and UP) regarding the six facilities. These contacts served a couple of purposes, namely, identifying the largest customers for each facility, and obtaining lift, gate, and train data. Additional data also was gathered that included lifts by day, split out by containers (international and domestic) versus trailers, and gate transactions by day by type (inbound, outbound, loaded, empty, bobtail). These data yielded the flow through the facilities without becoming entangled in short-term changes to train schedules and other operating adjustments. The train schedules themselves were available on-line and were supplemented with railroad records of actual arrivals and departures since some trains are run as extra or second sections.

By contacting the six facilities, relevant facility data were obtained that included a few relevant features of the rail facilities such as total acres, number of parking spaces, number of gates, number of employees/contractors, etc. The major customer contacts yielded the location and nature of their facilities, the location of their major markets or customer concentrations, and their pattern of truck trips between their facilities/markets and the six rail intermodal terminals in both directions, including empties and trips to obtain empties for loading. This information was then used to characterize and construct trip matrices for the nonport portions of truck traffic to and from the six rail intermodal facilities.

Air Cargo Trips

In the SCAG HDT model, air-cargo truck trips come from the agency’s Regional Airport Demand Allocation Model (RADAM) that was developed as a separate two-step process – airport trip generation and distribution. Three types of airport truck trips were accounted in this process. Heavy-duty trucks associated with airport operations such as maintenance, supplies, deliveries, and retail facility support comprised one category. Traffic between the five airports or with destination points outside of the airport area formed another element, while internal trips made by trucks within the airports formed the third component. The process of air-cargo trip generation involved the conversion of air cargo tonnage to truck trips, using the factors and relationships developed as part RADAM. The distribution was developed based on approximations of air cargo trip interchanges between airports and RADAM TAZs.

4.2.6  Constraints to Trip Generation

The general notion of building a trip table involves assuming that productions equal attractions. Depending on the availability of truck travel survey data, trip rates for a given sector or land use are either considered the same for production and attraction or they are estimated separately at each trip end. If the trip rates are assumed to be the same at both ends, then typically these are land use-based trip rates.

If data permits estimating two different rates for production and attraction, then these may be either employment- or land use-based trip rates. That is, the employment at that particular land use will drive the productions and/or attractions for any given sector. For example, “retail employment” in a TAZ can produce and attract trips that belong to the “mail/parcel” sector, if the supported by the data. If there are 200 “mail/parcel” expanded trips that are produced from a “retail” store, and if there are 300 “mail/parcel” expanded trips that are attracted to a “retail” store, then the production rate will be (200 trips/retail employee) and the attraction rate will be (300 trips/retail employee). These rates also can be estimated based on regression techniques where the dependent variables if the number of truck trips for a given sector and the independent variables are different types of employment. The coefficients associated with each employment variable are the trip rates. In other words, every sector (or trip purpose) will have a production rate and attraction rate for every type of land use (or employment) where trucks in that sector make stops at.

In the event of different productions and attractions, these will need to be balanced during trip distribution, so that the total number of trips originating from a given TAZ equal the number of trips destined to that particular TAZ.

4.2.7  Borrowed versus Survey-Based Truck Models

The borrowing of truck trip rates is a very common practice due to the lack of good survey data. This should, however, be done with caution. Almost one-half the urban truck models across the nation are based on the 1992 Phoenix metropolitan area truck model. The current QRFM recommends using the trip rates and gravity models from this model as a starting point, and then calibrating the parameters until they validate well with observed local count data. There are some limitations to this approach that needs to be understood well before borrowing truck parameters from other area models. The observed count data will serve well to validate the truck trip assignments but there will be no data for calibrating and validating trip generation and distribution models. That is, the precise estimates of total number of truck trips within each trip purpose or sector cannot be collected through a vehicle classification count program. Trip rates can be adjusted only after looking at the assignment results. Also, the average trip lengths and trip length frequency distributions can be calibrated only to approximate values and distributions borrowed from other area models.

The best way to estimate truck-model parameters is by collecting data through truck travel surveys. Different types of surveys such as trip dairy approach, establishment surveys, shipper/receiver surveys, and intercept surveys, provide different aspects of truck travel characteristics depending upon the type of business sector or trip purpose of trucks. The many benefits of using survey data are that:

  • Truck trip rates by sector or trip purpose can be estimated precisely as it will be calibrated and representative of the local truck travel behavior;
  • Observed data on average trip lengths and trip length frequency distributions can be used to calibrate/validate the trip distribution model;
  • Precise time-of-day factors can be derived from the observed survey data; and
  • Information on local issues also can be gathered from truck operators and drivers that could include commodity carried, qualitative data on what shippers and truckers see as their most difficult infrastructure problems (i.e., difficult intersections, bottlenecks, bridges, turning radii, road conditions, etc.), what most impacts their operation, etc.

The major limitation of truck travel surveys is the cost associated to conduct them especially since the response rates are well known to be very low. A considerable amount of resources and expertise is required to administer and conduct a successful truck travel survey.

4.2.8  Market Segmentation-Based Mode Split

The market segmentation-based method uses information from commodity flow data and base year mode split to forecast future mode split. It assumes that commodity and length of haul are good predictors of mode choice. The market segmentation method looks at the base year mode split by commodity and origin-destination pair and assumes that this reflects the relative service characteristics of available modes in these traffic lanes.

The basic assumption in this approach is that mode share for each commodity/O-D pair remains fixed in the future. But in the real world, the changes in the mix of commodities traded and the trading partners do affect overall mode share. So while using this approach, forecasters can do “what-if” scenarios by focusing on those markets (commodity/‌O-D) where modes actually compete to see if changes in modal characteristics could actually have a significant change on an overall mode share. The main data component here is the commodity flow data. When used for modal diversion analysis, the focus is primarily on intermodal cargoes and this can be determined from commodity flow data.

The following is presented as an example that explains the market-segmentation method when applied to modal diversion analysis for a state or a group of states. Using GIS tools or a routing network, the first step might be to determine a 500-mile radius from the centroid of each zone within the study area, where a zone might be defined as a county. O‑Ds farther than 500 miles from a zone usually represent O-D pairs where rail could compete with trucking. Now, using CFS or other national sources, those commodities need to be identified for lengths of haul greater than 500 miles where rail captures a known share of the market (e.g., 20 to 70 percent). The next step is to identify commodity/O-D pairs (at least one trip end in the study area) where rail is competitive but rail share is less than 50 percent. This will help in conducting what-if scenarios to see what impact would be if rail share could grow to 50 percent or 70 percent in all of the competitive markets. The changes or results of this modal diversion analysis can be seen in the total tonnage splits. It is always better to use national data to identify commodities for which truck and rail compete than to use study area commodity flow data. This is due to the fact that the lack of rail services may be limiting local markets and that is what requires change.

Pros

The advantages of the market-segmentation method are that:

  • It is simplistic in approach and in application;
  • The data is usually available for such an approach and is easy to process; and
  • It is reliable enough for modal networks and characteristics that do not change over time.

Cons

The limitations of this approach include:

  • Insensitiveness to policy impacts on mode choice;
  • Insensitiveness to implications of network investment strategies on mode choice; and
  • Assumption that modal characteristics remain constant over time when in reality there is a lot of variation.

4.2.9  Assignment Models

Traffic assignment is the last step in a travel-model system and there are a couple of broad ways to assign trucks to a roadway network. Truck assignments on highways could be either fixed or dynamic path assignment. In a fixed assignment, trucks are assigned to already existent fixed paths, whereas in a dynamic assignment, a computer program builds paths for the trucks. The key factors that go into the building of these paths, fixed or dynamic, are:

  • Infrastructure limitations (low bridges, bridge weight limits, speed limits, etc.) affect route choice.
  • Specific routings are usually selected as a function of cost, average travel time, the reliability of that travel time, and the general quality of service for the operators (safety, amenities, etc.). This happens after taking into account the limitations among available route choices.
  • The route may need to use specialized equipment or facilities, such as refrigerated terminal, or the cargo may be restricted from certain routes, like hazardous material/‌cargo prohibitions in tunnels.
  • The route may take competition among truck carriers or between modes into account (ship to certain intermediate destinations, less than truckload handling).
  • The operational characteristics of the network may be important, such as special truck routes, climbing lanes, or truck exclusions. The conditions probably vary by time of day in urban areas, which may affect the routing.
  • Highway routings or traffic assignment may be affected by all of the aforementioned factors, but only a few of them may be considered by the fixed path or dynamic models.

Fixed-Path Assignment

Fixed paths are provided by others, that is, paths already built are used. It may sometimes represent current routings of traffic or results of another dynamic assignment (e.g., ORNL routes for CFS flows). In fixed paths, if the network attributes change, either because of new facilities or congestion, there is no easy way to vary the paths. Also, the business decisions of carriers (which railroads work together) that are not easily modeled can be defined in these fixed paths.

The basic procedure in any assignment is to translate trip-table flows into link flows on a network and to use those link flows to determine system performance. The intermediate step used to make this translation is the information about the path or sequence of network facilities (links) used to travel from an origin to a destination. The basic feature of these paths in this assignment method is that they are fixed and would not vary, depending on network condition, congestion, new facilities, etc. These fixed paths can come from a variety of sources (TRANSEARCH, MapQuest, etc.). Invariably, these paths were created by the dynamic assignment methods that are described in the following section, but they have been saved by others as fixed paths for use in these assignments.

Once the paths are identified, it may be desirable to find the network flows a) for only selected origins or destinations (selected zone assignment); b) for only selected commodities (selected purpose assignment); or c) for only those flows that use certain facilities (selected link assignment).

For fixed-path assignment, the network needed does not have to be as rigorous as those used in sophisticated models, although for data management purposes alone, it is highly advisable. The paths are a file of the sequence of links used between each origin-destination pair. In order to produce system performance, the performance attributes for each link are required. These include information about the network and information about particular links in terms of travel times, costs, and distances among other parameters.

Fixed-path assignment methods are typically used to analyze long-haul traffic patterns at state or multistate level. Since the trips are over very long distances, the routing decisions are less responsive to local changes in network conditions and may remain fixed over long periods of time. These methods often develop deficiencies as traffic grows over time. However, they cannot be used to examine alternatives as the assignments are not responsive to network changes. Since routing models for nonhighway modes are generally proprietary or carrier-specific (and routing choices are more limited than for trucking), fixed path assignments can be very well used in these applications.

Dynamic Path Assignment

In dynamic assignment, paths are calculated by a computer program and may be used and discarded without the planner ever seeing them. Since the dynamic paths are computed as they are used, it is possible for the assignment to account for changes in the network. Dynamic assignment is the most commonly used process in urban automobile and transit passenger modeling. The outcomes from a dynamic assignment are similar to those of the fixed path assignment, such as link flow and network performance; however, dynamic path assignment can take congestion into consideration.

These paths also are a file of the sequence of links used between each origin-destination pair, but these files are temporary and created by the computer program. In order to calculate system performance, the impedance attributes are used to calculate the performance of each link. These include information about the network and information about particular links in terms of travel times, costs, and distances among other parameters.

Dynamic assignments can be used for any level of geography for which flows and networks are available and is the approach often used for modeling truck traffic at the metropolitan level. It is a more accurate way to estimate the impact of congestion on freight system performance, as the model can calculate new routes as congestion increases. It also is the best approach for alternatives analysis because the network can be modified to reflect alternative investment projects.

As explained for fixed-path assignments, the basic procedure in any assignment is to translate trip table flows into link flows on a network and to use those link flows to determine system performance. The intermediate step used to make this translation is the information about the path or sequence of network facilities (links) used to travel from an origin to a destination. In a dynamic path assignment, this path file is temporarily created within the assignment program. Just like in a fixed path assignment, the paths calculated in a dynamic process can be applied to perform selected zone assignment, selected purpose assignment, or selected link assignment.

For dynamic-path assignment, the network needs to follow the rules of the assignment program. In order to produce system performance, the performance attributes for each link are required and coded on to the highway network. These include information about the network and information about particular links in terms of travel times, costs, and distances among other parameters.

It is possible to calculate a wide variety of performance measures for dynamic-path assignments. It also is possible to do assignments for selected groups of commodities or other parameters analogous to trip purposes in passenger travel demand models. It is relatively complex to implement since special networks and software are required. Since it is so complex, the results of changes to the network may be counterintuitive or at least not obvious beforehand. However, it is very easy to modify the paths to account for new facilities or network conditions.

Type of Dynamic Assignments

There are a variety of methods to dynamically calculate paths which are described below.

  • All-or-Nothing or Preload Assignment – In the All-or-Nothing procedure, also referred to as preload, freight traffic is assigned to network without recalculating times or costs taking capacity constraints into consideration. It is appropriate for long-distance traffic flows where there may only be one desirable path anyway. Since a straight All-or-Nothing assignment typically loads too many trips onto the major facilities, a procedure to adjust the impedances for nonmajor segments is often applied.
  • Multiclass or Simultaneous Assignment – Truck trips are usually assigned together with the passenger vehicle model, because congestion has a significant impact on travel times experienced by trucks. If either nonfreight trucks or other vehicle trip tables are not available for congestion calculations, then they are preloaded onto the network using an All-or-Nothing procedure. Some agencies believe that trucks should be preloaded in all cases, because they do not believe that trucks, particularly larger less maneuverable trucks that may be operated by drivers not familiar with alternative routes, are as likely as automobiles to change their paths in response to congestion. Truck volumes are converted to Passenger Car Equivalents (PCE) to account for the fact that larger trucks take up more capacity and congestion for assignment of both trucks and passenger cars. This is explained in detail under Section 4.1.3.
  • Stochastic Assignment – In a stochastic or random assignment, all reasonable paths are used and are typically used in urban areas. It takes multiple paths in a network into consideration, and the user has control over how big a difference from the shortest path is reasonable. In any event, equal time/cost paths between the same O‑D pair will receive an equal share of the O-D flows.
  • User-Equilibrium Assignment – In equilibrium assignments, the travel times are recalculated based on delays associated with a loading and paths are recomputed and combined, such that all used paths have same travel time. This method is generally used in urban areas where there is a lot of congestion, and it takes network’s current capacity into consideration. Under equilibrium conditions, traffic arranges itself on congested networks in such a way that no individual trip maker can reduce his cost by switching routes. The equilibrium method attempts to find a solution where all used paths have the same travel time by iterating between All-or-Nothing traffic loadings and recalculating link impedances, such as travel time, based on the link volumes and capacity after each iteration. In fact, equilibrium is capacity restrained, since link times are recalculated based on capacity after each iteration. Capacity restrained assignment typically refers to those assignments where the user, not the computer, chooses how to proportion the flows from each iteration. For example, under equilibrium assignment, the computer calculates and may decide that equal times are achieved if 33 percent of the first assignment flows and 67 percent of the second assignment are used. Under capacity restrained assignment, the user may decide beforehand that 50 percent of each assignment is to be used.

4.3 State Freight Forecasting

4.3.1  Type of Model, Zone Structure, and Networks

Freight models in states that are geographically small and densely populated with adjoining urban areas, such as Connecticut and New Jersey, tend to take the form of urban truck models discussed in Section 4.1 above and will not be discussed further here. Freight models in larger states, particularly those with larger rural areas and/or large percentages of pass-through traffic, such as Indiana, Florida, and Wisconsin, forecast freight in “four-step” commodity models, are a principal focus of this section. Still other states, such as Virginia, Tennessee, and Georgia, follow the general form of commodity model, but use acquired commodity freight tables in lieu of forecasting those tables in the trip generation and trip distribution, and will be discussed in Section 5.0.

State “four-step” commodity models are truly multimodal. The modes considered in these models typically include truck, rail, water, and air, even though the assignment step may only address trucks, and sometimes rail. As multimodal commodity models, the flow unit is common to all modes, and is typically tons.  These models tend to be calibrated from annual commodity flow tables and the forecasts in the first forecasting steps will be annual tons.

Freight forecasting models, as all models, should have boundaries such that they internalize most of the trips that will be subject to forecasting. In the case of passenger modeling, these boundaries can be set at the jurisdictional boundaries of the state. Internal freight traffic within a state is typically no more than 25 percent of the flow total, and the flow to, from, and through the state due to national traffic comprise the majority of the freight flows. In order to properly forecast this traffic, the geographical area covered by state freight models typically is most of the continental United States, if not all of North America. The inclusion of modes that primarily travel distances of over 500 miles, such as rail, water, and air also suggests that the freight modal boundary should be much greater than just the state boundary. States that have developed “four-step” commodity freight models typically already have developed detailed travel-demand model zones and networks within the state boundary. These models and zone systems have been extended by inclusion of national highway and rail networks.

4.3.2  Integration with Four-Step Passenger Models

There is value in being able to forecast freight flows, even when those forecasts are not integrated with passenger forecasting models. However, those states that have developed “four-step” commodity freight forecasting models have almost always had an existing passenger model. That passenger model has a zone structure and at least a highway network that can be used in developing commodity freight models. There is an additional reason for integrating freight and passenger model. At least for certain modes, always for trucks and passenger automobiles, and less often for freight and passenger rail, the modal networks are shared by passenger and freight vehicles and theses vehicles will interact in causing and being impacted congestion. There are several issues that must be addressed in integrating the passenger and freight models. The time period for passenger models is typically daily, while the time period for state freight models is typically annual. Before combining the forecasts, the freight flows are typically converted to daily flow units. The passenger and freight models can be kept separate through the trip generation, trip distribution, and mode split steps. However, the socioeconomic and transportation data used by these respective models should be the same. The tables of travel times covering the same areas should be the same for both models. The employment for the freight model may include more detailed industrial classifications, but the employment data and forecasts should be consistent with the employment and zone totals that are used in the passenger model. The freight and passenger models need to be combined in the modal-assignment step and that is when the vehicles will be combined. Therefore, the issues that will be discussed in later sections include converting the commodity freight flow units to vehicles and, for highway assignments, dealing with the issues of combining trucks and automobiles through the use of PCE, and in what order the trucks and automobiles should be assigned and interact.

4.3.3  Data Requirement for State Freight Models

For statewide freight models, data are needed to develop and specify the equation used in the various steps, and forecast adapt is needed in the same format to create freight flow forecasts. In a passenger forecast, the equations and relationships are developed from a household survey of travelers. In freight models, a commodity flow survey, typically either the publicly available Census Bureau’s CFS or the private commercially available TRANSEARCH data available from Global Insight. These tables tend to have limitations that must be overcome in using them to survey as freight surveys for model development. The CFS is publicly available only for 114 zones nationally, while TRANSEARCH is available for county zones, but the number of zones increases the purchase price. The challenge in the use of both models, either through additional processing of the CFS, or eventually through the FAF2 database derived in part from the CFS or through purchase of TRANSEARCH, is to develop zone structures that are detailed within the model study area, the state, and increasing less detailed at distances from the state model area. The state counties in TRANSEARCH led their zone structure to be used at the aggregate level to develop district relationships between freight flow and an economic variable, usually employment, which can then be applied to smaller units of geography. The commodity table typically has what is referred to as two-digit level of detail. Employment data are needed at an industry detail matching this freight commodity structure. Even the 40-50 commodities available provide data management and computational challenges and commodities carried forward are typically those that are the largest and most important to the study area. The associated employment must be available for those important commodities but may be aggregated to less detail matching the aggregated commodities. For example, printing may be included with all nondurable manufactured goods while food products would be retained as a separate category.

These commodity-flow surveys also provide information needed to calibrate the trip distribution and mode split steps. Commodity flows will typically need to be converted into units of daily vehicles because this more easily integrates with passenger forecasts and other transportation design, and operations tasks are typically based on daily flows. Data are needed to develop factors that can be used to convert from annual tons to daily trucks. The model needs to be validated to observed counts. This validation data, on highways, is observational, such as truck classification counts and typically will have no information on the commodities being carried. Since observational counts also include no information on truck purpose, those counts probably include trucks carrying local delivery of local freight or trucks used in construction, service, and utility trucks, none of which are included in the freight commodity model. Conversion from annual flows to daily modal vehicle flows is needed only for those modes that will be used in assignment.

In addition to calibration data, there is a need for forecast variables. The creation of a model that forecasts freight flows based on detailed industry employment for the zones in the model provides no value unless the detailed employment forecast can be obtained or created for the same industry and geographic detail in that same detail on coverage similar to zone structure.

4.3.4  Trip Generation

Trip-generation equations allow the development of forecasts for the flow of freight entering or leaving a zone based on economic conditions in that zone, most often employment. Since the amount of freight consumed or produced by employees will be different commodities and both in the amounts and the types of industries involved, these state models develop different equations for different commodities. The number and types of commodities to be included depends largely on the computational resources available and the economy of the state. These equations are developed through regression of the observed commodity survey data to employment by industry. Examples are provided in this section for the trip-generation equations developed for the Indiana, Florida, and Wisconsin “four-step” commodity freight models.  Indiana developed trip generation equations using the 1997 CFS as the sample survey and employment by NAICS industry as the independent variable in the regression as shown in Table 4.5.

Table 4.5    Indiana Freight Model Variables used in Trip Generation

NAICS Employment

Description

212

Minerals and Ores

311

Food Manufacturing

312

Beverages and Tobacco

313

Textiles and Fabrics

314

Textile Mill Products

315

Apparel and Accessories

321

Wood Products

322

Paper

324

Printing

325

Chemicals

326

Plastics and Rubber Products

327

Nonmetallic Mineral Products

331

Primary Metal Products

332

Fabricated Metal Products

333

Machinery, Except Electrical

334

Computer and Electronic Parts

335

Electrical Equipment

336

Transportation Equipment

337

Furniture and Fixtures

421

Wholesale Trade, Durable Goods

422

Wholesale Trade, Nondurable Goods

POP

Population

Indiana developed equations for each of the two-digit Standard Classification of Transported Goods (SCTG) commodity categories used in the CFS. The production equations are shown in Table 4.6. In almost all instances in these equations, the employment variable in the production equation is related to the related industry producing the commodity. The equations produce annual thousands of tons of freight shipment by all modes. For example, according to the regression developed from the Indiana CFS data as shown in Table 4.6, each employee in the Chemical Industry (NAICS 324) produces 3,151 tons of Chemicals (SCTG 20) for shipment each year, with a “goodness of fit” (R-squared) of 78.2 percent.

Table 4.6    Indiana Freight Model Production Equations
Thousands of Annual Tons

SCTG

Name

Coefficient Times (NAICS3 Employment)
[See Table 4.2]

Degrees of Freedom

R‑Squared

1

Live Animals and Fish

0.003*(331)+.007*(337)

22

0.498

2

Cereal Grains

0.256*(311)

36

0.337

3

Other Agricultural Products

0.135*(311)

34

0.647

4

Animal Feed

0.149*(311)

41

0.772

5

Meat, Fish, Seafood

0.054*(311)

42

0.880

6

Milled Grain Products

0.045*(311)+0.027*(333)

43

0.853

7

Fats and Oils

0.000748*(Pop)+0.141*(335)+0-083*(311)

46

0.964

8

Alcoholic Beverages

0.0002188*(Pop)+0.013*(334)

46

0.882

9

Tobacco Products

0.009*(313)+0.005*(337)

19

0.690

10

Building Stone

0.016*(422)+0.0001118*(Pop)+0.005*(331)

22

0.919

11

Natural Sands

0.087*(421)

28

0.839

12

Gravel and Crushed Stone

0.835*(326)+1.145*(314)+0.443*(311)

40

0.940

13

Nonmetallic Minerals

0.226*(325)

29

0.507

14

Metallic Ores

Not Applicable

[no data]

[no data]

15

Coal

7.34*(212)

30

0.604

17

Gasoline and Fuel

7.812*(324)

44

0.873

18

Fuel Oils

4.017*(324)

45

0.939

19

Products of Petroleum

3.388*(324)+0.142*(325)

41

0.918

20

Basic Chemicals

3.151*(324)

43

0.782

21

Pharmaceutical Products

0.011*(337)+0.007*(313)

35

0.793

22

Fertilizers

0.00081*(Pop)

35

0.304

23

Chemical Products

0.025*(332)+0.017*(325)

44

0.790

24

Plastics and Rubber

0.912*(324)

46

0.709

25

Logs and Rough Wood

0.667*(321)

21

0.518

26

Wood Products

0.544*(321)

44

0.826

27

Pulp Paper

0.225*(322)+0.058*(324)

44

0.810

28

Paper Products

0.029*(311)+0.015*(334)+0.053*(314)

45

0.931

29

Printed Products

0.024*(422)+0.040*(322)

43

0.946

30

Textiles and Leather

0.101*(314)+0.051*(313)+0.058*(324)

44

0.970

31

Nonmetallic Minerals

0.002*(Pop)+0.248*(311)

45

0.909

32

Base Metal

0.356*(331)+0.080*(336)

45

0.911

33

Fabricated Base Metal

0.030*(332)+0.266*(324)+0.033*(327)

45

0.949

34

Machinery

0.019*(333)+0.026*(326)

47

0.897

35

Electrical Equipment

0.017*(332)+0.074*(324)

46

0.913

36

Vehicles

0.061*(336)

44

0.798

37

Transportation Equipment

0.008*(331)

33

0.620

38

Precision Instruments

0.001*(421)

39

0.826

39

Furniture

0.020*(337)+0.004*(336)

45

0.918

40

Miscellaneous Manufacture

0.000183*(Pop)+0.066*(314)+0.022*(311)

39

0.946

41

Waste and Scrap

0.099*(332)

37

0.931

43

Mixed Freight

0.0004*(Pop)

38

0.905

The attraction equations are related to the industries that consume commodities. Although it is possible to test all possible employment by industry to determine the statistically most significant industries, that effort may be considerable. To assist in the development of these equations, candidate industries, as well as population for consumer goods that will be tested in the regression, are identified by examining national input-output models. Indiana developed equations for each of the SCTG two-digit commodity categories.  The attraction equations are shown in Table 4.7. For example, according to the regression developed from the Indiana CFS data shown in Table 4.7, each employee in the Food Manufacturing Industry (NAICS 311) consumes 315 tons of Base Metal (SCTG 32) for shipment each year and each employee in the Transportation Equipment Industry (NAICS 336) consumes 79 tons, with a “goodness of fit” (R-squared) of 91.1 percent. It must be noted that it is not the point of this manual to justify these equations or relationships, nor to suggest that they are transferable to other regions, only to suggest that these are the findings for this freight model. It may be that these relationships indicate commodities being consumed that are locally prominent but not obvious unless more detailed information on commodity shipments, (i.e., shipment information for more digits in the hierarchical commodity classification system) is available. It also may be that the correlation is merely a spurious statistical aberration or a correlation with another more meaningful variable. Those developing the models should be aware of these concerns before choosing the variables to be used.

Table 4.7    Indiana Freight Model Attraction Equations
Thousands of Annual Tons

SCTG

Name

Coefficient Times (NAICS3 Employment)
[See Table 4.2]

Degrees of Freedom

R‑Squared

1

Live Animals and Fish

0.004*(311)

18

0.488

2

Cereal Grains

2.724 *(324)

37

0.399

3

Other Agricultural Products

1.196*(324)

45

0.504

4

Animal Feed

0.148*(311)

45

0.839

5

Meat, Fish, Seafood

0.030 *(311)+0.00015 *(Pop)+0.0004 *(336)

48

0.971

6

Milled Grain Products

0.00018 *(Pop)+0.025 *(311)+0.022 *(325)

47

0.980

7

Fats and Oils

0.000903 *(Pop)+0.068 *(311)+0.104 *(322)

48

0.986

8

Alcoholic Beverages

0.000250*(Pop)+0.008*(334)+0.023*(315)+0.078*(312)

47

0.984

9

Tobacco Products

0.008*(313)+0.004*(337)

44

0.732

10

Building Stone

0.015*(325)

22

0.688

11

Natural Sands

0.00121*(Pop)

30

0.899

12

Gravel and Crushed Stone

0.395*(311)+1.237*(314)+0.903*(331)+2.003*(312)

41

0.966

13

Nonmetallic Minerals

0.338*(322)

37

0.628

14

Metallic Ores

0.172*(331)

29

0.651

15

Coal

3.472*(212)+0.727*(311)

42

0.847

17

Gasoline and Fuel

4.60*(3,241+0.00169*(Pop)

44

0.912

18

Fuel Oils

3.237*(324)+0.110*(325)

47

0.943

19

Products of Petroleum

2.936*(324)+0.199*(325)

44

0.899

20

Basic Chemicals

3.218*(324)+0.050*(334)

46

0.865

21

Pharmaceutical Products

0.006*(325)+0.002*(422)

48

0.866

22

Fertilizers

0.000653*(Pop)

40

0.372

23

Chemical Products

0.000104*(Pop)+0.208*(324)+0.061*(314)+0.026*(326)

47

0.965

24

Plastics and Rubber

0.041*(325)+0.295*(324)+0.027*(333)+0.062*(314)

45

0.931

25

Logs and Rough Wood

0.683*(321)

33

0.555

26

Wood Products

0.494*(321)+0.391*(324)

47

0.908

27

Pulp Paper

0.043*(311)+0.123*(322)+0.122*(324)

47

0.970

28

Paper Products

.00007030*(Pop)+0.017*(334)+0.021*(311)

48

0.951

29

Printed Products

0.000295*(Pop)

45

0.964

30

Textiles and Leather

0.000041*(Pop)+0.079*(314)+0.032*(313)+0.058*(324)

47

0.983

31

Nonmetallic Minerals

0.00177*(Pop)+0.227*(311)

47

0.918

32

Base Metal

0.315*(311)+0.079*(336)

47

0.911

33

Fabricated Base Metal

0.428*(324)+0.035*(333)

46

0.927

34

Machinery

0.015*(333)+0.009*(336)+0.013*(325)

47

0.939

35

Electrical Equipment

0.000076*(Pop)+0.076*(324)+0.011*(326)

48

0.957

36

Vehicles

0.053*(336)

48

0.860

37

Transportation Equipment

0.035*(324)

39

0.723

38

Precision Instruments

0.000415*(421)+0.001848*(314)+0.000442*(422)

48

0.959

39

Furniture

0.000068*(Pop)

48

0.899

40

Miscellaneous Manufacture

0.000235*(Pop)+0.031*(321)+0.014*(313)

44

0.946

41

Waste and Scrap

0.051*(332)+0.066*(331)+0.037*(311)

40

0.941

43

Mixed Freight

0.000356*(Pop)+0.036*(314)

46

0.924

Florida developed trip-generation equations using the 1998 TRANSEARCH data for Florida as the sample survey and employment by SIC industry for counties as the independent variable in the regression. Florida developed equations not for all commodities in the TRANSEARCH database, but only for those commodities it determined to be the most important commodities in Florida as shown in Table 4.8.  The production equations are shown in Table 4.9. In almost all instances, the employment variable in the production equation is related to the industry producing the commodity. The equations produce annual tons of freight flows, by all modes. For example, according to the regression developed on the Florida TRANSEARCH data shown in Table 4.9, each employee in the Chemical Industry (SIC 28) produces 678 tons of Chemicals (STCC 20) for shipment each year, with a “goodness of fit” (R-squared) of 60.9 percent.

Table 4.8    Florida Freight Model Commodity Groups

Commodity Group Code

Commodity Group Name

STCC Codes in
Commodity Group

Actual Production Tonnage

Actual Attraction Tonnage

1

Agricultural Products

1, 7, 8, 9

5,502,692

3,368,257

2

Minerals

10, 13, 14, 19

50,450,949

49,485,912

3

Coal

11

3,113,832

26,316,127

4

Food

20

21,528,927

23,389,919

5

Nondurable Manufacturing

21, 22, 23, 25, 27

3,778,169

4,456,032

6

Lumber

24

9,906,141

13,916,051

7

Chemicals

28

5,482,657

5,090,377

8

Paper

26

27,683,647

32,411,062

9

Petroleum Products

29

5,438,235

41,896,320

10

Other Durable Manufacturing

30, 31, 33-39

6,969,684

13,199,839

11

Clay, Concrete, Glass, and Stone

32

53,193,380

56,777,305

12

Waste

40

5,537,231

4,663,125

13

Miscellaneous Freight

41-47, 5,020, 5,030

3,462,632

5,991,052

14

Warehousing

5,010

69,759,287

70,051,969

Table 4.9    Florida Freight Model Production Equations

Commodity Groups:
Code

Commodity Groups:
Name

Coefficient

Variable

R‑Squared

1

Agricultural

45.957

SIC07

0.409

2

Nonmetallic Minerals

6,977.771

SUM(SIC10-14)

0.738

3

Coal

0.000

No Production in Florida

[no data]

4

Food

245.464

SIC20

0.743

5

Nondurable Manufacturing

18.024

SUM(SIC21, 22, 23, 25, 27)

0.963

6

Lumber

241.464

SIC24

0.535

7

Chemicals

678.583

SIC28

0.609

8

Paper

190.814

SIC26

0.643

9

Petroleum Products

795.117

SIC29

0.573

10

Other Durable Manufacturing

23.578

SUM(SIC30, 31, 33-39)

0.696

11

Clay, Concrete, Glass

1,498.501

SIC32

0.704

12

Waste

0.500

TOTEMP

0.393

13

Miscellaneous Freight

0.599

SUM(SIC42, 44, 45)

0.436

14

Warehousing

157.426

SUM(SIC50, 51)

0.766

The attraction equations are functions of the industries that consume commodities. Florida developed equations for each of the 14 commodity categories shown in Table 4.8, identifying candidate industries to be tested by examining an input-output model.  The attraction equations are shown in Table 4.10. For example, according to the regression developed from the Florida TRANSEARCH data, each employee in the Nondurable Warehousing Industry (SIC 51) consumes (receives) 109 tons of Food Products (STCC 20) each year, with a “goodness of fit” (R-squared) of 89.1 percent. It must be noted that it is not the point of this manual to justify these equations or relationships, nor to suggest that they are transferable to other regions, only to suggest that these are the findings for this freight model.

Table 4.10  Attraction Equations

Commodity Groups:
Code

Commodity Groups:
Name

Coefficient 1

Variable 1

Coefficient 2

Variable 2

R‑Squared

1

Agricultural

23.537

SIC20

[no data]

[no data]

0.479

2

Nonmetallic Minerals

1,461.302

SIC28

[no data]

[no data]

0.556

3

Coal

178.639

SIC49

[no data]

[no data]

0.008

4

Food

109.51

SIC51

[no data]

[no data]

0.891

5

Nondurable Manufacturing

24.698

SIC51

[no data]

[no data]

0.873

6

Lumber

147.624

SIC25

0.448

Pop

0.877

7

Chemicals

83.247

SIC51

[no data]

[no data]

0.891

8

Paper

23.924

SIC51

[no data]

[no data]

0.852

9

Petroleum Products

0.228

Pop

[no data]

[no data]

0.864

10

Other Durable Manufacturing

46.762

SIC 50

[no data]

[no data]

0.837

11

Clay, Concrete, Glass

2.964

Pop

[no data]

[no data]

0.930

12

Waste

68.089

SIC33

[no data]

[no data]

0.263

13

Miscellaneous Freight

0.962

SUM(SIC42, 44, 45)

[no data]

[no data]

0.072

14

Warehousing

2.926

Pop

[no data]

[no data]

0.572

Wisconsin developed trip-generation equations using the 2001 TRANSEARCH data as the sample survey and county employment by SIC industry as the independent variable in the regression. Wisconsin developed equations for the commodities determined to be the most important for Wisconsin.  The production equations are shown in Table 4.11. In almost all instances, the employment variable in the production equation is related to the same industry producing the commodity. The equations produce annual tons of freight flows by all modes. For example, according to the regression developed on the Wisconsin TRANSEARCH data, each employee in the Chemical Industry (SIC 28) produces 476 tons of Chemicals (STCC 20) for shipment each year, with a “goodness of fit” (R-squared) of 81 percent.

Table 4.11  Wisconsin Freight Model Trip Production and Attraction Regression Models

Commodity

Trip Production:
Production Coefficient

Trip Production:
Production
Variables

Trip Production:
R‑Squared

Trip Attraction:
Attraction Coefficient

Trip Attraction:
Attraction Variables

Trip Attraction:
R‑Squared

Farm and Fish

767.90

SIC01, SIC02, SIC07, SIC09

0.20

31.07

SIC20, SIC54

0.27

Forest Products

[no data]

[no data]

[no data]

[no data]

[no data]

[no data]

Metallic Ores

[no data]

[no data]

[no data]

[no data]

[no data]

[no data]

Coal

[no data]

[no data]

[no data]

[no data]

[no data]

[no data]

Nonmetallic Minerals, Except Fuels

701.05

SIC14, SIC15, SIC16, SIC17

0.63

898.32

SIC14, SIC15, SIC16, SIC17

0.95

Food or Kindred Products

325.17

SIC20

0.85

2.13

Population

0.71

Lumber or Wood Products

422.85

SIC24

0.49

168.54

SIC24, SIC25, SIC50

0.60

Pulp, Paper, or Allied Products

197.10

SIC26

0.91

97.42

SIC26, SIC27

0.79

Chemicals

476.18

SIC28

0.81

5.80

Total Employment

0.81

Petroleum or Coal Products

[no data]

[no data]

[no data]

2.52

Population

0.87

Clay, Concrete, Glass, and Stone

2,023.11

SIC32

0.61

6.26

Population

0.84

Primary Metal Products

200.38

SIC33

0.85

36.73

SIC33, SIC34

0.23

Fabricated Metal Products

83.102

SIC34

0.88

0.55

Population

0.90

Transportation Equipment

63.29

SIC37

0.36

10.34

SIC42

0.42

Waste or Scrap Materials

0.46

Population

0.78

[no data]

[no data]

[no data]

Secondary Warehousing

447.00

SIC42

0.69

6.83

Population

0.85

Rail Drayage

[no data]

[no data]

[no data]

[no data]

[no data]

[no data]

Other Minerals

[no data]

[no data]

[no data]

[no data]

[no data]

[no data]

Furniture or Fixtures

13.17

SIC25

0.47

0.05

Population

0.72

Printed Matter

75.01

SIC27

0.66

0.46

Total Employment

0.92

Other Nondurable Manufacturing Goods

9.49

SIC21, SIC22, SIC23

0.33

0.11

Population

0.38

Other Durable Manufacturing Goods

21.87

SIC30, SIC31, SIC35, SIC36, SIC38, SIC39

0.95

40.93

SIC50

0.59

Miscellaneous Freight

[no data]

[no data]

[no data]

[no data]

[no data]

[no data]

Hazardous Materials

[no data]

[no data]

[no data]

[no data]

[no data]

[no data]

Air Drayage

[no data]

[no data]

[no data]

[no data]

[no data]

[no data]

The attraction equations are related to the industries that consume commodities. The candidate industries tested in the regression were identified through examination of an input-output model. Indiana developed equations for each of the 24 commodity categories shown in Table 4.11.  For example, according to the regression developed on the Wisconsin TRANSEARCH data, each person (Population) consumes two-tons of Food Products (STCC 20) shipments each year, with a “goodness of fit” (R-squared) of 71 percent. It must be noted that it is not the point of this manual to justify these equations or relationships, nor to suggest that they are transferable to other regions, only to suggest that these are the findings for this freight model.

In comparing the production equation for the same commodity, Chemicals, in Indiana, Florida, and Wisconsin, the coefficients are different, reflecting the unique composition of industries in each state. This suggests further that it is more appropriate to transfer the methods, not the rates, to other states. The same is true for the attraction equations. In comparing the attraction equations for Florida and Wisconsin, which use the same commodity and industry classifications, the independent variable in Florida was chosen to be Warehousing Employment, while in Wisconsin it was chosen to be Population. Both findings may be appropriate and reflect local business patterns, further reinforcing that it is the method not the coefficients that would be transferable.

However, the trip-generation equations for the three states shown do indicate that it is possible to develop meaningful equations of commodity flow based on employment, as shown by the high R‑squared values. The types and ranges of the coefficient can guide developers of other models.

In order to use these models as forecasting tools, it is necessary to have a forecast of zonal employment for these same industries. This information may not be directly available but may be estimated through application of current shares and local knowledge of planned industry growth to less detailed industry forecasts. Also, the coefficients in these equations are based on current labor productivity (the amount of goods produced or consumed by an employee). As industrial processes change, labor becomes more productive and those productivity increases may be known for individual industries. The relative growth in productivity between the base and forecast years should be applied to the coefficients in these equations when developing forecasts.

4.3.5  Special Generators at Intermodal Terminals

The development of trip-generation equations from employment is based on the assumption that freight shipments to and from a zone are related to the industrial activity associated with a commodity. It is possible to have freight activity in a zone when there is little or no activity in related industries. These zones by commodity will be easily identifiable as outlier data points in the development of the trip generation regressions. The fact that these zones are outliers does not mean that the data are incorrect. It may indicate that the commodity productions or attractions in that zone may need to be treated as a special generator.

The need for special generators also will depend on whether the commodity flow survey being used as the sample survey is a database of unlinked commodity trips (e.g., TRANSEARCH) or linked trips (e.g., CFS). When the database is unlinked, records will begin or end not only at the ultimate producing and consuming zones, but also at intermodal transfer points, such as intermodal rail yards, ports, or airports. Since there will likely be no industrial employment associated with these intermediate zones, they will need to be treated as special generators. When the database includes linked modal trips, for example for the “rail-truck” mode in the CFS, the freight flows begin or end in the ultimate producing and consuming zones, but there is no information on where the intermodal exchange is made. Even in these linked trip databases, international gateways, such as border crossings and ports, may still need to be treated as special generators.

The Florida and Wisconsin models were developed using the TRANSEARCH database as the sample survey. The magnitude of the special generator issue from each of these freight models is shown in Tables 4.12 through 4.14. For these zones, since the trip-generation equations could not be used, forecasts need to be obtained from local officials, such as facility operators in the case of terminal facilities. The productions and attractions for these special generators are added to the productions and attractions developed in the trip generation step. The special generators listed in Tables 4.12 through 4.14 are not meant to be transferable, only an indication of the method, scale, and type of commodities that might be encountered in developing state-freight models.

Table 4.12  Florida Freight Model Productions and Attractions for Ports and Terminals
(Annual, Thousands of Tons)

Commodity Code

Description

Productions

Attractions

01

Agricultural

463

478

02

Nonmetallic Minerals

8,813

8,814

03

Coal

9,300

9,300

04

Food

4,386

3,212

05

Nondurable Manufacturing

891

1,233

06

Lumber

204

285

07

Chemicals

1,491

713

08

Paper

11,977

9,060

09

Petroleum Products

2,369

46,396

10

Other Durable Manufacturing

3,196

3,410

11

Clay, Concrete, and Glass

8,391

8,391

12

Waste

644

644

13

Miscellaneous Freight

2,083

2,084

14

Warehousing

18,391

22,608

Total

[no data]

72,600

116,628

Table 4.13  Wisconsin Freight Model Freight Outbound Special Generators and Tonnages

Commodity

Special Production
County Name

Tonnage

Comments (Possible Cause for Special Generation)

Farm and Fish

Lacrosse

2,173,173

Port, Reinhardt

Farm and Fish

Portage

1,901,216

Potatoes, crops

Forest Products

Very low tonnage

Total Tonnage for WI = 18,332

Provide single growth factor for State of Wisconsin

Metallic Ores

Brown

2,371,126

Port of Green Bay

Metallic Ores

Douglas

1,587,324

Port of Duluth Superior

Metallic Ores

La Crosse

668,395

Port of LaCrosse

Metallic Ores

Milwaukee

140,055

Port of Milwaukee

Coal

Douglas

12,444,327

Port of Duluth Superior

Nonmetallic Minerals

Milwaukee

21,792,428

Sand and gravel pits

Food or Kindred Products

No special generators

[no data]

[no data]

Lumber or Wood Products

Douglas

1,627,383

Port of Duluth Superior

Lumber or Wood Products

Sheboygan

1,503,401

[no data]

Pulp, Paper, or Allied Products

No special generators

[no data]

[no data]

Chemicals

No special generators

[no data]

[no data]

Petroleum or Coal Products

Douglas

2,717,057

[no data]

Petroleum or Coal Products

Waukesha

1,967,124

[no data]

Petroleum or Coal Products

Milwaukee

960,732

[no data]

Petroleum or Coal Products

Dane

833,881

[no data]

Petroleum or Coal Products

Winnebago

729,764

[no data]

Petroleum or Coal Products

Ashland

542,406

[no data]

Petroleum or Coal Products

Portage

540,501

[no data]

Petroleum or Coal Products

Brown

145,156

[no data]

Petroleum or Coal Products

Outagamie

117,685

[no data]

Clay, Concrete, Glass, or Stone Products

Milwaukee

9,525,503

Port of Milwaukee

Primary Metal Products

Rock

1,040,050

Many manufacturers

Fabricated Metal Products

Milwaukee

1,372,127

Many manufacturers

Transportation Equipment

Rock

1,108,735

GM plant

Waste or Scrap Materials

Grant

2,600,812

Fly ash (power plants)

Waste or Scrap Materials

Douglas

497,483

Scrap

Waste or Scrap Materials

Brown

326,510

[no data]

Ware Housing Secondary

Outagamie

9,426,510

[no data]

Rail Drayage

Milwaukee

1,296,539

Chicago intermodal

Rail Drayage

Winnebago

1,011,177

Chicago intermodal

Rail Drayage

Brown

923,456

Chicago intermodal

Rail Drayage

Trempealeau

228,971

Ashley intermodal

Rail Drayage

Dane

184,334

Chicago intermodal

Rail Drayage

Waukesha

164,566

Chicago intermodal

Rail Drayage

La Crosse

111,985

Twin Cities and Chicago

Rail Drayage

Rock

106,424

Chicago intermodal

Other Minerals

Very low tonnage

Total Tonnage = 53,629

[no data]

Furniture or Fixtures

Trempealeau

83,462

[no data]

Printed Matter

Milwaukee

378,646

[no data]

Other Nondurable Manufacturing Products

Milwaukee

34,248

[no data]

Other Durable Manufacturing Products

No special generators

[no data]

[no data]

Miscellaneous Freight

Milwaukee

182,492

[no data]

Miscellaneous Freight

Brown

115,303

[no data]

Miscellaneous Freight

Trempealeau

111,000

[no data]

Air Drayage

Very low tonnage

Total Tonnage = 116,931

Provide single growth factor for State of Wisconsin

Hazardous Materials

Very low tonnage

Total Tonnage = 17

Provide single growth factor for State of Wisconsin

Table 4.14  Wisconsin Freight Model Freight Inbound Special Generators and Tonnages

Commodity

Special Attractor
County Name

Tonnage

Comments (Possible Cause for Special Generation)

Farm and Fish

Douglas

5,560,394

Port of Duluth Superior

Farm and Fish

Jefferson

1,435,994

Livestock

Forest Products

Very low tonnage

Total Tonnage = 18,332

Provide single growth factor for State of Wisconsin

Metallic Ores

Douglas

9,342,656

Port of Duluth Superior

Coal

Douglas

16,322,948

Port of Duluth Superior

Coal

Kenosha

5,167,839

Pleasant Prairie

Coal

Columbia

4,476,274

Columbia Plant

Coal

Sheboygan

3,542,520

Edgewater Plant

Coal

Milwaukee

3,512,500

Oak Creek Power Plant

Coal

Marathon

2,354,536

Weston Power Plant

Coal

Buffalo

1,809,495

Power Plant @ Alma

Coal

Brown

1,641,810

Pulliam, De Pere

Nonmetallic Minerals

Outagamie

26,160,819

[no data]

Nonmetallic Minerals

Winnebago

14,371,919

[no data]

Food or Kindred Products

Milwaukee

5,683,901

[no data]

Lumber or Wood Products

Milwaukee

5,740,909

[no data]

Pulp, Paper, or Allied Products

Milwaukee

3,965,026

Wholesale/retail/printing

Chemicals

Milwaukee

3,467,274

General manufacturing

Petroleum or Coal Products

No special generators

[no data]

[no data]

Clay, Concrete, Glass, or Stone Products

No special generators

[no data]

[no data]

Primary Metal Products

Milwaukee

2,966,812

Raw steel/iron

Primary Metal Products

Rock

806,748

[no data]

Fabricated Metal Products

Milwaukee

2,479,333

Processed metal products

Fabricated Metal Products

Rock

315,205

GM, Stoughton Trlr, others

Transportation Equipment

Milwaukee

881,894

Automobile dealerships

Transportation Equipment

Rock

795,415

GM – components

Waste or Scrap Materials

Grant

471,001

[no data]

Waste or Scrap Materials

Douglas

454,251

[no data]

Waste or Scrap Materials

Milwaukee

367,036

[no data]

Waste or Scrap Materials

Brown

222,142

[no data]

Waste or Scrap Materials

Buffalo

178,566

[no data]

Waste or Scrap Materials

Waushara

165,471

[no data]

Warehousing Secondary

No special generators

[no data]

[no data]

Rail Drayage

Milwaukee

1,173,005

[no data]

Rail Drayage

Winnebago

947,578

[no data]

Rail Drayage

Brown

786,440

[no data]

Rail Drayage

Trempealeau

162,427

[no data]

Rail Drayage

Dane

161,237

[no data]

Rail Drayage

Waukesha

148,886

[no data]

Other Minerals

Very low tonnage

Total Tonnage = 53,993

[no data]

Furniture or Fixtures

Milwaukee

364,949

[no data]

Printed Matter

Milwaukee

837,500

Quad Graphics, Journal-Sentinel

Other Nondurable Manufacturing Products

Milwaukee

594,710

[no data]

Other Durable Manufacturing Products

Milwaukee

3,598,312

[no data]

Miscellaneous Freight

Trempealeau

234,632

[no data]

Miscellaneous Freight

Milwaukee

139,136

[no data]

Miscellaneous Freight

Brown, Wisconsin

90,240

[no data]

Miscellaneous Freight

Crawford

43,238

[no data]

Miscellaneous Freight

Grant

21,957

[no data]

Miscellaneous Freight

Winnebago

14,254

[no data]

Air Drayage

Very low tonnage

Total Tonnage = 146,793

[no data]

Hazardous Materials

Very low tonnage

Total Tonnage =  447

[no data]

 

4.3.6  Trip Distribution

The market for freight trips in commodity freight forecasting will be national, if not international, in scope. The trip distribution equation to be used will most often be a gravity model. Gravity model programs are included in virtually all of the major travel demand model software packages. The gravity model uses the zonal productions and attractions, which were calculated in the trip generation and special-generator steps, and the difficulty of travel or friction factor for the travel between the production and attraction zone:

T subscript ij equals, open parenthesis, P subscript i times A subscript j times F subscript ij, close parenthesis, divided by, open parenthesis, sum of A subscript j times F subscript ij from j equals 1 to n, close parenthesis)

where:

Tij = trips (volume in tons) originating at analysis area i and destined to analysis area j;

Pi = total trips produced/originating at i;

Aj = total trips attracted destined at j;

Fij = friction factor for trip interchange ij;

i   = origin analysis area number, i = 1, 2, 3... n;

j   = destination analysis area number, j = 1, 2, 3... n; and

n  = number of analysis areas.

and further:

F subscript ij equals e to the power of negative, open parenthesis, 1 divided by k, close parenthesis, times t subscript ij

where:

k  = average distance between all zones;

tij = a measure of the travel impedance between i and j, expressed in miles or time; and

= the exponential natural constant.

In freight forecasting, each commodity will have a different average distance travel and hence a different coefficient for the trip distribution equation. This average distance can be easily calculated for each commodity from the database. What it also requires is a table of the travel impedance between the zones that is easily obtained from the transportation network. From these distances, it is easy to calculate the ton-miles (or ton-hours, if the impedance unit is travel time) for each zone, to sum these ton-miles for a commodity over all zones and to then divide by the totals tons for all zones, for that commodity. When the friction factor is an exponential distribution, the average distance can be used as the coefficient in that equation.

As can be seen in Tables 4.15 through 4.18, the average distances traveled by most commodities is measured in hundreds of miles. These average distances are beyond the scope of most existing state passenger model boundaries and explain why the scale of the geography for freight models must be national. The tables from the three models also indicate that the coefficients in trip distribution are not readily transferable. For Paper Products, the average distance traveled is 299 miles in Indiana (SCTG 27), 649 miles in Florida, and 922 miles in Wisconsin. These distances are appropriate for use in these models, but the differences show how the distribution patterns differ with the local economy. In the Florida freight model, impedance variables were tested for both time and distance. Only for those commodities that travel a short distance, such as Concrete and Warehousing, did time provide a better variable for impedance.

Table 4.15  Indiana Freight Model Trip Distribution Model Coefficients

SCTG

Commodity

Mean Distance

Modeled Coefficient

1

Live Animals and Fish

253

-0.0040

2

Cereal Grains

410

-0.0024

3

Other Agricultural Products

400

-0.0025

4

Animal Feed

213

-0.0047

5

Meat, Fish, Seafood

458

-0.0022

6

Milled Grain Products

472

-0.0021

7

Fats and Oils

313

-0.0032

8

Alcoholic Beverages

343

-0.0029

9

Tobacco Products

245

-0.0041

10

Building Stone

93

-0.0108

11

Natural Sands

58

-0.0172

12

Gravel and Crushed Stone

51

-0.0196

13

Nonmetallic Minerals

222

-0.0045

14

Metallic Ores

526

-0.0019

15

Coal

446

-0.0022

17

Gasoline and Fuel

106

-0.0094

18

Fuel Oils

172

-0.0058

19

Products of Petroleum

462

-0.0022

20

Basic Chemicals

564

-0.0018

21

Pharmaceutical Products

243

-0.0041

22

Fertilizers

489

-0.0020

23

Chemical Products

530

-0.0019

24

Plastics and Rubber

76

-0.0132

25

Logs and Rough Wood

294

-0.0034

26

Wood Products

549

-0.0018

27

Pulp Paper

299

-0.0033

28

Paper Products

292

-0.0034

29

Printed Products

538

-0.0019

30

Textiles and Leather

100

-0.0100

31

Nonmetallic Minerals

350

-0.0029

32

Base Metal

457

-0.0022

33

Fabricated Base Metal

542

-0.0018

34

Machinery

683

-0.0015

35

Electrical Equipment

464

-0.0022

36

Vehicles

686

-0.0015

37

Transportation Equipment

738

-0.0014

38

Precision Instruments

581

-0.0017

39

Furniture

354

-0.0028

40

Miscellaneous Manufacture

225

-0.0044

Table 4.16  Florida Freight Model Trip Distribution Results

Commodity Group

Average Impedance

Coincidence Ratio

Adjusted R-Squared

Agricultural

1,254 (miles)

0.752

0.775

Minerals

311 (miles)

0.503

0.396

Coal

818 (miles)

0.452

0.449

Food

659 (miles)

0.833

0.646

Nondurable Manufacturing

555 (miles)

0.870

0.959

Lumber

581 (miles)

0.820

0.645

Paper

649 (miles)

0.826

0.749

Chemicals

754 (miles)

0.741

0.743

Petroleum Products

1,078 (miles)

0.855

0.988

Durable Manufacturing

917 (miles)

0.813

0.713

Clay, Concrete, Glass

263 (free flow minutes)

0.790

0.832

Non-Municipal Waste

959 (miles)

0.546

0.661

Miscellaneous Freight

928 (miles)

0.625

0.743

Warehousing

411 (free flow minutes)

0.820

0.936

Table 4.17  Wisconsin Freight Model Average Trip Lengths by Commodity

Number

Commodity Group

Average
Trip Length

Friction Factor between
Zones i and j

1

Farm and Fish

731.96

exp(-distance i-j/731.96)

2

Forest Products

1,644.20

exp(-distance i-j/1,644.2)

3

Metallic Ores

586.82

exp(-distance i-j/586.82)

4

Coal

830.67

exp(-distance i-j/830.67)

5

Nonmetallic Minerals, Except Fuels

153.12

exp(-distance i-j/153.12)

6

Food or Kindred Products

784.93

exp(-distance i-j/784.93)

7

Lumber or Wood Products

780.78

exp(-distance i-j/780.78)

8

Pulp, Paper, or Allied Products

922.64

exp(-distance i-j/922.64)

9

Chemicals

956.15

exp(-distance i-j/956.15)

10

Petroleum or Coal Products

541.74

exp(-distance i-j/541.74)

11

Clay, Concrete, Glass, and Stone

451.74

exp(-distance i-j/451.74)

12

Primary Metal Products

694.71

exp(-distance i-j/694.71)

13

Fabricated Metal Products

816.90

exp(-distance i-j/816.9)

14

Transportation Equipment

1,070.93

exp(-distance i-j/1,070.93)

15

Waste or Scrap Materials

565.88

exp(-distance i-j/565.88)

16

Secondary Warehousing

320.37

exp(-distance i-j/320.37)

17

Rail Drayage

134.29

exp(-distance i-j/134.29)

18

Other Minerals

1,601.01

exp(-distance i-j/1,601.01)

19

Furniture or Fixtures

1,496.62

exp(-distance i-j/1,496.62)

20

Printed Matter

734.23

exp(-distance i-j/734.23)

21

Other Nondurable Manufacturing Goods

1,590.87

exp(-distance i-j/1,590.87)

22

Other Durable Manufacturing Goods

1,271.49

exp(-distance i-j/1,271.49)

23

Miscellaneous Freight

1,547.35

exp(-distance i-j/1,547.35)

24

Hazardous Materials

2,779.89

exp(-distance i-j/2,779.89)

25

Air Drayage

100.07

exp(-distance i-j/100.07)

 

4.3.7  Mode Split

While the purpose of developing multimodal freight tables in the trip generation and trip distribution steps was to allow modes choice to be considered in the forecasting steps, in reality the ability to forecast mode choice is fairly limited. Information about the utility of each mode considered in the decision by shippers is limited. Information about the time, cost, and reliability for modes other than trucking are difficult to obtain, particularly since most state freight models still only include highway networks and do not include other modal networks that could be used to develop these characteristics. Finally, the modeling basis for most mode choice models, the logit choice equation, is based on the assumption that each shipping unit is a decision-maker. While the decision-maker for a person trip in a passenger model is an individual, the decision-maker for millions of tons of freight, the flow unit, may be a single individual and, therefore, the mathematical basis for the use of this equation may not be satisfied and the observed mode splits may reflect the business decisions of a few individuals. For all of these reasons, most state freight models assume that existing mode splits by commodity, modified qualitatively if at all, can be applied to forecast tables of multimodal freight flows.

The Indiana Freight Model developed a mode split step that was based solely on replicating the existing observed mode shares based on the distance between zones. However, since the distance between zones will not change, the mode choice step at this point defaults to applying an estimate of the observed mode shares by commodity to forecast freight bales.

The Florida Freight Model applies the existing mode share from air and water to forecast freight flows. It does attempt to estimate the mode split for truck and rail based on utilities for each mode estimated solely from the highway distances. Those estimated utilities are applied to a set of coefficients developed by applying the ALOGIT software to the TRANSEARCH database used as a revealed-preference survey.

The Wisconsin Freight Model makes no attempt to model changes in mode split. It applies the observations of mode split observed in the 2001 Wisconsin TRANSEARCH database to forecast of freight flows.

4.3.8  Conversion to Vehicles

The multimodal nature of state commodity models required that the units of freight flow be expressed in a term that was common to all modes. That unit in most models is annual flow in tons. Prior to assignment in modal networks, particularly when the freight flow will be combined with passenger flow, it is appropriate to convert the modal flow in tons to flow in vehicles. This is almost always done for the truck mode, it is done for the rail flow only if there is a rail network and railcar flow will be considered in that assignment.

The conversion of tons to vehicles is often referred to as payloads or density. It is typically based on observed loadings of freight by vehicle by commodity. This conversion can be considered analogous to the automobile occupancy factor used to convert person trips to automobiles. As expected the conversion factors differ by commodity, since each commodity will have different densities, shipment size, and handling characteristics and may use different truck body types. The payload factors may be developed from national data such as the Census Bureau’s VIUS, state records within VIUS, or from commercial vehicle surveys. The payload factors for Indiana are shown in Table 4.18 for both rail cars and trucks. These densities were modified from the Strategies Freight Transportation Analysis report for the State of Washington.

Table 4.18  Indiana Freight Model Commodity Density Values for Railcars and Trucks

SCTG

Commodity

Railcar Density
Tons per Car

Motor Carrier Density
Tons per Truck

1

Live Animals and Fish

9.77

3.9

2

Cereal Grains

96.63

30.1

3

Other Agricultural Products

86.79

22.3

4

Animal Feed

88.28

25.3

5

Meat, Fish, Seafood

74.41

18.6

6

Milled Grain Products

85.50

21.4

7

Fats and Oils

87.02

21.0

8

Alcoholic Beverages

87.31

21.0

9

Tobacco Products

45.75

18.3

10

Building Stone

100.00

25.4

11

Natural Sands

97.97

25.4

12

Gravel and Crushed Stone

97.97

24.1

13

Nonmetallic Minerals

100.44

23.4

14

Metallic Ores

95.91

21.4

15

Coal

109.36

22.0

17

Gasoline and Fuel

84.04

28.2

18

Fuel Oils

88.22

20.0

19

Products of Petroleum

73.66

23.5

20

Basic Chemicals

98.66

17.5

21

Pharmaceutical Products

Not Applicable

13.2

22

Fertilizers

101.81

27.4

23

Chemical Products

93.96

20.1

24

Plastics and Rubber

94.30

13.3

25

Logs and Rough Wood

64.11

29.2

26

Wood Products

82.41

24.2

27

Pulp Paper

82.75

23.5

28

Paper Products

70.90

17.2

29

Printed Products

Not Applicable

15.1

30

Textiles and Leather

14.17

13.3

31

Nonmetallic Minerals

98.64

21.2

32

Base Metal

91.47

18.4

33

Fabricated Base Metal

79.66

12.2

34

Machinery

49.77

13.8

35

Electrical Equipment

16.69

12.7

36

Vehicles

21.73

13.3

37

Transportation Equipment

41.36

12.1

38

Precision Instruments

Not Applicable

9.0

39

Furniture

15.00

10.7

40

Miscellaneous Manufacture

65.22

14.0

41

Waste and Scrap

79.86

20.0

43

Mixed Freight

32.45

14.2

The Florida Freight Model developed payload factors for trucks from the Florida data records in VIUS, as shown in Table 4.19. The payloads are inferred by comparing the loaded and empty weight of the truck and the percentage of miles driven for each commodity is taken from the records. The data records also include the range of the trip in miles, which makes it possible to develop payload factors that vary by distance range. The VIUS data records include information on the percentage of miles driven empty. For the Florida Freight Model since empty truck trips are not being explicitly modeled, the payload factor was adjusted to include empty miles. This adjustment ensures that the number of truck trips, if not the direction, is reflected in the model.

Table 4.19  Florida Freight Model Tonnage to Truck Conversion Factors

Commodity Group

Average Payload in Pounds:
On Road Average

Average Payload in Pounds:
Less Than 50 Miles

Average Payload in Pounds:
50‑100 Miles

Average Payload in Pounds:
100‑200 Miles

Average Payload in Pounds:
200‑500 Miles

Average Payload in Pounds:
More Than 500 Miles

Agricultural

16.36

9.20

18.14

21.95

19.48

17.79

Minerals

20.82

20.62

17.50

21.07

Not Applicable

23.00

Food Products

18.23

8.64

18.60

22.29

21.10

21.23

Nondurable Manufacturing

8.68

3.58

5.05

18.10

6.22

14.79

Lumber

14.03

4.70

25.19

22.39

28.32

24.16

Paper

15.11

11.32

9.96

19.86

17.00

18.48

Chemicals

16.59

11.61

20.75

19.62

23.46

18.66

Petroleum Products

21.04

19.55

25.52

27.32

21.85

17.33

Durable Manufacturing

11.38

5.12

6.97

18.72

19.21

17.23

Concrete, Clay, Glass, Stone

18.47

15.82

20.31

19.97

22.71

22.40

Non-Municipal Waste

12.90

10.28

17.03

16.15

23.07

21.03

Miscellaneous Freight

12.44

6.90

7.21

20.89

19.29

18.43

Warehousing

9.07

9.02

6.53

23.91

3.34

11.56

Average

14.21

9.97

12.02

20.57

19.61

18.80

The Wisconsin Freight Model developed payload factors for trucks from the Wisconsin data records in VIUS, as shown in Table 4.20 in the same fashion as Florida.

Table 4.20  Wisconsin Freight Model Truck Payload Factors by Commodity and Distance Class


Commodity Group

Description

Truck Payload Factor
(Tons per Truck):
0-50 Miles

Truck Payload Factor
(Tons per Truck):
50-100 Miles

Truck Payload Factor
(Tons per Truck):
100-200 Miles

Truck Payload Factor
(Tons per Truck):
200-500 Miles

Truck Payload Factor
(Tons per Truck):
More Than 500 Miles

1

Farm and Fish

10.64

12.66

14.25

15.86

18.48

2

Forest Products

13.16

18.59

20.74

21.67

21.47

3

Metallic Ores

23.69

19.68

23.05

23.05

23.05

4

Coal

23.70

19.70

23.00

23.05

23.05

5

Nonmetallic Minerals, Except Fuels

23.69

19.68

23.05

23.05

23.05

6

Food or Kindred Products

8.43

11.38

15.11

17.28

21.70

7

Lumber or Wood Products

12.15

12.26

14.30

18.18

18.27

8

Pulp, Paper, or Allied Products

20.35

13.46

17.04

17.99

19.02

9

Chemicals

6.32

7.73

14.87

18.21

24.14

10

Petroleum or Coal Products

7.15

9.81

22.50

21.97

27.56

11

Clay, Concrete, Glass, and Stone

13.13

12.28

13.75

13.41

17.09

12

Primary Metal Products

7.14

12.71

11.81

21.43

20.04

13

Fabricated Metal Products

8.22

11.85

15.92

21.16

21.22

14

Transportation Equipment

6.81

8.90

9.31

17.15

19.90

15

Waste or Scrap Materials

10.21

11.58

14.44

17.83

19.98

16

Secondary Warehousing

10.21

11.58

14.44

17.83

19.98

17

Rail Drayage

10.21

11.58

14.44

17.83

19.98

18

Other Minerals

9.94

7.90

10.99

19.93

22.39

19

Furniture or Fixtures

2.20

5.63

10.76

17.41

15.06

20

Printed Matter

20.35

13.46

17.04

17.99

19.02

21

Other Nondurable Manufacturing Goods

4.53

5.12

15.40

20.15

20.53

22

Other Durable Manufacturing Goods

6.61

14.38

12.36

17.13

16.57

23

Miscellaneous Freight

10.21

11.58

14.44

17.83

19.98

24

Hazardous Materials

10.21

11.58

14.44

17.83

19.98

25

Air Drayage

10.21

11.58

14.44

17.83

19.98

For all freight models, in addition to the conversion from tons to trucks or other modal vehicles, there is typically a conversion from annual flows to average daily flows that can be compared to daily vehicle assignments or counts. Each of the models shown use the same value for each commodity, 306 average working days per year (representing 52 weeks of 6 working days less 6 major holidays), although other values may be considered, such as varying the assumptions of working days per week between 5 and 7 and the number of holidays from 0 to 12. Although none of the freight models shown in this section do so, it is possible to use different annual to daily factors for each commodity; however, doing so increases the computational and data management issues.

4.3.9  Assignment

After the modal vehicle trip tables are assigned, the modal freight vehicle trip table can be assigned to the modal networks. While modal tons tables are created for each of the three state freight models discussed in this section, each of these models only assign the freight trucks. In each state, the rail tables are preserved in the event that rail networks are constructed and used at some future date.

At the time this manual was prepared, the Indiana Freight Model had not yet been integrated into the Indiana Statewide Travel Demand Model (ISTDM), and assignment results were not available.  The Florida Freight Model was assigned as part of an equilibrium mutliclass assignment with automobiles in the Florida Interstate Highway Model using congested times. No Passenger Car Equivalent was applied to trucks as part of the assignment. The results of the assignment are shown in Tables 4.21 and 4.22. As shown in these tables, the match between the modeled and the observed truck counts was quite good. The validation count locations were chosen as rural Interstates and other facilities where freight trucks were expected to represent most of all truck trips, excluding urban locations where other truck purposes that were not modeled would represent the majority of the observed trucks.

Table 4.21  Florida Freight Model State Line Volume/Count Ratio

Interstates Freeway

Model Volume

Observed Count

V/C

I‑75

10,175

9,600

1.06

I‑95

4,125

4,350

0.95

I‑10

4,062

4,450

0.91

Total

18,362

18,400

1.00

Table 4.22  Florida Freight Model Major Statewide Screenline Volume/Count Ratio

Screenline

Model Volume

Observed Count

V/C

North Central Statewide

26,559

30,016

0.88

Southeast Statewide

24,724

24,696

1.00

In the Wisconsin Freight Model freight, truck flows include only a subset of all heavy trucks reported in Wisconsin DOTs traffic count program, which is the Traffic Analysis Forecasting Information System (TAFIS). On rural Interstate facilities, where freight trucks predominate, the difference between observed truck volumes and TRANSEARCH freight trucks will be minimal. On urban highways, where urban activity generates significant additional trucking activity, the differences will be greater. Generally, it was determined from other sources that freight truck traffic at the state level in Wisconsin represents 60 percent or more of all truck VMT.

As with any truck count data, the TAFIS database does not distinguish between commodity and non-commodity truck volumes. It is, therefore, difficult to compare the modeled freight truck volumes directly with the TAFIS average annual daily truck counts. However, since the TAFIS truck counts are classified based on axle categories, it is possible to compare the modeled commodity truck volumes with TAFIS three-axle or higher truck counts. Admittedly, this was an approximate way of checking the reasonableness of modeled truck volumes, but in consultation with Wisconsin DOT, this was determined to be the best validation procedure given the data limitations. The comparison between the observed trucks and the assigned freight trucks volumes from the freight model was adjusted to reflect observed TAFIS truck length adjustments by functional classification. As shown in Table 4.23, the match between the assigned and observed truck VMT on a systems level was considered acceptable.

Table 4.23  Wisconsin Freight Model HPMS versus Model Truck VMT by Functional Class

FHWA
Functional Class

Functional Class Name

HPMS Total:
3-Axle and Truck DVMT

HPMS Total:
ST 4-Axle and Truck DVMT

2000 Model Commodity Truck VMT

Truck Length Difference Factor

2000 Predicted Commodity
Truck VMT After Length Adjustment

Percent of HPMS
3-Axle or Higher

Rural: 1

Interstate

3,528,471

3,387,684

2,761,871

82%

3,366,233

95%

Rural: 2

Other Principal Arterials (PA)

2,426,032

2,090,873

2,347,979

95%

2,467,805

102%

Urban: 11

Interstate

1,369,176

1,223,630

1,690,912

139%

1,213,852

89%

Urban: 12

Other Freeway/
Expressway

570,328

468,678

797,307

163%

488,147

86%

Urban: 14

Other PA

1,034,015

703,218

521,625

94%

555,703

54%

Urban: 1+11

Total Interstate

4,897,648

4,611,314

4,452,783

100%

4,580,086

94%

Urban: 2+12+14

Total PA

4,030,375

3,262,769

3,666,911

98%

3,511,655

87%

 

4.4 Site/Facility Planning

Site/facility planning is an essential component of a comprehensive freight planning process. A large fraction of freight traffic flows in a region move to and from freight facilities (manufacturing plants, warehouse/distribution centers, or intermodal transfer facilities). Consequently, development of a new facility or expansion of an existing facility can have a significant impact on the magnitude and spatial distribution of freight flows in a region.

Multimodal access route planning is one of the most important elements in a site/facility planning process. This involves predicting mode-specific freight traffic demand generated by the facility and using these predictions for planning the development of multimodal freight access routes to ensure efficient handling of demand by each access mode. Multimodal access routes provide the critical link between the facility and the larger transportation network, and consequently, any bottlenecks on access routes can have significant impacts on facility operations (for example, economic impacts associated with transportation delays).

The site/facility planning process can be subdivided into two broad steps thatinclude freight modeling and the planning applications step.  These steps are discussed in greater detail in the following sections, focusing specifically on planned sites/facilities. The planning approach for existing sites/facilities is relatively straightforward and involves collecting simple traffic counts and observing where and when these counts are taken, and using simple trend analysis or trip generation rates using existing counts to forecast freight flows on the network.

The freight modeling step in a site/facility planning process involves predicting freight flows generated by the facility on the surrounding transportation network. The steps involved in freight modeling include the following:

  • Data collection;
  • Network identification;
  • Trip generation;
  • Trip distribution; and
  • Traffic assignment.

The forecasting steps are followed by a planning analysis where the performance of the freight system associated with these forecasts.

4.4.1  Data Collection

This is the first step in the freight modeling process, which involves gathering all the information pertaining to the planned facility that will potentially feed into the freight modeling framework. The types of data collected include, but are not limited to, the following:

  • Type of facility (industrial, manufacturing, warehousing, retail, intermodal, etc.);
  • Facility size (land area, floor area, number of employees, etc.);
  • Types of commodities, products, and services produced and consumed;
  • Expected shipment volumes (weight, volume, value, etc.);
  • Frequency and schedules (timing) of shipping operations;
  • Types of carriers and vehicles used for transportation;
  • Location of markets for commodities/services produced and consumed (local, intercity, out-of-state, and international); and
  • Types and locations of intermediate facilities (warehouses, consolidation facilities, etc.) serving the planned facility.

Sources for obtaining data on the planned facility include the developer, designer, owner, contractor, or the municipal/city engineer’s office responsible for approving plans and specifications, and issuing construction permits.  Typically, basic information such as type of facility and facility size can be obtained from available documents, while more detailed data on commodities, market area, intermediate facilities, schedules, etc. may only be gathered through surveys of appropriate individuals (facility operators, carriers, shippers, and receivers).

4.4.2  Network Identification

Network identification is the second step in the freight modeling process, which involves identifying all the transportation facilities surrounding the site. These include roadways (freeways, arterials, collectors, and locals), rail network (mainline, spurs, etc.), waterway network, and transportation terminals (rail, intermodal, trucking, marine, and air cargo). A critical element in the network identification step is collecting data on physical and operating characteristics of the transportation network, including size, geometry, capacity, traffic volumes, speed limits, and any other network restrictions (for example, truck size and weight limits). Transportation network information is important in site/facility planning to analyze the choice of mode (based on relative level of service characteristics of available modes), as well as routing patterns of freight movements to and from the facility. Sources of transportation network information include state DOTs, MPOs, and traffic divisions of city or local governments.

4.4.3  Trip Generation

This step of the freight-modeling process estimates the average total freight trips (by mode) that would be generated by the planned facility for a specific time period (daily, annual, etc.). The total trips generated by the facility include both production (originating from the facility) and attraction (destined to the facility) trips.

The most common methods used for facility trip generation include trip generation rates, regression equations, and surveys. Using trip generation rates is the simplest approach for trip generation, in which estimates of number of trips per employee (or any other facility variable such as land area) are applied to the target facility to estimate the total trips generated. Trip generation rates also can vary based on truck types and the type of facility (land use). The trip generation rates used in this approach can be derived from previous surveys of freight flows associated with similar facilities or from standard sources providing average trip generation rates for facilities, based on facility and truck types.

The use of regression equations for trip generation offers the ability to predict the total trips generated as a function of more than one facility variable, which makes this approach potentially more robust and reliable compared to the use of trip generation rates. For example, a regression equation predicting total daily freight trips as a function of land use category, number of employees, and building/floor area. However, caution should be maintained when developing and using regression equations for trip generation, as equations with statistical inconsistencies (for example, using two facility variables in the regression equation that are correlated) will not result in reliable estimates.

Conducting surveys is the most time- and cost-intensive approach for trip generation, but it can provide the most accurate results, compared to trip generation rates and regression equations. This approach is useful in the case of special trip generators such as intermodal terminals, in which trip generation estimates are derived through direct contacts with a limited number of firms (facility operators and users – carriers, shippers, etc.). This approach is particularly effective if the planning agency has been building contacts with the freight community over a longer period of time.

A discussion on generating mode-specific trip generation estimates is presented below.

Highway

For trucking, the objective of the trip-generation step is to determine average daily truck activity (inbound and outbound) associated with the site/facility, by truck classification. The usual approach for developing these estimates is by conducting surveys of fleet managers of the planned site/facility.

Rail

The primary source for developing trip-generation estimates for rail is the Carload Waybill Sample. This data source provides extensive rail shipment data that can be accessed by state agencies from the Surface Transportation Board (STB). Some key rail shipment data available from the Carload Waybill Sample include origin and destination points, number of carloads, tonnage, participating railroads, and interchange locations.

Marine

Trip generation for marine freight flows typically are used for port facilities. Intermodal freight flows through ports are represented in terms of loading and unloading of TEUs, or 40-foot equivalent units (FEU). Key maritime data sources that could be used for trip generation include U.S. Waterborne Exports and Outbound Intransit Shipments, and U.S. Waterborne General Imports and Inbound Intransit Shipments (providing shipment weight and value and percentage of containerized cargo by port), and Tonnage for Selected United States Ports (providing total, domestic, and international tonnage handled for selected ports). Available fro the U.S. Army Corps of Engineers, these tonnage estimates also can be used to develop trip generation estimates for truck and rail modes generated by the port facility, after applying mode share ratios based on surveys of port facility operators.

Air

The Airport Activity Statistics of Certificated Route Air Carriers database is a primary data source for air traffic statistics, providing detailed data on freight express and mail traffic associated with each airport and individual airline. Trip generation estimates also can be derived through surveys of air cargo terminal operators. In cases where truck trip generation estimates for air cargo terminals cannot be derived from primary surveys (for example, due to higher costs) or through secondary data sources, default truck trip generation rates derived from a single study of truck trip rates for air cargo operations at the JFK International Airport can be used, which are presented in Table 4.24. However, these default estimates should be used with caution since they were developed from a survey of a single air cargo terminal operation.

Table 4.24  Truck Trip Generation Rates for Air Cargo Operations

Type of Firm

Number of Firms

Number of Workers per Firm

Truck/Van Trips per Day per Firm

Truck/Van Trips per Day per Employee

Courier

3

35

26

0.75

Forwarder

9

39

27

0.67

Broker

5

20

22

0.91

Trucking

1

20

25

0.50

Total/Average

18

33

25

0.73

Source:  Characteristics of Urban Freight Systems, Table 5.7.

4.4.4  Trip Distribution

Depending on the characteristics of the facility, and the types of products/shipments involved, the freight trips generated by a facility may have origins and destinations at several different locations, with distributions ranging from short-distance local to long-haul interstate and international trips. The trip-distribution step in the freight modeling process determines these origin-destination distribution patterns of the freight trips generated by the facility.

There are three broad classes of origin-destination trip patterns that guide the trip distribution process, which include the following:

  • Long-Haul – Trips with trip lengths of at least 250 miles to/from the facility.
  • Short-Haul – Interstate or interregional trips moving within a 250-mile radius to/from the facility (for example, delivery movements from a wholesale distribution warehouse to retail establishments).
  • Local – Short-distance local delivery trips. Examples of trips falling in this group include local distribution trips related to retail activity and intermodal drayage trips generated by rail or marine terminals.

Trip-distribution tables for a facility are typically developed by applications of standard gravity models, or by conducting surveys of facility operators or shippers/receivers. A key indicator that is critical to establishing the origin-destination patterns of freight trips generated by the facility is the economic input-output characteristics of the activities associated with the facility. An example of an economic indicator is the types and quantities of raw materials used by a manufacturing facility to produce final products, and in what quantities.

4.4.5  Traffic Assignment

This is the penultimate step in the facility freight-modeling process thatinvolves “loading” the predicted freight trips, by origin-destination and mode, on the transportation network surrounding the facility. The modal transportation network used in this step may include roadways, rail network, waterways, and transportation terminals (nodes).

The criteria used in the traffic assignment process vary depending on the mode of transportation. For example, freight trips to/from the facility occurring by barge will typically have limited waterway routing options, usually just one, in which case, all the trips will be assigned to that route. On the other hand, truck O‑D trips will typically have a number of routing options on the roadway network surrounding the facility, which are assigned using standard traffic assignment criteria, which include costs, travel times, traffic volumes, speed/weight/height limits, or other level of service measures.

In order to assess the impacts on traffic conditions and level of service (LOS) due to the planned facility, both passenger as well as freight trips associated with the facility need to be added to the transportation network. A similar approach is followed to estimate facility-related passenger trips, based on the knowledge of the approximate number of employees expected to work at the planned facility.

4.4.6  Planning Analyses

This is a step in site/facility planning wherein the results of the freight modeling step are used for conducting planning analyses pertaining to the facility. As discussed earlier, multimodal access route planning is the core component of a facility planning process. Based on the facility freight modeling results, the following types of analyses are typically conducted to support the facility planning process:

  • Level of service (LOS); and
  • Time of day.

Level of Service (LOS) Analysis

LOS analysis involves assessing the level of service of the transportation network surrounding the facility after accounting for the additional freight and passenger trips occurring due to the development of the planned facility. Important LOS characteristics analyzed include delay, congestion, physical deterioration, accidents, air quality, and noise. The inclusion of key safety and environmental impact parameters in the LOS analysis underscores the importance of incorporating safety and environmental impacts in the facility planning process, in addition to standard considerations pertaining to transportation system efficiency and reliability.

Time-of-Day Analysis

Time-of-day analysis is a critical component of the analyses conducted for planning freight facilities. This analysis typically feeds into the LOS analysis and involves assessing the performance of the transportation system at different times of the day.  Analyzing time-of-day interactions between freight and passenger traffic, and how they impact LOS, is an important component of freight planning because of the variances in time-of-day distributions of freight and passenger trips.  Time-of-day analysis also is important to assess environmental impacts at different times of the day (for example, noise during the nighttime period).

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