Intermodal focus

The model developed for this project is intended to address transportation issues and problems on a regional or statewide scale. Consequently, the model differs in a number of respects from the models typically used for urban transportation planning. One way this model differs is that it addresses both passenger and freight transportation. Traditional urban transportation models focus on passenger transportation. However, ISTEA has placed added emphasis on freight transportation recognizing the need for improved efficiency in the movement of natural resource, agricultural and manufactured goods in order for the United States to remain a leader in the international marketplace. Therefore, this model addresses the location, accessibility and scale of freight transfer facilities; conflicts between freight and passenger facilities; and the shared use of transportation facilities by freight and passenger traffic.

Ownership alternatives

Second, this model addresses both public and privately owned transportation infrastructures. The redirection of federal transportation programs from a modal to a system-wide focus requires increased cooperation and coordination between the public and private sectors. Coordination is especially important when addressing intermodal transportation needs. Continuing to focus transportation planning on just public facilities would result in a suboptimal transportation system.

Logistics issues

Third, the model addresses logistical issues, such as distribution and production facility location. Although the relationship between business logistics and transportation planning is well recognized, transportation planners have generally taken the location of such facilities as exogenous variables to which transportation systems must respond. However, in reality decisions made regarding transportation system and logistic facility investment are often made simultaneously.

Economic feasibility and impact analysis

Fourth, the model integrates economic feasibility and economic impact analysis with the analysis of issues traditionally addressed by transportation models, such as traffic congestion and air quality. Traditional transportation models include information on levels of economic activity, such as population, employment, personal income, and trade flows, as exogenous factors used to estimate trip productions and attractions. Feedback effects to an area’s economy resulting from transportation investment are generally ignored. In addition, economic feedback effects are generally omitted in analyzing the economic feasibility of transportation investment projects. The rationale for the exclusion of these effects is the view that they represent transfers of economic activity and do not result in net changes in the overall level of economic activity. This view is only partly correct. To the extent that transportation improvements result in gains in productivity beyond those measured as travel timesavings and vehicle operating cost savings, they should be included in the feasibility analysis for transportation investments. Also, transportation system improvements may accelerate the rate of growth of economic activity in an area. Given that the value of benefits and costs associated with a project decrease with time, a project resulting in accelerating the realization of benefits enhances the economic well being of society.

Distribution of impacts

Fifth, the model quantifies the distribution of impacts among different segments of society and among different business sectors. Although not important from the perspective of a project’s economic feasibility, disaggregation of benefits and costs among various constituencies becomes important in building political support for a transportation system improvement. Also, this feature of the analysis becomes essential in allocating financial responsibility and for devising financing mechanisms for a project. In particular, this aspect of the model is required in order to identify the extent to which transportation investment may be financed employing market pricing versus public subsidy.

Thus, this model internalizes many factors that are omitted from traditional transportation models. In this manner the model permits the dynamic analysis of transportation system investment alternatives.



The model incorporates many elements of the traditional four stage urban transportation modeling process. However, this process has been modified and expanded in six major respects. First, the model is demand driven meaning no a priori assumptions are made regarding the options available for solving a particular transportation system problem. Second, the trip generation and distribution stages have been disaggregated to more accurately capture the unique transportation service demands of intercity passenger, natural resource and agricultural commodity, and manufactured goods traffic. Third, the mode choice analysis incorporates a series of filters associated with trip purpose and population characteristics for passenger service demands, and product and market attributes for freight service demands. Fourth, the model addresses the traffic impacts of transfer, distribution and production facility location decisions as well as transportation way and terminal facility investment decisions. Fifth, the economic effects resulting from alternative transportation and logistic system investments feed back into both the trip generation and traffic assignment stages of the model. Sixth, the transportation network and regional economy components of the model are supported by a single geographically referenced database. A more detailed description of the distinguishing features of the model is provided below.

Transportation Demand Forecasts

The demand for all transportation services is derived from personal needs or business practices for passenger trips and from market forces or logistics requirements in the case of freight movement. There exists an obvious distinction between factors from which the demands for passenger and freight transportation service are derived. Furthermore, even within these two broad categories, factors influencing transportation needs differ enough to require a variety of approaches to adequately estimate and forecast these demands for service.

For example, individuals demand intercity transportation services for a variety of economic and personal reasons. Economic reasons for intercity passenger travel include employment, delivery of business services, marketing and product promotion, training, and meeting and conference attendance. On the other hand, personal reasons for intercity travel include recreation, vacation, shopping, medical and other profession services, family obligations, and education.

The demand for freight transportation service depends to a great extent on a product’s stage in the production process. Transportation service demands for intermediate goods are often dictated by the logistics requirements of industries for which they are inputs. On the other hand, transportation service demands for both raw materials and finished goods are generally market-driven.

Recognizing these differences in the factors that influence the demands for intercity passenger and freight transportation services, the model disaggregates the trip generation and distribution stages into five components. For passenger service demands there are separate model components for business and non-business travel. For freight transportation, service demands for raw materials (i.e., agricultural and mineral), intermediated goods, and finished goods are each modeled separately.


Delineation of Passenger and Freight Service Requirements

In addition to the demands for transportation service depending on the different purposes identified in the previous section, individuals and business managers respond to a variety of preferences and service requirements when determining the types of transportation service that most appropriately meet their needs. For passenger transportation, factors such as the values placed on time, convenience, comfort, and security, as well as out-of-pocket cost, influence travel choices in terms of time of travel, mode choice, and route. For freight transportation, the physical characteristics of a shipment (i.e., size, weight, and density), the physical state of a shipment (i.e., gas, liquid, or solid), product perishability, and value similarly influence travel choices. Also, market options and logistics management practices, such as the growth of international trade, the increased use of shipping containers, and the adoption of just-in-time inventory management, strongly influence transportation service requirements.

These passenger and freight service preferences and requirements are incorporated in the model in the form of general service characteristic filters. The filters provide a basis for classifying transportation service demands according of common basic attributes, such a travel time sensitivity, special handling requirements, value of service, and physical attributes. These filters, combined with modal service characteristics described in the next subsection, act to limit the universe of viable transportation system improvement options.


Delineation of Transportation Mode Service Characteristics

The service characteristics of transportation modes vary in terms of accessibility, capability, reliability, security, speed, energy efficiency, and cost. No single mode possesses a clear-cut advantage is terms of all the service characteristics. For example, motor vehicles possess a clear advantage in terms of accessibility in providing either passenger or freight service, but they are not as efficient as barge or railroad in hauling bulk commodities like coal. Similarly, air transportation is generally the fastest of all modes for trips over a few hundred miles, but it suffers severe limitations in terms of shipment size constraints and cost.

Given the multi-modal scope of the model, every effort is made not to prematurely limit modal options for meeting transportation service needs. However, some options are clearly not viable for satisfying special types of transportation service demands. Consequently, the model employs a set of modal filters to identify the technologically feasible options for each category of transportation service demand. For example, pipeline in all cases and barge in most cases would be eliminated at this stage in the model as options for passenger transportation. Similarly, motor carrier and railroad options would be determined non-viable for long distance movements of large volumes of natural gas.


Mode Choice and Route Assignment Analysis

The objective of this component of the model is to determine the pattern of mode and route choices that optimize the operating performance of the transportation system. The exact form of the optimization rule varies among the different types of transportation service demands.

For passenger transportation the mode choice and route selection decisions are generally made sequentially. Trips made for personal reasons are disaggregated by trip purpose and trip characteristics, such as distance, time of day, and number of individuals making the trip. Trip purposes and characteristics interact to define the traveler’s choice of modes. Then, with the mode choice decision made, the route followed in making the trip is determined to minimize one’s travel time.

Passenger travel for business purposes is similarly influenced by the purpose for which the trip is being made and by the trip characteristics. In some cases the trip purpose precisely determines the mode choice, such as is the case for trips associated with the delivery of many business services which require a motor vehicle to carry service equipment and supplies. When mode is not predetermined by the trip purpose, mode and route choices are usually made to jointly minimize travel time and out-of-pocket cost.

For bulk natural resource and agricultural commodities, modal and routing decisions are generally made simultaneously. In the case of agricultural commodities, such as corn and soybeans, trip destinations are determined in accordance with the producer’s or marketer’s desire to maximize income net of transportation and handling costs. Thus, modal and route options are considered simultaneously with market bid prices in determining the optimal distribution pattern for these commodities.

In the case of intermediate manufactured goods, mode and route choices are more constrained than for bulk commodities. Also, often either the producer or the purchaser of the product determines the transportation mode while the carrier chosen to provide the transportation service determines the route. In addition, trip destinations are generally predetermined by contractual arrangements between producers and consumers of the intermediate goods. Although transportation rates may influence these contractual arrangements, other factors such as technical features of the product, product quality, reliability of the suppliers, and volume price discounts play a more important role in the establishment of these supplier-customer relationships.

For finished goods the choice of transportation mode and route are generally made sequentially. First, either the manufacturer or the purchaser of the product decides on the mode of transportation. This decision generally reflects the desire of the party making the mode and carrier choices to minimize overall logistics costs. Then the carrier chosen to provide the transportation service determines the routing in order to minimize its operating costs.


System Capacity Needs Evaluation

The initial process of estimating the demands for transportation service, distributing trips among alternative origins and destination, choosing transportation modes, and assigning traffic to specific routes results in the identification of capacity constraints on the existing transportation and logistics system. This stage of the analysis involves the quantification of the magnitude of these system deficiencies.

The principal focus of this stage is the extent to which congestion on the existing transportation system results in the diversion of traffic among routes, modes, and time periods from what they would be under optimal conditions. The impacts of traffic diversion are measured in terms of changes in travel time and vehicle operating costs. Safety impacts are also quantified.

In addition logistic system constraints involving the location, size, and layout of storage, distribution and transfer facilities are evaluated. The costs associated with capacity constraints involving these types of facilities are measured in terms of changes in inventory costs, delay time, operating costs, and capital costs.


Project Alternative Identification and Evaluation

The initial four-step transportation service demand analysis results in the identification of existing transportation and logistics system utilization. This stage of the analysis results in the identification of what usage of the transportation and logistics system would be under ideal conditions. The differences between the two scenarios provide a set of potential system improvements.

Conceivably, more than one alternative exists for solving system capacity problems. In other cases a combination of system improvements are required to solve capacity problems. Often improvement alternatives involve two or more transportation modes, as well as changes to logistics facilities.

The evaluation of improvement alternatives requires the quantification of costs and benefits associated with each. How costs and benefits vary depending on the timing or phasing of alternative improvements must also be taken into consideration. To the extent possible externalities arising from different alternatives must also be included in the analysis. In addition, non-quantifiable impacts should be described. Because different alternatives may be expected to impact the economy of the area under study, feedback effects from the economy to the transportation and logistical system may be expected. Consequently, several iterations of this stage of the analysis may be required.


Economic Impact Analysis and Feedback Evaluation

Improvement to an area’s transportation and logistics infrastructure may be expected to give rise to associated productivity gains and/or expanded market opportunities for the area’s businesses. Transportation system improvements may also make an area more attractive as a place for people to live and for recreation activities. These impacts of infrastructure investment on an area’s economy may in turn be expected to influence how the infrastructure will be used.

For example, if improvements to the transportation system make an area’s manufacturing enterprises more productive than similar businesses located elsewhere, then manufacturing activity may be expected to increase in the area. The resulting impacts on the demand for transportation services that may be expected to follow include: increased levels of freight movement, changes in supply sources and markets, increased demand for warehouse space, and more and possibly longer trips by people commuting to work. In this sense the initial transportation and logistics system improvements may be expected to have a multiplier effect on both the area’s economy and on the demand for transportation services in the area.

Furthermore, the stimulation of an area’s economy resulting from transportation and logistic system infrastructure improvements may affect the character of transportation service demands. For example, as an area’s economy grows it generally diversifies. Thus, an area’s economy once dominated by agricultural and manufacturing enterprises may experience substantial growth in the service and retail sectors. When this occurs, demand for commercial air transportation for both passengers and express freight may follow.

Thus, improvements required to remedy initial system deficiencies might be expected to result in both increased demands for service and in changes in the types of transportation and logistics service demands. Recognition of these secondary, or induced, impacts requires the model incorporate the analysis of how system improvements impact an area’s economy and then how changes in an area’s economy feedback additional service demand requirements to the transportation and logistic system.


Investment Feasibility and Impact Distribution Analysis

The evaluation of investment feasibility for public sector transportation system improvements generally involves conducting a benefit-cost analysis. Traditional types of benefits taken into consideration include: (1) the value of travel time savings, (2) the reduction in vehicle operating costs, (3) savings associated with improved safety, and (4) the value of beneficial and adverse environmental impacts. Other types of economic benefits are generally ignored. The rationale for ignoring other types of benefits, such as changes in personal or business income, is the belief these represent transfers of economic activity or they represent just another way of quantifying the four types of benefits previously identified.

Certainly care needs to be taken to avoid the double counting of benefits; some of the economic impacts of transportation system investment no doubt fall in this category. For example, increases in land values near a relocated highway in many cases represent nothing more than the value lost by property located near the previous highway route. Similarly, a business’s increased profits resulting from a decrease in transportation costs associated with shipping its products to market often represents little more than another way of counting travel time, operating cost, and safety cost savings resulting from the transportation system improvement.

On the other hand, many types of benefits experienced by individuals and private businesses resulting from transportation system improvements represent a net gain to society over and above the value of the transportation and environmental impacts previously mentioned. For example, transportation system improvements that improve the reliability of goods movements may result in a reduction in inventory requirements and distribution center locations. Also, transportation system improvements may result in an expansion of market opportunities. This may permit a firm to adopt more efficient technology than would be justified by the smaller market area previously served. In addition, expanded market access may mean a broader selection of merchandise for consumers. Furthermore, transportation system improvements often result in expanded employment opportunities that permit individuals to make better use of their training and skills.

On the cost side of the equation, businesses only take into consideration their own private costs when evaluating investment options. Thus, the evaluation of all transportation and logistics system improvement options within a single benefit-cost framework provides a more accurate assessment than do separate evaluations of public sector transportation improvements and private sector transportation and logistics system improvements. Similarly, using a single social discount rate rather than separate rates for public and private sector improvements yields a consistent basis for comparison of different packages of improvement options that include both public and private sector elements.

Furthermore, the comprehensive evaluation of both public and private sector improvement options is required in order to permit the determination of how benefits and costs are distributed among different social and economic segments of society. The identification of to what extent different constituencies benefit from alternative transportation and logistics system improvements serves two needs. First, no improvement is generally favored by everyone, and often improvement options benefit some groups while harming others. Consequently, the identification of winners and losers is needed in order to be able to identify the political feasibility and fairness of different improvement options. Second, the distributional analysis may be used to provide a basis for devising financing plans for the different options. In some cases this may require some groups of beneficiaries (winners) to subsidize other groups (losers) in order to win support for a particular system improvement.

As outlined above, a multimodal investment model must be both comprehensive and integrated. It must be comprehensive in the sense that the entire transportation and logistics system of an area under study is taken into consideration. This means that all modes of transportation, whether under public or private ownership, be incorporated in the model. Also, the incorporation of logistics facilities is required because decisions made regarding the investment in transportation facilities often impacts decisions regarding the location and scale of logistics facilities, and vice versa.

In addition, to facilitate the consideration of feedback effects resulting from transportation and logistics system improvements, the model must integrate traffic analysis and economic impact analysis capabilities. Finally, given the spatial and temporal nature of the types of issues the model is required to address, the integration of the traffic and economic impact analysis capabilities requires the support of geographic based data structure. This data structure is discussed in the next section.



The demand for transportation services and the economic impacts that result from transportation and logistic system improvements possess both spatial and temporal dimensions. Consequently, a geographically referenced database is required to support the multimodal investment analysis model. The accommodation of transportation system analysis and economic impact analysis within a geographic information system (GIS) presents a number of challenges. However, this approach also possesses numerous advantages over traditional modeling methods. The requirements and advantages associated with the development of such an integrated model are discussed below.


Integration of Transportation and GIS Modeling Capabilities

The integration of transportation system analysis software with a GIS database requires the combination of point, line and area feature and attribute files in a single database. Point files represent features of the transportation and logistics system (i.e., access, junction and interchange points, traffic zone centroids, and logistics facility locations) and location references for demographic and economic data used to forecast transportation service demands. Line files represent transportation guideway features (i.e., highways, rail lines, pipelines, and waterways), and area files include political jurisdictions, traffic zones, and environmental features.

The combination of transportation and logistics system information with demographic and economic data in a single geographically referenced database has four advantages. First, data used to forecast the demand for transportation service is generally not collected on a geographic basis consistent with traffic zone boundaries. The use of GIS makes the reaggregation of data relatively easy. Second, the analysis of different transportation and logistics system improvements often requires the reconfiguration of traffic zones, the resegmentation of guideway elements, and/or the addition or subtraction of access, interchange or junction points. These system changes generally necessitate the re-estimation of service demands. The flexibility inherent in GIS permits the accommodation of a much broader set of improvement options than do conventional transportation network models. Third, transportation system improvements often affect an area’s economy, which in turn results in additional and different demands for transportation and logistics services. The integration of the transportation system model with GIS permits the identification of these feedback effects by making the adjustment of demographic and economic data simultaneous with the determination of transportation and logistics system service demands. Fourth, the spatial analysis capabilities of GIS support the analysis of the geographic distribution of transportation and logistics system improvement impacts on the environment, land use patterns, and population groups.

However, in order for transportation and logistics system analysis to be carried out within a GIS several requirements must be satisfied. First, the transportation elements of the GIS database must be configured as a connected network rather than just as cartographic elements. Second, the GIS must accommodate the dynamic segmentation of system elements. Third, the transportation network must be geographically referenced in a manner consistent with the demographic, economic, and environmental coverage. Fourth, the GIS must accommodate the offsetting of coincident transportation system elements, such as parallel highway and rail lines, for display purposes, while maintaining true location references for traffic modeling. Fifth, changes to the system, including traffic zone adjustments, must be automatically reflected in associated attribute data files.


Integration of Economic Impact Analysis and GIS Modeling Capabilities

The incorporation of economic impact analysis capability within a GIS requires both the spatial and temporal referencing of demographic and economic data. Economic impact models generally consist of five elements: (1) a population and labor supply element, (2) a wage, price and income element, (3) a consumption, government expenditure and trade element, (4) a labor demand and capital investment element, and (5) a business input-output element. Integration of economic impact analysis capability with a transportation and logistics system augmented GIS requires the incorporation of a sixth element, i.e. a trade flows element. This sixth element allows the estimation of levels of economic interaction among different regions. In addition, it provides the capability to convert economic activity measured in dollar terms into weight units and transportation vehicle counts.

The principal advantage of integrating an economic impact analysis capability into the model is the facilitation of the identification and estimation of feedback effects resulting from alternative transportation and logistics system improvements. Conventional transportation system models permit the identification the extent to which system improvements redistribute existing traffic among elements of the system and the extent to which new traffic is induced by satisfying previously existing latent demands for transportation service. The economic impact capability adds to this newly generated traffic arising from demands for transportation and logistic services resulting from economic growth that would not occur in the absence of system improvements.


Integration of Investment Feasibility Analysis and GIS Modeling Capabilities

The feasibility of public sector infrastructure investment projects is generally evaluated using benefit-cost analysis. The primary differences between this method of investment analysis and the methods used for private sector projects are (1) a societal versus firm perspective, (2) the internalization of externalities, and (3) use of a social discount rate. In addition, public sector investment analyses are increasingly being required to identify the distribution of benefits and costs as well as aggregate measures of project feasibility. These impact distribution analyses often possess a geographic dimension.

For transportation and logistics system projects the geographic distribution of impacts on an area’s population and economy may be significant. Improvements in accessibility for one area resulting from transportation system investment often result in the transfer of economic activity from one location to another. Also, location of an improvement often has a direct bearing on the cost of the project. Thirdly, social and environmental impacts typically vary by location. Thus, conducting economic feasibility analysis within a GIS environment greatly enhances the quality and content of project evaluations.

Multimodal Investment Analysis: Phase 1 Contents

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