Quick Response Freight Manual II
7.0 Economic Activity Models
Economic activity models can be thought of as the freight equivalent of the integrated economic, land use, and transportation models used in passenger travel demand modeling. Economic activity models have two main components which work together: an economic/land use model and a freight transportation demand model. Before delving into the specifics of the modeling framework, data inputs, and modeling outputs of economic activity models, it is important to understand the interrelationships between the economy, land use, and freight transportation, in order to appreciate the relevance and importance of economic activity models for freight forecasting and to develop robust models to accurately predict future freight flows. The following sections describe these interrelationships and the different ways in which these components interact with each other. Due to the complex relationships and the unique details of a regional economy considered by these models, parameters are not readily transferred and the development and application of these models can hardly be considered a “Quick Response.” They are discussed here to provide a better understanding of the methods discussed in previous sections.
As discussed in Section 2.0 of this manual, freight transportation is an essential component of economic activity. Economic activity, which is typically measured in terms of the production of goods and services in a region, generates demand for freight transportation. For example, economic relationships between industries engaged in production and consumption of goods translate into spatial freight movements. These economic interrelationships between industries are described by economic input-output models in terms of the value of different commodities consumed by industries to produce industrial outputs. These economic input-output relationships, coupled with industrial land use patterns, are essential inputs for the spatial analysis of freight movements associated with economic activities in a region.
Freight demand associated with personal consumption is another important component of the impact of economic activity on freight transportation. Increased economic activity in a region fuels personal consumption, which leads to increased freight transportation activity associated with retail trade. Economic input-output models also describe commodity and service consumption activity of households, in terms of the value of goods and services used for final (household) consumption, which can serve as essential inputs to predict total freight demand associated with personal consumption in a region.
The interrelationships between economic activity and land use are important to understand, particularly in developing freight forecasts, since land use defines the spatial distribution of economic activity, and economic activity has a significant impact on the location and types of land uses in a region. For example, increased port economic activity may impact the development of new warehousing/distribution center land use and their location patterns. In addition, new land uses/development also can fuel economic activity in a region, which underscores the importance of integrating land use forecasts with predicting economic activity and associated freight demand. For example, the development of a new intermodal terminal in a region can instigate the development of logistics parks and warehouse/distribution centers, resulting in increased economic activity and associated demand for freight transportation.
Another important component in the interrelationships between the economy, land use, and transportation is how the performance of the transportation system impacts the economy, as well as industry land-use patterns in a region. Delays in the transportation system due to congestion can lead to significant costs for shippers, which are eventually passed on to the consumer. It is estimated that congestion on the transportation system is costing the United States economy more than $200 billion a year. Transportation service availability and system performance are critical factors that impact industry location choice decisions. For example, the development of an intermodal terminal may attract significant industrial investment in a region due to increased transportation system capacity and reliability, and lower costs compared to trucking.
Last, but not the least, is the consideration of the impacts of land use on freight transportation. Industrial and other freight facility land-use patterns in a region essentially define the spatial distribution of freight flows, since a large fraction of freight traffic moves to and from these land uses (excepting through traffic). Consequently, any future variations in the land use patterns of these facilities resulting from land use regulations or new land use developments instigated by economic growth have a direct impact on the spatial distribution of freight flows. For example, the development of new warehousing/distribution center land use may result from increased port economic activity, and their development location patterns (impacted by land use regulations), can have significant impacts on the distribution of truck trips generated by the port.
7.1 Modeling Framework
This section provides a more in‑depth look at the two essential components of economic activity models, namely the economic/land use model, and the freight demand model. The economic/land use component of the model generates socioeconomic forecasts at the zonal level of detail, based on considerations of the structure of the economy and the locations of industrial and household activities in the region in the future. These socioeconomic forecasts along with industrial activity location and economic interrelationships information are used interactively with the freight travel demand model to develop freight trip generation and distribution estimates. The travel demand model component then performs the mode split and network assignment steps to predict freight flows on the network by each mode of transportation.
Changes in land use may have a negative impact on freight transportation, especially with regard to facilities in densely developed urban areas. The economic land use model should be able to replicate the observed shift of freight facilities to areas distant from urban centers which have cheaper land in the large parcels often required by modern logistical and freight centers.
7.1.1 Spatial Input-Output Model
The modeling framework of economic activity models (integrated modeling of the economy, land use, and freight demand) is often referred to as a spatial input-output (I‑O) model. A spatial I-O model involves an economic component that defines household and economic activity and industry economic relationships in the region, a land use component that distributes household and economic activity across zones, and a spatial transportation component that defines the links and nodes of the network connecting the zones, and computes transportation flows on the network. All of these components are integrated together for freight flow forecasting.
Figure 7.1 depicts the steps involved in the modeling framework of economic activity models. The first step is the running of the economic/land use model which generates zonal socioeconomic forecasts. The model then performs the trip generation, trip distribution, mode split, and traffic assignment steps to estimate future freight flows on the transportation network by each transportation mode.
Figure 7.1 Steps Involved in Economic Activity Modeling Framework
Source: NCHRP 8-43 – Methods for Forecasting Statewide Freight Movements and Related Performance Measures.
However, there are some key features of economic activity models, which differentiate them from traditional four-step travel demand models, which are described below:
- Unlike traditional travel demand models, socioeconomic data (such as zonal employment and industrial economic activity) are not directly supplied to the model, but created internally by applying an economic/land use model. Additionally, in order to estimate economic activity, the generation and distribution of freight flows may be forecast within the economic/land use model component. The forecasts of freight flows, converted into vehicle flows on modal network, are then assigned to the transportation networks.
- At the end of each model run, the resulting performance on the transportation system, converted into costs, are used as feedback to the economic/land use component, which then updates the socioeconomic forecasts based on the predicted transportation system performance. The model then reruns the freight forecasting process with the new socioeconomic forecasts to reestimate modal freight trips on the transportation network. This iterative process continues until there is no further update in the socioeconomic forecasts generated by the economic/land use component of the model, from the predicted freight flows on the network and the resulting transportation system performance.
Since the performance of the freight transportation system, particularly on the highway network, is governed by its interaction with passenger vehicles, economic activity models are usually integrated with passenger travel demand forecasting models so that the predicted performance of the transportation system and the subsequent update of the socioeconomic forecasts are as accurate as possible.
The key feature of economic activity models is the integrated modeling of the dynamic interactions between economic activity, land use, and transportation. A conceptual framework of how these interactions are modeled is presented in Table 7.1. The feedback of model results to the economic/land use model accounts for any changes in economic activity and/or land use that would result from future variations in transportation system performance. These changes in economic activity and land-use patterns in turn impact the magnitude and distribution of freight flows on the transportation network and associated transportation system performance. Due to the considerations of these dynamic interdependencies between transportation system performance, economic activity, and land use, economic activity models also offer capabilities to accurately model induced freight demand impacts of new transportation or industrial investments.
Table 7.1 Dynamic Interactions in Integrated Economic Activity Modeling Framework
Economy and Land Use |
Integrated Model Application |
Transportation Component |
|---|---|---|
|
Trip Generation Trip Distribution Socioeconomic Forecasting |
|
Source: NCHRP 8-43 – Methods for Forecasting Statewide Freight Movements and Related Performance Measures.
7.2 Data Requirements
The following data elements are essential inputs required to develop economic activity models for freight forecasting.
7.2.1 Socioeconomic Data
Base year socioeconomic data are key inputs that feed into the economic/land use component of the model. Specific data elements in this category include base year population and employment by different industry sectors, for each zone, which are used to develop intrinsic socioeconomic forecasts within the framework of the economic activity model. Sources for socioeconomic data include the U.S. Census, MPOs, and state and regional economic development agencies.
7.2.2 Economic Activity Data
I/O data are the main economic data inputs for the spatial I-O modeling framework of economic activity models. These data describe the economic relationships between different industry sectors in terms of the values of the various types of goods and services consumed to produce outputs. I-O data also describe final consumption activity in terms of the total value of goods and services consumed by households. In addition to I-O data, information on values per ton for each commodity are essential inputs in order to translate economic I-O data into equivalent shipment tons. Also, I-O data may be only available at the county level of detail, which are disaggregated by the model to zones for trip generation using the population and employment data inputs described in an earlier section. Sources of I-O data include IMPLAN™ and RIMS‑II™, which track the buying/selling interrelationships between industries within a given region. They reflect forward and backward linkages in the flow of money associated with business suppliers and consumer spending. They can, thus, capture the full economic impacts (including multiplier effects) derived from changes in demand or output in a given industry.
7.2.3 Land Use Data
Data concerning the availability of land, industrial land use patterns, and the rules and regulations governing the development of land uses in the future are critical inputs that are used by the economic/land use component of the model to generate socioeconomic forecasts. Some key issues with respect to land use data that are important to consider as inputs to the model include an understanding of industry location choice decisions as a function of transportation system performance, as well as a better representation of the interdependencies between land use and economic activity (for example, how increased economic activity in one industry sector may fuel the development of land uses associated with other industry sectors, and vice versa. This tendency, for example the location of automobile parts and accessory firms near automobile assembly plants, is often called clustering of industries).
7.2.4 Transportation Network Information
Like traditional travel demand models, transportation network information is a key input for economic activity models in order to assign the freight flows, by mode, to each modal transportation network. The network is represented in terms of links and nodes that provide connectivity between zones. Following are some key network attributes to consider in the model:
- Capacity;
- Size and weight regulations;
- Hazardous material regulations;
- Road closures; and
- Speed limits.
7.3 Oregon Statewide Passenger and Freight Forecasting Model
The first integrated statewide transportation and land use model for Oregon (Oregon Statewide Model) was developed through the establishment of the Transportation and Land Use Model Integration Program (TLUMIP) by the State of Oregon in 1996. An update of this model was initiated by ODOT in 1999, leading to the development of the second generation integrated statewide model that simultaneously models economic activity, land use, transportation supply, and travel demand. The main purpose of developing the integrated land use and transportation model was to analye and support land use and transportation decisions by making periodic long-term economic activity, demographic, passenger, and freight flow forecasts at the statewide and substate levels. A key objective of the integrated statewide model is to analyze the potential effects of transportation and land use policies, plans, programs, and projects on travel behavior and location choices.
Key characteristics of the Oregon Statewide Model include the following:
- Single geographic scale for the statewide region;
- Complete integration of economic, land use, and transportation components;
- Modeling of dynamic interactions between the economy, land use, and transportation;
- Hybrid equilibrium (for economic and transportation markets), and disequilibrium (for activity and location markets) formulations; and
- Activity-based modeling formulation.
7.3.1 Modeling Framework
The Oregon Statewide Model belongs to the class of economic activity models designed for forecasting both passenger and freight movements. The modeling framework consists of a set of seven stand-alone but highly integrated modules, which are depicted in Figure 7.2.
Figure 7.2 Modules in the Oregon Statewide Model
Source: J.D. Hunt et al., Design of a Statewide Land Use Transportation Interaction Model for Oregon, 2001.
Descriptions on each of these modules are presented below:
- Regional Economics and Demographics – Key data components in this module include annual productions by economic sector, employment by industry sectors, and in‑migration and payroll by economic sector. Besides economic production and industry employment data, this module also includes four sectors for final demand, which include exports, household consumption, investment, and government (state or local).
- Production Allocations and Interactions – The production allocations and interactions module determines the distribution of production activity among zones and the consumption of space by these production activities in each zone. The module also reflects the flows of goods and services and labor from production locations (zones) to consumption locations (zones), as well as the exchange prices for goods and services, labor, and space each year.
- Household Allocations – In this module, household allocations to zones reflect the same distributions as the allocations from the previous year. The labor flows originating from these households are allocated to the production (exchange) locations based on the production allocations to zones determined from the production allocations and interactions module. Similarly, distribution of freight demand associated with household consumption activity is modeled by allocating the flows of commodities consumed by the households to zones based on zonal production (exchange) allocations.
- Land Development – The land development module estimates the year-to-year changes in available space in each zone in the region. The primary task of the land development module is to adjust the quantity of space over time in the region in response to changes in price. Other modules in the model determine a price for each category of space in each zone using a highly disaggregate process (one grid cell at a time), based on the fixed supply of space available in each zone for that particular year. The model uses the zoning patterns and does not forecast how the political process can change zoning patterns.
- Commercial Movements – A key output of the commercial movement module is the average annual growth estimates for weekday truck traffic volumes. In order to determine truck traffic growth rates, the module synthesizes a fully disaggregated list of individual truck shipments. For each truck movement, the synthesized data include the type of vehicle (light single-unit, heavy single-unit, articulated), starting link, ending link, starting time, commodity hauled, and transshipment organization. The module uses truck shipment sizes consistent with the CFS. Activity-based truck tours are generated by the module using activity interaction matrices, which contain aggregate freight flows between activity centers. These flows are first translated into discrete shipments by commodity, and then combined into truck tours. The module also considers empty truck movements, O‑D distribution patterns for which are derived from the patterns for loaded vehicles.
- Household Travel – The household travel module estimates specific individual passenger trips made by households during a particular representative workday for each year, with information on starting link, ending link, starting time, tour mode, vehicle occupancy, utility attribute coefficients, and nonnetwork-related utility components. The process starts by assigning each household member an activity pattern for the day. The activity pattern is a listing of the sequence of activities undertaken by the household member as a series of tours made out from the home or work place.
- Transportation Supply – The transportation supply module is a hybrid of macroscopic and microscopic techniques. The module computes equilibrium travel times by loading a conventional O‑D trip table to a network. These equilibrium travel times, derived from a macroscopic perspective (total vehicles), are then used in a microscopic assignment, which works at the level of individual vehicles, determining the network loadings from synthesized commercial vehicle and household travel demands.
- The data store is the database in which all the information input and output from the modules is stored. Also, all information flowing between modules passes through the data store.
7.3.2 Geographic Coverage
The Oregon model has three geographic components:
- A statewide model for assessing broader statewide policy options;
- A substate model for regional analysis along major intercity transportation corridors; and
- An urban model for a more detailed analysis of local impacts associated with policy and investment decisions.
7.3.3 Modes
The Oregon Statewide Model is an integrated passenger and freight forecasting model, with simultaneous assignments of future passenger and freight movements on the transportation network. The modes involved in the model include two-axle truck, three-or-more-axle truck, rail, automobile and van, water, and air cargo.
7.3.4 Data Requirements
Table 7.2 presents the data elements used to develop the Oregon statewide model. The transport supply and demand data elements, which include network information, modes, modal split parameters, user charges, and vehicle operating costs, are comparable to those required to develop traditional travel demand forecasting models. Excepting the development of factors to translate flows in dollars to equivalent person and freight truck trips, the same holds true for the land use-transportation interface data.
Table 7.2 Data Inputs for Oregon Statewide Model
| Category | Data Elements |
|---|---|
Land Use and Socioeconomic Data |
|
Land Use and Transport Interface Data |
|
Transport Supply and Demand Data |
|
Source: The Oregon Statewide and Substate Travel Forecasting Models, Rick Donnelly and Pat Costinett, Parsons Brinckerhoff Quade & Douglas, Inc., William J. Upton, Oregon Department of Transportation, TRB on-line publications (onlinepubs.trb.org), 1999.
7.3.5 Freight Forecasting Process
The Oregon Statewide Model has it roots in TRANUS™, an integrated land use and transportation model that can be applied at an urban or regional scale. TRANUS has two purposes: 1) to simulate the probable effects of applying particular land use and transport policies and projects; and 2) to evaluate these effects from social, economic, financial, and energy points of view. TRANUS has two main components: land use and transportation. Since land use and transportation influence one another, a change in the transportation system, such as a new road, a mass transit system, or change in rate charges, will have a direct effect on land use patterns, which will in turn impact the magnitude and distribution of freight demand in a region.
Figure 7.3 shows the dynamic interactions between land use and transportation over time that are modeled in the TRANUS framework. The model simulates the interactions between land use and transportation for each time period, by predicting the impacts of transportation on new land use, as well as modeling the associated transportation demand impacts of changing land use patterns. Under each temporal iteration process, the new land use is dependent on the land use in the previous iteration, as well as transportation system and demand characteristics at the end of the previous iteration step.
Figure 7.3 Dynamic Interactions in an Integrated Land Use-Transportation System
Source: NCHRP 8-43 – Methods for Forecasting Statewide Freight Movements and Related Performance Measures.
The first step in the model involves generating a set of paths connecting origin and destination pairs by each transport mode (freight, private automobile, public transport, etc.). Second, the model transforms the potential travel demand estimated by the activity/transport interface into actual trips at particular times of the day (peak, off-peak, 24 hours, etc.). Trips for each category are distributed to modes by means of a multinomial logit (MNL) model, in which the utility function is determined by the composite cost of travel by mode. Third, the model assigns trips by mode to the different paths connecting origins to destinations by that mode. Trips are simultaneously assigned to operators and to links of the network, also using an MNL modeling approach. The combination of the MNL modal split and assignment models is equivalent to the two-level hierarchical modal split model.
The goods and services shipments flows are estimated using the spatial distributions of activities and population, following the path from the production locations to the exchange locations and then to the consumption locations. A notable aspect is the absence of a separate trip distribution step in the model, as is the case in traditional four-step travel demand models.
Mode split and assignment are accomplished together as a simultaneous loading to a multimodal network. The multimodal network represents the supply of various combinations of available goods and services transportation, which include two-axle trucks, three-or-more-axle trucks, rail, automobile and van, water, and air cargo.
The transportation supply module is a hybrid of macroscopic and microscopic techniques. A standard equilibrium assignment is made using congested travel times and the resulting origin to destination travel times also are saved. These equilibrium travel times are then used in a microscopic assignment, which works at the level of individual vehicles, determining the network loadings from synthesized household travel and commercial movement demands.
The commercial movement module determines the growth of freight movements during a representative workday in each year. In fact, the model steps through time in a series of one-year steps that allow the entire system to evolve. The representation for year T+1 is influenced in part by the conditions determined for year T. These yearly freight movements are then converted to a representative weekday.
7.4 Cross-Cascades Model
7.4.1 Modeling Framework
The Cross-Cascades model incorporates the spatial I-O modeling framework for passenger and freight forecasting, involving a household and economic activity component (household consumption activity and economic interrelationships between industries for production and consumption), a land use component (spatial distribution of household and economic activity), and a transportation component (physical and operational transportation network attributes). Using the spatial I-O modeling approach, the model simultaneously develops forecasts, generated iteratively, of modal passenger and freight traffic volumes on the corridor network, mode splits, population, and employment. Figure 7.4 depicts the spatial I-O modeling approach of the Cross-Cascades model.
Figure 7.4 The Cross-Cascades Corridor Spatial Input-Output Approach
Source: Cross-Cascades Corridor Analysis Project Summary Report, Washington Department of Transportation, 2001.
7.4.2 Geographic Area
Since the model was developed to specifically analyze passenger and freight travel demand along the Cross-Cascades corridor, the market area of the model is limited. The model is comprised of 61 zones, with 54 in Washington, 1 in Idaho, and 6 external zones. The internal zone structure includes 25 subcounty zones within the corridor (24 in Washington, and 1 in Idaho), and 30 other county-level zones in Washington. The external zones in the model include Western Canada; Canada (east of Cascades); Northern Idaho, Montana, and East; Eastern Oregon, Southern Idaho, and Southwest; West Oregon and California; and non-United States.
7.4.3 Modes
The Cross-Cascades model is an integrated passenger and freight forecasting model, with simultaneous assignments of passenger and freight traffic volumes on the corridor network. Freight travel modes considered in the model include medium truck, heavy truck, rail, and air freight, while passenger travel modes include automobile (private), automobile (work), coach (bus), Amtrak (rail), and air.
7.4.4 Data Requirements
Following are some key data inputs to the Cross-Cascades model:
- Household Data – 1998 county-level household data from the Washington State Population Survey (data disaggregated to subcounty zones and income groups based on 1990 U.S. Census distributions).
- Employment Data – 1998 county-level employment data derived from BEA data on total industry employment, and Labor Market Economic Analysis (LMEA) studies on covered and noncovered employment.
- MEPLAN Model Coefficients – Economic activity in Washington State was modeled through the use of MEPLAN model coefficients. These coefficients define the amount of each type of employee or personal activity required to generate a single unit of economic activity for a particular industrial or household sector.
- Modal Networks – Transportation networks incorporated in the model include all the major Washington highways, Burlington Northern & Santa Fe (BNSF) rail lines across Stevens Pass and Stampede Pass, and airways connecting the cities of Seattle, Wenatchee, Yakima, Moses Lake, the Tri-Cities area, and Spokane. In addition, truck, rail, and air cargo terminals are explicitly coded into the network for the identification of access routes, and assignment of traffic volumes.
- Intermodal Terminal Data – Truck, rail, and air freight terminals are explicitly coded and included in the assignment and path identification process. The use of multimodal paths through intermodal connectors between the various model systems allows the inclusion of terminal transfer costs (parking and freight handling). Nodes in the transportation component of the Cross-Cascades model include attributes of geographic location and connections for not only highway and rail nodes but also nodes with special identifier codes for airports, truck terminals, and ports.
7.4.5 Freight Forecasting Process
The Cross-Cascades model is implemented in the MEPLAN software, developed and distributed by ME&P of Cambridge, England. MEPLAN is based on the concept that, at any geographic level, land use and transportation affect one another. The location of households in turn create demands for industrial land, retail floor space, and housing. The relationship of the supply of land to the demand for development influences prices for space in each location, and that pattern of prices in turn influences where people choose to live and work. In addition, the mobility and access provided by transportation also affects the demand and location of residents, employers, and new developments.
Trip Generation
The Cross-Cascades model as implemented in MEPLAN, uses an I‑O structure of the economy to simulate economic transactions that generate transportation activity. A spatial I-O model identifies economic relationships between origins and destinations. For future years, the spatial allocation of economic activity, and thus trip flows, is influenced by the attributes of the transportation network in previous years. Together, the land use/economic components and the location of the transportation network affect transportation flows. Transportation costs, including the costs of congestion created by increasing travel demands, also influence the location of households and businesses.
Figure 7.5 shows the schematic of the interactions between the economic/land use and transportation model components for the trip generation and trip distribution steps in the model. Trade-to-trip ratios translate economic activity into transportation flows, which are developed using the TRANSEARCH freight flow data. Similarly, household trip rates are applied to estimate equivalent trips associated with households, using information on the number of household units. These rates are developed primarily using Nationwide Personal Transportation Survey (NPTS) travel data. The trade-to-trip ratios and household trip rates are exogenous inputs to the model.
Figure 7.5 Trip Generation and Distribution in the Cross-Cascade Model
Source: Special Input-Outputs, Cross-Cascades Corridor Analysis Project, Summary Report, Washington State Department of Transportation, 2001.
Trip Distribution
The Cross-Cascades model handles trip generation and trip distribution in a single step, as depicted in Figure 7.5.
Mode Split
As discussed earlier, the Cross-Cascades model is an integrated passenger and freight forecasting model. The freight transportation modes in the model include air, rail, medium trucks, and heavy trucks. The passenger component of the model includes four personal passenger trip categories (commuter, shopping, visit friends and relatives, and recreation/other), and two business passenger trip categories (services and business promotion), the modes available for which include air, Amtrak (rail), coach (bus), private automobile, and work automobile. In the Cross-Cascades model, mode choice is calculated based on monetary values of time, distance, and cost. The mode split disutility function structure and coefficients are defined with cost functions. Costs (disutility) are related to mode choice through a nested logit function with linear utility.
Traffic Assignment
The Cross-Cascades model handles mode and route choice simultaneously in a manner that distributes trips stochastically rather than assigning all trips to the least cost route. Freight and passenger trips also are handled simultaneously.
A key feature of MEPLAN is the ability of the transport model to provide feedback to the economic/land use model. At the end of each iteration, the transport model generates travel disutility (costs) for each zone pair, which in turn influence business and household location decisions. In future year iterations of the model, a nested logit model is used to determine the changes in business and housing location patterns in response to changing transportation costs.
