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85 S t e p 8 8.1 Goal The goal of this step is to identify and analyze additional indirect effects (often referred to as WEBs) in order to provide a full accounting of the final effects of the project(s). Analysts have argued that factors beyond route and mode shifts that lead to induced demand and cost changes (i.e., logistics and supply chain decisions, trade shifts, changes in O-D and markets) can lead to higher-order indirect effects, which should be included when evaluating freight projects of a large geographic scale. All other indirect effects or externalities not additional to the direct effects are, by definition, redistributive effects, which are part of EIA. WEBs are higher-order network externalities or indirect effects and arise for transformative or strategic projects that are geared to introducing large cost or access changes. These additional effects can sometimes be up to 25â30% of the direct effects at the area- or corridor-wide level. In terms of macroeconomic effects (regional, statewide, or national), this ratio can be higher (i.e., economic effects do not always land where the traffic originates). The development of inland terminals and logistics hubs that link line-haul networks to different gateway points specifically belong in this category. Projects such as these are designed to specifically shift trade by creating access to new markets. WEBs can also work negatively, when factors like lack of competition can cause restrictions in production and output. United Kingdom practice now identifies three sources of additional wider-economy impacts (conceptually, they may be positive or negative), all of which are discussed in the context of pas- senger travel but the concepts are equally applicable to freight investments. These sources are: â¢ Agglomeration economies: external economies of access to economic mass. â¢ Imperfect competition benefits due to output effects in markets where prices diverge from marginal costs. â¢ Market failures in the input (labor) market. If any of these market failures are present (in the markets for freight transport itself, input markets they rely on, or secondary markets that the transport serves, such as product markets), then a conventional partial BCA that considers only the transport markets in isolation from other indirect markets (e.g., product markets) will produce biased estimates of the economic value of a transport project. The reasoning is that lower transport costs are assumed to translate into increased production and lower prices, thus benefitting society. Lower costs can be expected to increase production in both imperfectly competitive sectors and in more competitive indus- tries. Part of this additional production (GDP) will flow to the national government in the form of taxes to be available for spending on other projects. Conventional BCA does not include this effect because it considers only the primary transport market, so a WEB perspective adds it as an additional benefit. Analyze Higher-Order Quantifiable Metrics
86 Guide for Conducting Benefit-Cost Analyses of Multimodal, Multijurisdictional Freight Corridor Investments In principle, a fully specified BCA would need to take a general equilibrium approach, examining surpluses that occur in each of the relevant markets, and then adjust these surpluses for double counting where relevant. In the United Kingdom, however, these are approxi- mated in a simpler additive framework (instead of a general equilibrium model) where posi- tive or negative externality benefits are included on the benefit side of the BCA. This additive approach has now also been adopted in Australia, New Zealand, and the United States (for highway projects only). Certain key clarifications must be recognized for the additionality of WEBs to hold: â¢ WEBs arise from causal linkages between flows, benefits, and their manifestations in other markets. WEBs are valued in terms of productivity gains accruing to existing users (higher output per unit of input). â¢ Large-scale projects with network effects are candidates for WEBs, as well as projects with strategic changes in connectivity. Table 13 shows the overall relation of BCA metrics, WEBs, and EIA; the order of occurrence; and the induced or indirect nature of metrics. Temporal Order of Occurrence Metrics Temporal Nature and Effects on Firm Output/Productivity of Transport-Using Sectors Comments BCA, EIA, orWEB First-order benefits Conventional TEE metrics: Change in travel/transit time/transport cost Change in operating costs including fuel to carriers, operators, and shippers (sometimes included as logistical cost savings) Travel time reliability Public externalities: Accident/safety effects Environmental effects (emissions and air quality) Short term (immediately following the opening of the project). Assumes firm output (Q) is change in O-D volume. Route shifts and diversions. Conventional BCA. Second- order benefits (induced, indirect network effects) Logistical cost changes leading to reorganization effect O-D linkages between production and consumption regions Medium term (3â5 years). Firm output (Q) is fixed. O-D volume may change (68) Applicable in certain circumstances. Of value for intermodal and multimodal movements. WEB. Hard-to- capture WEBs for all modes. Third-order benefits (induced) Gains from additional reorganization effects such as locational effects and product variability Longer term (5â10 years). Firm output. O-D volume changes. Occurs in different markets (land and product markets) WEB. Hardest- to-capture WEBs. Indirect Effects Not Part of BCA Other effects Effects that are not considered benefits according to the conventional BCA but can be of interest to policy makersâjobs, regional employment, personal income, and gross regional product Occur in different markets and time frames; different from actual investment EIA and fiscal impact analysis. Supporting analysis. - constant (Q ). No Table 13. Types of benefits, timing of occurrence, and relation to BCA, EIA, and WEBs.
Analyze Higher-Order Quantifiable Metrics 87 8.2 Tasks Determine Whether WEBs Should Be Considered Determine whether the conditions are present in the scenarios to be analyzed for WEBs to be considered. Four such conditions can arise in large-scale projects: â¢ Induced demand from network effects. â¢ Market structure in transport or transport-using markets. â¢ Logistical or supply chain implications. â¢ Trade shifts and changes in O-D or changes in markets such as projects that develop inter- modal terminals along existing or new line-haul networks. As noted in Step 4, these can be identified from (a) induced demand estimation itself from the type of models, data, assumptions, and BCA impact area, or (b) presence of supporting fac- tors such as access and connectivity, or transport and shipping costs changes that also influence latent demand (new users) and lead to productivity implications for existing users. Documenting and measuring supporting metrics such as cost changes brought about by the transport project is of value in justifying the presence of WEBs; however, the metrics need to be used with formal methods to value them in the context of BCA while differentiating between existing users and new users. In principle, general equilibrium methods are the state of the art in addressing many of these valuation considerations. The state of the practice, however, is to address these as addi- tive components when a compelling case can be made for the presence of induced demand and supporting metrics. The development of tools to measure supporting metrics is not uniform across modes. Select WEBs to Analyze Select the specific sources of WEBs to be included in the BCA. Consider benefit triggers when considering WEBs. Also understand the corridor conditions and modal competition in the alter- native corridors when considering WEBs. Internationally, the research on WEBs has occurred along different lines than in the United States. The United Kingdom has assumed that WEBs can be approximated in an additive manner. Their research has therefore been guided by the devel- opment of methods that can support additive consideration of such effects primarily directed to passenger travel. Some other countries (e.g., Australia and New Zealand) have adopted methods similar to the United Kingdomâs. The European Union and Netherlands, on the other hand, have invested in the development of macro- or system-level network tools (integrated models) that allow consideration of both user benefits and WEBs simultaneously using spatial comput- able equilibrium models. In the United States, two broad categories of WEBs have been investi- gated that may be included in freight-based BCA: (1) reorganization and supply chain logistics effects and (2) trade shifts. They are also assumed to be considered additively or as additional to conventional benefits discussed in Steps 5 and 6. Reorganization and Supply Chain Logistics Effects Reorganization effects refer to network externalities from projects that reduce transportation costs in one or more modes. Reductions in transport costs can lead to first-order benefits in one or more modes, which, in turn, can lead to reductions in the cost of production and can influence carrier demand. In the longer run, these cost reductions can be felt in other related markets (e.g., industrial shipper markets or product markets). In the case of shippers, research shows that cost reductions have the potential to generate reorganization benefits for non-marginal investments. In a perfectly competitive market, there is no reason to believe there would be reorganization since all firms should receive normal profits. However, the reduction in transport costs may allow firms in an industry to take advantage of cost reductions to reorganize production and
88 Guide for Conducting Benefit-Cost Analyses of Multimodal, Multijurisdictional Freight Corridor Investments distribution costs. Eventually, this may lead to spatial concentration. A focus on conventional user benefits may lead to ignoring these longer-term logistical and supply chain effects. While these effects are difficult to model, some advances have been made in this arenaâall directed at quantifying benefits to existing users. For instance, these effects have been investigated for highways, but there is no established methodology for addressing these supply chain effects for other modes. Therefore, this effect should only be considered if highway improvements are under consideration. Highway Freight Logistics Reorganization Tool (HFLRT). FHWA-sponsored research on the reorganization effects of truck freight corridors (69) and led to a web-based BCA tool (HFLRT) that is designed to capture higher-order logistical reorganization effects to existing shippers, carriers, and receivers. The tool takes into account the idea that reducing freight costs lowers production costs and increases market demand for freight carriers. The tool is designed for a national level of analysis of such effects and is suited for handling segment-level analysis of multistate corridors, but is not readily adapted for use in other modes. NCHRP Report 786. NCHRP Report 786 (70), on the other hand, provides a process instead of a tool that aims to capture WEBs induced from logistical cost effects accruing to existing users. This framework relies on quantifying logistics costs and the link to longer-term productivity driven by a benefit transfer approach. The guidance in this report notes that freight projects should be considered for productivity implications. Boston Logistics Approach. Cambridge Systematics et al. (18) developed a third approach for quantifying supply chain benefits for large-scale investments, which are assumed to be those that have associated economic development effects and require the consideration of public benefits. These alternate methods are presented in Appendix I. Reorganization and/or supply chain effects should be examined for large-scale multijuris- dictional highway corridors. All of these methods rely on assumptions, past literature, and a stepwise process to calculate effects within a broad additive approach. These approaches attempt to capture somewhat similar effects, although NCHRP Report 786 specifically attempts to link logistics costs to long-term productivity implications. Trade ShiftsâAccess-to-New Markets (New or Generated Moves from Different Origins and New Destinations) The ability to cut production costs (and influence regional, state, or national productivity) can be significant when transportation investments open up new markets. Access or connectivity to international gateways influences freight generation and land use and can also lead to generated demand. Terminal Access Connectivity Tool (TACT). TACT is a sketch plan tool developed under SHRP2 C11 by ICF Consulting and is suitable for evaluating last-mile highway connectivity changes. The outputs of the tool include vehicle hours saved via improved access, an index of connectivity importance as a way to proxy improvements in market reach (or number of mar- kets potentially accessible), and value of time savings scaled by the importance of the terminal. NCHRP Report 786 notes that, if the truck freight traffic share is greater than 12%, TACT should be used to document the connectivity index. NCHRP Report 786 provides intermodal connec- tivity elasticity factors of .01 (for airport and port freight terminals connectivity projects) and 0.005 for freight rail terminals to translate the change in connectivity index for existing users to a measure of productivity gains. The intermodal elasticities measure the percent change in value added in response to a 1% change in the access or connectivity index. An important caveat is that
Analyze Higher-Order Quantifiable Metrics 89 the tool is developed primarily for addressing highway connectivity to terminals; a second caveat is that the elasticity factor ignores lags and assumes the productivity gain is instantaneous, which could lead to overestimation if results are not phased out over time. Change in the Regional Access or Capture Area. For intermodal terminals, first establish the change in the regional access or capture area for the inland hub using the impact area delinea- tion procedures outlined in Step 3 for intermodal facilities. These opportunities should first be identified as part of a conceptual analysis. Benefits and Growth Factor. Use an informal process for determining the benefits, where it is assumed that some of the first-order benefits will trickle down to longer-term productivity gains. Intermodal productivity elasticities suggested by NCHRP Research 786 can be a starting point to consider such benefits for existing users as part of a sensitivity analysis. Next, phase these benefits out over time. For example a project that improves access to a freight terminal will only see benefits after completion. Finally, new users (induced demand) can also be a source of significant WEBs if major bottlenecks are addressed or new linkages are made to input sourcing markets. In a conceptual analysis, a growth factor can be used to determine the additional induced demand for the corridor facility. This growth factor should be applied to container demand at the terminals that can be considered new or induced demand that is generated for the facility. Con- tainer lift capacity should be considered when analyzing potential demand as well as the presence of competing facilities within the dray radius. A practical approach in the near term is to treat this as a scenario analysis. It is best to use modal own-price elasticities to develop terminal container growth rates under the preliminary assumption that the introduction of a project will not alter transport shipping costs of other routes or modes in the region. If, however, the project alters transport costs of other modes, mode choice elasticities provide a way to develop scenarios. In principle, it would be difficult to determine the network effects of a facility without a formal simulation model. This is a nascent and emerging area, and there are no established procedures for handling induced demand from the consideration of inland logistical facilities providing hinterland access and their associated benefits directly in BCA. Rail. In the context of rail, network simulation and optimization models exist; however, they are resource intensive. These models should be reserved for use in feasibility analysis where greater detail is warranted. Biases in Forecasted Volumes. Valuation of WEBs has been directed to highway auto pas- sengers and transit, with limited attention to freight overall. The failure to consider WEBs for strategic and large projects can lead to biases in TEE metrics. The problem is magnified by the use of fixed-trip demand models (see Step 4), aggregate data sources for forecasting flows for the scenarios, and/or BCA impact areas driven by the area of coverage of travel models that do not necessarily reflect the multiple jurisdictions with interest in the project or map the O-D served by the project. An example is the use of MPO models (which define the BCA impact area as the MPO region) for the evaluation of a corridor largely serving origins and destinations outside the MPO boundaries. A direct consequence of this is errors in estimates of user charges in multimodal contexts. Simply stated, revenue projections (often used in investment-grade studies or financial BCA) made on the basis of volumes derived from fixed-trip models need corrections, especially for out years in the forecast when diversions from another mode are involved. The problem is also related to scale and can be magnified in the context of multistate and national projects. Solutions to Estimating New Demand. One resource-intensive solution is to consider models with destination choice or logsum models that allow the transport cost effects to be
90 Guide for Conducting Benefit-Cost Analyses of Multimodal, Multijurisdictional Freight Corridor Investments captured in trip volumes by O-D pairs. A second solution is to use the growth factor as part of a scenario analysis suggested earlier. Perform Valuation of WEBs Analysts should consider WEBs for strategic, transformative, and otherwise large-scale proj- ects. The most prudent course is to evaluate induced demand from several sources to determine if TEE user benefits in BCA results are likely to underestimate or overestimate true net benefits. The key question to ask is: To what extent will the BCA be biased due to biases in estimation of new users or induced demand? There are essentially three independent methods of valuing different sources of WEBs exclud- ing imperfect competition. Each is designed for different contexts and based on different valu- ation methods. All seek to value induced logistical benefits as a longer-term enhancement in volume (as in HFLRT) or as a productivity enhancement (NCHRP Report 786) or as a cost savings (Boston Logistics approach). All methods seek to recommend an enhanced BCA. HFLRT. HFLRT (now part of the FHWA BCA tool) seeks to value logistical benefits from a national estimation of response elasticities for national highway corridors. It values: â¢ The immediate cost reduction to carriers and shippers. â¢ The impact of improved logistics while keeping output fixed. â¢ Increased demand. â¢ Product diversity. NCHRP Report 786. NCHRP Report 786 uses a market segmentation approach in combina- tion with costs and empirical estimates of productivity elasticities in relation to logistics drawn from literature that is generally applicable to any mode. The Boston Logistics Approach. The Boston Logistics approach is also somewhat similar but relies on a supply chain market segmentation and response elasticities drawn from company insights. REDYN and REMI. A final way of dealing with WEBs that allows the analyst to address market structure either on the freight market side or the product markets they serve is to rely on spatial computable equilibrium models that incorporate such market structure considerations and allow causality to be modeled. Currently, the only tools in the United States that allow addressing these are primarily REDYN developed by Tanner and Regional Economic Models, Inc. (REMI), which incorporate new economic geography into their computable general equilibrium models. The disadvantages of these models are that REDYN is no longer supported for such purposes, they are costly to use, they can be difficult for maintaining transparency, and they need advanced capabilities for both analysis and interpretation. The simpler market correction approach is suggested for conceptual analysis. Internationally, general transport and economy synchronized models have provided the way forward by way of spatial computable general equilibrium (SCGE) models [e.g., Com- putable General Equilibrium Model Europe (CGE Europe), Netherlands-RAEM,14 Finland- REgFin, Belgium-Rhomolo] to examine the interlinkages between transport and economy. The European Union has forged ahead with the merging of its transport models and economy model (Trans-tools). Conventional multiregional input-output (MRIO) models are not suited for these purposes. 14RAEM is the acronym for the Dutch spatial computable general equilibrium model.
Analyze Higher-Order Quantifiable Metrics 91 Consider Using Producer and Consumer Surplus for Capacity Contexts. For transforma- tional large-scale projects, when it is difficult to use logsums, consider using the surplus as a first approximation, since the demand for the project is a derived demand. This infers to the higher- order increase in industry profits occurring from market structure in product markets. This is not the same as producer surplus accruing to freight operators and producers. Commodity markets relying on freight transport are typically characterized by market imperfections. These include factor immobility, characteristics of imperfect competition and transaction costs associated with moves between raw materials, intermediate goods, and finished costs. In other words, there are transactions costs associated with transport chains of supply chain of commodities. A procedure shown in Equation 22 can be used to estimate WEBs for transformational projects relying on industry mark up factors and user benefits to shippers at the national scale. The theory of this is linked to price markup over marginal cost in product markets. It is complex to establish mark-ups or multipliers (m2) for commodity markets served by different modes; however the following steps can help address this: â¢ Use the freight flow analysis and distribution of cargo or commodities transported from earlier steps to identify the industries and the characteristics of the industry. â¢ Use values in the range of 0.1 (coal) to 0.5 (industries such as petroleum or agricultural com- modities that rely on multimodal transport) to quantify WEBs from firms passing on cost savings to final consumers. The markups are based on several factors including price elasticity of demand of the commodity, prices, and production costs (including transport costs), but as a measure of market power they suggest how industry can benefit from transport cost reduc- tions as long as the industries do not face restrictions in changing price or output. Transport providers such as rail are known to price discriminate based on commodity specific price-cost mark ups. â¢ Start with a very low value such as 0.1 subjected to sensitivity analysis along with an estimate of direct shipper or operator benefits from Step 6. â¢ Estimate WEBs from imperfect competition in product markets using Equation 22. Equa- tion 22 provides an order of magnitude approximation of potential productivity gains under the assumptions that industries respond to cost reductions by adjusting output at given level of factor inputs. ( )( )=WEB Surplus measures or Shipper & Operator Benefits (22)2mt t This guidance recommends the use of Equation 22 when conditions of imperfect competition are identified in product markets for addressing WEBs for existing users/shippers/operators. It recommends the use of growth factors for addressing the influence of induced demand (new users) from transport cost changes to approximate the potential size of WEBs through the use of rule of half. If spatial general equilibrium models or logistics models can be developed that capture some of the factors described in this chapter, they can be used. However, this guidance also recommends that this is an area warranting further research. 8.3 Double Counting with EIA and BCA Do not include benefits from indirect effects from sources other than user benefits in other indirect markets (e.g., productivity effects, tax revenues, personal income, and jobs). These are part of EIA. Projects that are driven by economic development considerations (e.g., terminals, ports, and inland ports) should consider these benefits separately since they are significant drivers. On the other hand, if the project meets the WEB-induced demand conditions, analyze WEBs to avoid a potential bias in BCA benefits when possible.
92 Guide for Conducting Benefit-Cost Analyses of Multimodal, Multijurisdictional Freight Corridor Investments 8.4 Inputs: Recommended Tools and Data Sources WEBs can be considered as both epistemic, aleatory uncertainty and deeper uncertainty. A number of tools can assist the analyst with the analysis of higher-order quantifiable metrics in the near term: â¢ Table 13. â¢ SHRP2 C11 TACT toolkit (truck freight) (http://www.tpics.us/tools/). â¢ FHWA BCA tool with HFLRT (truck freight) (http://ops.fhwa.dot.gov/freight/freight_analysis/ econ_methods/bca_logistics/index.htm). â¢ Step 5 tools: travel models, integrated models, and activity models. â¢ Appendix I tools (Cambridge Systematics and Boston Logistics Group) and NCHRP Report 786. â¢ Producer surplus approximation for large scale urban to rural transport projects. 8.5 Best Practices and Examples Best practices for Step 8: â¢ Clearly discuss the reasons and motivations for including one or more WEB(s). â¢ Consider WEBs for large-scale transformational projects (also known as non-marginal projects). â¢ Report BCA without WEBs. If WEBs can be quantified, present them separately so as to not exaggerate benefits and the net present value of a project. â¢ Accompany WEBs with a clear treatment of sensitivity and risk analysis. â¢ Use models that capture the assumptions regarding the source of WEBs. â¢ Recognize that WEBs are in a developmental stage, and allow flexibility in accommodating measures, metrics, and valuation methods as long as they are able to demonstrate causality, net additionality, and linkages to broader economic effects such as productivity. â¢ Apply the rule of half to new user benefits generated by WEB conditions. Note: There are few examples associated with WEBs in the context of freight, but there is significant literature that links trade and transport costs to logistical efficiencies and deals with linkages to product and other markets. Example 1: The United Kingdom WebTAG guidance provides a clear framework for consid- ering WEBs. While it is not suitable for freight projects, the reporting process for WEBs can be extended to multimodal multijurisdictional BCA. Example 2: NCHRP Report 786 provides an example of the use of TACT for the evaluation of a hypothetical new highway project connecting to an airport terminal for passengers and freight cargo. The report demonstrates the use of the tool along with the use of inputs with and without a travel demand model. This tool and the HFLRT tool have not been used in any actual application. Example 3: Jacoby and Hodge (71) and Cambridge Systematics et al. (72) applied the sup- ply chain logistics approach to evaluate the effect of the Baltimore Freight Rail Bypass project. They evaluated the effect of the proposed alignment using five categories of benefits including supply chain effects: â¢ Existing freight rail: the benefits for existing freight rail operators from reduced travel times and the removal of bottlenecks. â¢ Shipper cost savings. â¢ Highway benefits: the benefits to the highway system from reduced future truck vehicle miles traveled.
Analyze Higher-Order Quantifiable Metrics 93 â¢ Passenger rail time savings: the benefits for existing Amtrak users who would experience an improvement in travel time through the region. â¢ Supply chain benefits: the supply chain benefits that shippers would enjoy due to the infra- structure improvements. These include access to lower-cost supply sources, the consolidation of facilities, and the reduction of inventory through smaller-order quantities. A commodity-chain-specific percentage increase is added to direct freight system user benefits that are associated with supply chain improvements. Three effects are considered (see Appendix I and Table I3 of this guidebook): â¢ A possible reduction in material costs, stemming from cost-effective access to lower-cost supply sources. â¢ The consolidation of plants due to extended market reach. â¢ The reduction of inventory through smaller, more frequent order quantities. Freight flows are categorized into six supply chain categories: â¢ Continuous flow manufacturing. â¢ Make-to-stock manufacturing. â¢ Design-to-order manufacturing. â¢ Distribution. â¢ Retailing. â¢ Extraction. Once the freight benefits are estimated and extracted, supply chain benefits are estimated and assigned to each industry based on employment-based weights for each of the six categories. Lastly, the analysis uses the parameters estimated by Boston Logistics Group to calculate poten- tial second-order industry logistics effects. For each of the three types of freight-related direct impacts, the supply chain benefits are estimated as a percent of the reduction in transportation cost. Index values for the relative amount of externally purchased materials, fixed asset intensity, and value of inventory are provided. These values allow for the quantification of all three impacts for each shipper type. Then, a sensitivity analysis is conducted. Table 14 presents a summary of the results of the consideration of supply chain WEBs in the evaluation of this project. Assumptions were: â¢ $2.5 billion capital costs. â¢ Annual operations and maintenances costs were 3% of capital costs. Maryland Benefits (without Supply Chain Benefits) National Benefits Excluding User and Supply Chain Benefits Scenario 1 (Freight Distance 300â500) National Benefits Excluding Supply Chain Benefits Total National Benefits Freight rail operators $270,229,331 $270,229,331 $270,229,331 $270,229,331 Shipper costs â $1,052,304,268 $1,655,796,822 $1,655,796,822 Amtrak $176,187,771 $625,621,147 $625,621,147 Highway benefits $564,591,640 â $873,653,722 $873,653,722 Supply chain benefits â â â $1,303,373,082 Total benefits â $2,063,313,010 $2,551,647,300 $3,425,301,022 Total costs â $3,046,338,138 $3,046,338,138 $3,046,338,138 Table 14. Baltimore rail bypass case example of higher-order supply chain benefits.
94 Guide for Conducting Benefit-Cost Analyses of Multimodal, Multijurisdictional Freight Corridor Investments â¢ 25-year analysis period 2010â2035 (residual values considered). â¢ For sensitivity measures, one scenario assumes average distances for rail through trips versus distances traversed for in-state trips of 300 to 500 miles. A second scenario assumes 500 and 750 miles. (Results shown in Table 14 are for Scenario 1.) There is one important problem with this exampleâthe approach does not clarify if these are benefits to existing users, new users, or both. 8.6 Common Mistakes Common mistakes occur when the project team: 1. Does not include both logistical implications on productivity and supply chain benefits. These factors are intended to proxy similar WEB linkages. 2. Includes WEBs for smaller-scale projects that lead to significant changes in the cost of trans- port and that may have lower potential for creating major shifts of corridor volumes (e.g., corridors associated with grade separation improvements alone).