Research in Transportation Business & Management 5 (2012) 92–98

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Research in Transportation Business & Management

A strategic decision model for evaluating inland freight hub locations

Suzanna Long a,⁎, Scott E. Grasman b,1

a Engineering Management and Systems Engineering Department, Missouri University of Science and Technology, 600 W. 14th St., Rolla, MO 65409, United Statesb Department of Industrial and Systems Engineering, Kate Gleason College of Engineering, Rochester Institute of Technology, 81 Lomb Memorial Drive, Rochester, NY 14623, United States

⁎ Corresponding author. Tel.: +1 573 341 7621.E-mail addresses: longsuz@mst.edu (S. Long), segeie

1 Tel.: +1 585 475 3952.

2210-5395/$ – see front matter © 2012 Elsevier Ltd. Allhttp://dx.doi.org/10.1016/j.rtbm.2012.11.004

a b s t r a c t

a r t i c l e i n f o

Article history:Received 14 May 2012Received in revised form 7 November 2012Accepted 8 November 2012Available online 7 December 2012

Keywords:Multimodal freight logisticsInland hubsStrategic decision makingSubject-matter experts

This research identifies criteria that can be used to evaluate existing or potential inland multimodal freighthubs. Inland hubs are essential for a more efficient freight distribution system and can play an importantrole in improving the livability and economic vitality of a region. The research uses data gathered fromsubject-matter experts to determine the relevant criteria needed to evaluate the location of inland freighthubs. Findings are then categorized in a multi-criteria decision framework. Decision makers can use the find-ings to identify factors that will provide strategic inputs that are vital to the location decision process.

© 2012 Elsevier Ltd. All rights reserved.

1. Introduction to inland hubs

The economic well-being of a country is increasingly dependentupon an efficiently operating freight transportation system. Inlandfreight hubs are developed in order to create a modal shift from roadtransport to rail or barge transport and to prevent the overcrowding ofseaport areas (Notteboom & Rodrigue, 2005; Rodrigue & Notteboom,2010a, 2010b). These hubs have the logistics capabilities to facilitatemodal transitions; thus, contributing to the reduction of highway con-gestion and increasing the efficiency of freight movement (Oberstart &DeFazio, 2008). However, developing the logistics capabilities of hubscan be resource intensive and the benefits from this development canbe difficult to predict. Limited criteria exist to evaluate hub locationsthat include an assessment of quantitative and qualitative metrics(Lipscomb, Long, & Schmidt, 2010). Because of the nature of transporta-tion, namely the costs involved with differing lengths of travel andmodes used, location and connectivity to the population are importantcriteria when considering the effectiveness of a particular inland hub.

North American gateway systems are typically located at well-established east andwest coast seaports and use the landbridge concepttomove products to inlandmarkets. These ports are typically connectedfor long-distance transport to inlandmarkets via rail (U.S. Army Corps ofEngineers, 2007). U.S. policy decisions allowing shippers to break bulkand clear customs at inland ports of entry, such as Kansas City, Missouri,provide effective mechanisms for by-passing delays at congestedocean-going ports of entry. As a second example, the collaborativepartnership between the Port of Prince Rupert and the inland Ports of

@rit.edu (S.E. Grasman).

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Chicago and Memphis, provides more efficient transport times and im-proved processing for shipments from Asia (Lipscomb & Long, 2008;Rodrigue & Notteboom, 2010b). Quantitative data, including the num-ber of interstate highways, navigable riverways, air freight terminals,and class I railroads serving the region, should be used along with qual-itative data, such as the support of development agencies and localindustry, to measure a region's logistics development potential. Inlandports, or hubs, are strategically positioned based on landbridge position-ing to coastal ports, connectivity at the proposed or existing location,regional economic viability with respect to industrial and consumermarkets, and government infrastructure (Lipscomb & Long, 2008).Priority levels between the criteria can be set based on the objectivesof the decision makers. Once these priority levels are set, specific waysto measure a region in each of the criterion can be used to accuratelymeasure the logistics capabilities and development potential of a particu-lar region.

The U.S. has the most extensive freight transportation network intheworld, with nearly three timesmore paved roadmiles and railroadsthan the next closest country (Research and Innovative TechnologyAdministration, 2010). The relatively larger area, lower population den-sity and highly populated urban areas of theU.S. put higher demands onthe network so the size of this network is justified; however, the freighttransportation capabilities of the U.S. are not invulnerable to deficien-cies. According to the U.S. Department of Transportation, the efficiencyof the transportation network is not growing apace with the volumes offreight utilizing the system. (Federal Highway Administration Office ofManagement and Operations, 2008) Because much of this freight vol-ume is international, ports of entry have experienced the highest levelsof congestion. This, in turn, has stimulated the development of inlandhubs. Although the congestion has been significantly reduced due tothe economic downturn, the impact that efficient freight transportation

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has on the economy remains vital (Research and Innovative TechnologyAdministration, 2010). It is essential that the system be improved to fa-cilitate economic growth and avoid delays caused by congestion. Inlandhubs relieve some of the congestion at ports of entry by allowing inter-national freight to be consolidated or deconsolidated in areas withexcess freight capacity. This has been documented in previous resultspublished by Hesse and Rodrigue (2004) and Lipscomb and Long(2008).

With projections of up to a seventy percent increase in freight vol-umes moving throughout the U.S. by 2020, addressing the issue offreight congestion will involve a mixture of adding capacity, preserv-ing existing infrastructure, and improving operating efficiencies. (U.S.Government Accountability Office, 2008) All three of these strategiescan be accomplished through the addition of new, strategically locat-ed inland hubs or the development of existing inland multimodalfreight hubs. Building an efficient network of inland freight hubswould therefore increase the efficiency of freight movement through-out the U.S.

Because inland hubs facilitate the exchange of freight betweenmodes, they also allow for better utilization among the transportationmodes. Therefore, it becomes important to locate and develop inlandhubs considering both the region's ability to facilitate freight activityand the overall impacts that freight activity will have on the region.This research will identify relevant evaluation criteria for inland freighthub development. Criteria are determined and validated throughin-depth interviews with freight transportation subject-matter expertsat existing inland hubs. The criteria presented illustrate the use of datafor evaluating freight hub location alternatives. Decision makers canuse these results to more effectively determine the options in evaluat-ing regional hub alternatives.

2. Literature on location theory for inland hubs

The economies of scale that are realized at inland hubs and theirability to facilitate multimodal movements serve as the basis for theirintroduction into the network (Campbell, 1996). Location theory pro-vides the foundation for this idea by explaining how space enters eco-nomic relationships. Specifically, it addresses transportation costs andtheir effects on the location decision. There is an incentive to economizetransportation activities because the associated costs directly affect theprices that a firmmust charge for its outputs. In its simplest form, loca-tion theory states that afirmwill decide to locate a facility based onhowitwill change theweight of its inputs. For example, a company that addsweight to their inputs will have an incentive to locate closer to the pointof consumption, whereas a company that decreases the weight of theirinputs will locate closer to the supply. Here, cost can be substituted forweight as there is an incentive to minimize total shipping costs byconverting relatively higher cost per unit freight, such as truck freight,to lower cost per unit freight, such as rail freight.

An inland hub, looked at as a single facility, can be considered both acost-decreasing and cost-increasing facility depending on whether thefreight is moving into or outside of a region. For freight moving outsideof the region, the inputs into the hub are high-cost drayage trucks thatserve the local freight market. The freight delivered by this truck isconverted into relatively lower cost per unit freight by being placedon a train or a barge and sent to its final destination. For freight movinginto a region, the freight arrives as relatively lower per unit cost train orbarge freight and is converted into high-cost drayage freight. In both in-stances it is most efficient to locate the facility close to both the demandand supply of drayage freight. The Fermat–Weber location problemintroduces the problem of locating facilities optimally by finding thegeometric mean of a graph given cost and distance data. However, noexplicit formula exists to solve for this location.

The popularity of just-in-time inventories places additional pressureon the freight transportation system by demanding flexibility and quickresponsiveness. Truck transportation is considered the most flexible

while air transportation is the fastest; however, these modes also de-mand a higher per unit cost of transporting the goods compared withrail and barge transportation. The balance of speed and cost of deliverymust be considered since they will have major implications for thekind of infrastructure needed. Grasman (2006) details a quantitative re-searchmodeling study that determines which combination of transportmodes will minimize either cost or lead time.

As the freight network expands, both regional developers and privatebusinesseswill need amethod for assessing the transportation strengthsand weaknesses of a region. Regional developers want to leveragestrengths and address weaknesses while businesses want to identifythe location that best suits them for their transportation-related activi-ties. Developing inland freight hubs is resource-intensive and there isrisk involved with possible under-utilization; therefore, the location ofthese developmentsmust be chosen considering awide range of factors.

Researchers have approached location problems with a variety ofquantitative models. Limbourg and Jourquin (2008) use integer pro-gramming to locate facilities with the goal of minimizing total transpor-tation costs. This method not only uses aggregated supply and demandpoints, but also accounts for commodityflows and their geographic loca-tion in order to determine the optimal location of multimodal terminalson a given network. Melkote and Daskin (2001) also use integer pro-gramming but identify changes to the network topology along withidentifying potential facility locations. Arnold, Peeters, and Thomas(2004) formulate the location problem as a binary linear program, butsolve it using a heuristic approach. Racunica and Wynter (2005) alsouse heuristics in their model and allow for non-linear and concave costfunctions.

Existing tools including location theory and other quantitative loca-tion decisionmodels provide guidance for hub locations, but do not pro-vide qualitative information regarding livability and sustainability thatare vital for determining community readiness. In order to obtain a ho-listic view of the location decision, rather than a purely quantitativeview,Murthy andMohle (2001) suggest that good performance criteriashould include both quantitative and qualitativemeasures as applicableto the project, and Bontekoning, Macharis, and Trip (2004) extensivelyreviewed current multimodal research and recognized that a moremultidisciplinary approach is needed in modeling multimodal terminallocation decisions. Management and policy theory were the two areasthey identified that needed to be considered more thoroughly.Multi-criteria decision analysis (MCDA) serves as a good tool formodel-ing freight-related development decisions because of its flexibility tocombine different types of data and different viewpoints from experts(Macharis, 2005). Piantanakulchai and Saengkhao (2003) use the Ana-lytic Hierarchy Process (AHP) in conjunctionwith a Geographical Infor-mation System (GIS) to aid in location and alignment decisions.

The AHP has also been used as a way to gather input from differentstakeholders of potential transportation development projects (Dooms&Macharis, 2003;Macharis, 2005). Sirikijpanichkul (2007) presents a de-cisionmodel that specifically addresses the location issue and attempts toselect the optimum location based on the needs of stakeholders. Doomsand Mecharis (2003) presents a similar model that takes into accountthe short and long-term objectives of multiple stakeholders, but it doesnot specifically address the location decision. This model identifies thekey stakeholders in the port's long term strategy and a way to includethese parties in the decision making. Henesey, Notteboom, andDavidson (2003) also use this approach and incorporate Multi AgentBased Simulation to provide a foundation for inland hub decisionmakers.

The needs of all the stakeholders involved in a multimodal terminallocation project can be complex. Quantitative modeling tends to maxi-mize the benefits of the users and operators of terminals without con-sideration for community impacts. Community concerns often includeenvironmental, economic, and quality of life effects of the project. Envi-ronmental and land use impacts have been identified (Litman, 1995;McCalla, Slack, & Comtois, 2001), but quantifying the effects of these im-pacts is difficult. The economic effects of transportation facilities are

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often unclear due to the complexities of these impacts. Although amoreefficient freight networkwould be beneficial for any region, the possibleside effects of multimodal terminals, such as noise pollution, decreasedland values, and stimulation of urban sprawl, can outweigh these bene-fits (Litman, 1995; Litman 2007). Likewise, if jobs are created as a resultof increased multimodal development, but traffic congestion increases,the net effect of the development itself could be negative.

Finding the balance point between all of the relevant criteria canbe difficult and, often, a partnering opportunity can enhance a goodlocation's potential or even supersede a deficient location's disadvan-tages. Lipscomb and Long (2008) suggest that hub development deci-sions should take advantage of the synergies created throughstrategic partnerships. They specifically cite the partnership betweenthe Port of Prince Rupert, Canadian National railroad, and the Port ofMemphis as a development that was effective because of both the lo-cation factors and the collaboration that took place between theseorganizations.

Because of the wide range of factors that affect the decision to eithercreate new inland freight hub locations or further develop existing hubs,multi-criteria decision analysis (MCDA) is an effective way to considerall of the relevant criteria as well as all of the relevant stakeholders.MCDA is a tool with strong benefit for scenarios with multiple levels ofdecision, large stakeholder components, and significant qualitative in-puts. Current models describe the methodology for evaluating and lo-cating hubs, but they do not provide a description of how to developthe relevant criteria and how to rank alternatives based on these criteria.

3. Research questions and methods

This research considers a multi-criteria approach to inland hub lo-cation decisions as a means of creating a decision making frameworkcapable of integrating both quantitative and qualitative inputs. Re-search outcomes include an identification of strategic factors orcriteria for inland hub evaluation. In addition, the value of creating amulti-tiered approach to the decision process that is considered anda process for selecting metrics is explored.

3.1. Subject-matter expert interviews

Interviews were conducted with 18 transportation professionals ac-tively working with multimodal freight. Respondents were selected togain perspective frommultiple categories of experts including econom-ic development, freight managers, state departments of transportation(DOTs), facility administration, port authority representatives, and mu-nicipal planning organizations (MPOs). The interviews conducted forthis research provided expert insights into the characteristics necessaryfor hub development. The respondents in the interviews were identi-fied through contacts with six transportation-oriented organizationsfrom three inland hub locations, Kansas City, MO, Louisville, KY, andMemphis, TN. These organizations represented both the public and pri-vate sectors and included transportation engineering consultants,non-profit economic development organizations, and port authorities.The respondents were interviewed for their perspectives on what con-tributed to a region's logistics capabilities and the information gatheredfrom these interviews was used in determining both the characteristicsof logistics hubs and the level of importance of these characteristics. Thecumulative responses from each respondent categorywere compiled tocreate a single response representing each organization. This was doneto further protect anonymity of response.

A closed-ended questionnairewas considered but ultimately rejectedto remove interviewer bias. Many factors contribute to the freight trans-portation capabilities of a region so it was important not to direct thefocus of the respondent. Instead, a narrative interview protocol wasestablishedusing open-endedquestions designed to encourage thought-ful responses by subject-matter experts. The narrative protocol includedbasic demographics (position, education level achieved, number of years

in the transportation freight industry), regional characteristics (econom-ic metrics, workforce supply, policy constraints or incentives), facilitycharacteristics (size, multimodal connectivity, freight flows by type andvolume) before switching to discussions of factors impacting hub loca-tion evaluative criteria. Interviews typically were 30 min to an hour inlength and began with a general question about which factors they feltcontributed the most to the development of an inland freight hub.Then, they were asked to elaborate on these factors so the researcherscould understand them better and determine how they could be mea-sured. Subsequent questioning was designed to determine in-depth re-sponses to assist with model development and analysis. Details wereused to determine weighting factors and pair-wise comparisons thatcan be used with the model.

3.2. Relevant criteria identification from subject-matter expert interviews

Table 1 maps the top criteria identified by each organization torelevant data sources and metrics. The criteria with the highest re-sponse rates from subject matter respondents were physical infra-structure and proximity to population. The respondents emphasizedthese as the fundamental elements that influenced the capabilitiesof inland hubs. Infrastructure is made up of the roads, railroads, air-ports, and multimodal terminals that give a region access to markets.Richardson (2005) reinforces the interview responses by identifyinginfrastructure along with availability of rail service and road infra-structure capacities as factors that affect the sustainability of anytransportation system.

Proximity to market represents how close to a region are the supplyand demand of freight. These factors have some interaction with eachother because a larger population reach will call for better transporta-tion infrastructure, and better infrastructure will increase region's ac-cessibility to its surrounding population. Both of these elements arebasic factors in determining the development potential of a multimodallogistics hub and if a region is deficient in one of these areas, its abilitiesfor logistics development will be severely diminished.

Land availability was identified by half of the respondent organiza-tions. This aspect represented the expansion capabilities of a region.Without available land for warehouses, terminals, and other relatedbuildings or infrastructure, the development opportunities would stag-nate. This is especially evident at the West Coast ports of Los Angelesand Long Beach. Although this area is a key hub for freight cominginto the U.S., the development potential here is relatively non-existentbecause there is no room for expansion.

Government and industry supports were also mentioned in the in-terviews and supported by Richardson (2005) as important factors tothe sustainability of inland hub regions and transportation systems ingeneral. The support from the government was said to play a big rolein accelerating the progression of logistics projects from the conceptual-ization stage to the building and implementation stage. Regions thathave strong government developmental agencies are able to attract lo-gistics development because the project implementation process is veryefficient. These agencies also serve as connection points between the re-gion and other organizations looking for good locations to locatelogistics-related facilities. Often, location consultants are hired to findthe best location for a business and these regional development agen-cies can help provide the necessary data to these consultants so thatthey can make an informed decision.

The supply of laborwas alsomentioned as a variable in hub develop-ment. Without a supply of quality workers who could operate equip-ment to move the freight and manage the overall freight system, theregion's logistics capabilities would be significantly diminished.

Relevant characteristics outside of the top three factors all exploredsome element of inland hub effectiveness. The community characteris-tics of a region and the history of industrial development there will playa big role in the community's attitudes towards logistics developmentactivities. One interviewed expert referred to their region's history as

Table 1Summary of criteria identified by subject matter experts.

Criteria Description Measurement method Data sources

Infrastructure Capacity to move freight access totransport modes

Identify highways, railroads, waterways, airports,and multimodal terminals

Infrastructure maps, U.S. Dept.of Transportation

Proximity tomarket

Market reach, one-day market reach Find population within 600 mile radius of alternativeregion

U.S. Census Bureau

Land availability Land available for transportationlogistics development

Identify vacant land, buildings/land available forre-development, etc.

Region-specific real estatedata

Govt. andindustry support

Government support of transportationdevelopments and size of regionaltransportation/distribution industry

Identify regional economic development councils,especially those with transportation emphasis. Findthe number and size(by revenue or employment) of local industry.

Region-specific data ongovernment organizationsand industries

Labor supply Industrial labor supply able to meetexpanding transportation developments

Identify the proportion of a region's workers that havethe skills for transportation jobs

Bureau of Labor Statistics

Origin/destination distances Distance between freight flows to andfrom a region

Use freight flow data to compare the near optimallocation with the region's actual location

Freight Analysis Framework,FHWA

Congestion Delays in freight movement caused bycongested traffic

Use congestion indices to measure congestion levels offreight significant corridors. Other corridors will requireprimary data collection from local experts.

American TransportationResearch Institute

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a transportation hub and cited this as a critical factor in the acceptancefrom the community of expansion and development. Another expertexplained that a large portion of their region's population wereemployed in freight related occupations, so development was expectedand encouraged because of the job opportunities that were usually cre-ated. Both of these situations contribute to the public's general under-standing and acceptance of logistics-related developments.

Table 1 provides a summary for the identified criteria.

4. Results: strategic decision model

4.1. Model Formulation

The decision to devote resources to logistics developments in aspecific region must consider many factors that are both quantitativeand qualitative in nature, and the decision must consider a variety ofstakeholders. Purely quantitative models or purely qualitative discus-sions do not give the decision maker a comprehensive view of the de-velopment opportunities in a region. Therefore, it is important tocombine the two sources into one model to accommodate the needsof different stakeholders. The criteria developed from this researchcan be easily integrated into a strategic decision model. A variety ofwell-documented analysis tools exist for evaluating the strategic de-cision model developed. The Analytic Hierarchy Process, discussedin Section 2, is one such method. Fig. 1 presents a preliminary strate-gic decision model using the criteria that have been established in thisresearch. The model was compiled and validated through further dis-cussion with subject matter experts using an iterative, Delphi-stylemethod. The ordering of the model and the category tiers wererefined based on feedback until the consensus was reached. Themodel can be used to best consider hub location decisions duringthe evaluation or planning process.

The first and second level criteria established by this research serveas the decision anchors for the model and are weighted most heavily inthe decision process. Related decision factors are indicated below therelevant criteria. Linkages exist between the primary and sub-criteria,but are not indicated as part of the model. Proper weightings for theselinkages should be established through future research to fully utilizethe decision model. The model presented in Fig. 1 is intended as astarting point for the development of additional lower level criteriabased on regional scenarios.

The use of the model includes five steps: (1) determine the objectivefor thedecisionmodel (2) identify relevant criteria for judging regional al-ternatives (3) validate the criteriawith subjectmatter experts (4) identifythe alternatives and (5) judge the alternatives based on the criteria.

(1) Specify the objectiveBecause of the wide range of applications for the AHP, it is im-portant to first identify the overall objective for the decisionmodel. Here, the goal is to evaluate existing or potential inlandhubs based on which location presents the best alternative foroverall logistics development and potential.

(2) Determine the relevant criteria to use when judging the alterna-tivesThis step utilizes existing secondary data, observations, anddata gathered from subject-matter experts to determine whatcharacteristics a multimodal logistics hub location needs topossess in order to be effective.

(3) Validate criteria with subject matter expertsCriteria are validated through the use of iterative discussionswith subject matter experts. The relative importance of eachcharacteristic is then determined with pair-wise comparisonsbetween all of the criteria as developed through the interviewprocess.

(4) Identify alternativesIdentify inland hub locations based on comparable populations,infrastructure, logistics development initiatives, and locationfor providing improved capacity and regional developmentthrough a freight corridor of interest.

(5) Judge the alternatives based on the criteriaAt this point, the hierarchy is complete and the alternatives areready to be evaluated and compared against each other throughpair-wise comparisons.

4.2. Discussion

In this section, a discussion of criteria identified through inter-views with subject matter experts and how they are connected tothe strategic decision model is explored in greater detail. This discus-sion further details the final recommendations of subject matter ex-perts regarding tiers, or levels of criteria. Two levels of criteria arepresented. The first level criteria were identified directly from thenarrative interviews. In addition, second level criteria that were out-side of the top responses are presented, but still had significant rank-ing or were established in the literature. Each criterion is given adefinitive name, a description, and a specific measurement method.These criteria form a preliminary framework that can be used in anMCDA/AHP style analysis of inland hub locations. Rather than provid-ing a unique solution, the decision model presented in Fig. 1 is used toidentify alternatives and evaluate options using fact-based regionalcriteria.

Fig. 1. Strategic decision model.

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4.2.1. First-level criteria

4.2.1.1. Infrastructure. This criterion measures a region's capacity tomove freight and access to different transport modes. A regionwith bet-ter access to highways, railroads, etc. will be more capable of supportingnew logistics developments. This criterion includes comprehensive anal-ysis of access to renewable energy sources and sustainable technology.

Infrastructure can be measured simply by identifying the high-ways, railroads, and waterways and the existing airports and multi-modal terminals in the region and determining the capacity thateach one can handle.

4.2.1.2. Proximity to market. This criterion identifies themarket reach ofa region. The unofficial standard for this, mentioned by one respondent,was the one-day market reach by truck. Based on average truck speedson major freight corridors (U.S. Department of Transportation FederalHighway Administration, 2006) and hours of operation rules for truckdrivers (Federal Motor Carrier Safety Administration, 2010) whichallow for eleven hours of driving per day, one-day travel distance fortrucks is approximately 600 miles. The population located within thisdistance from a given region is its proximity to market measurement.This criterion also includes analysis of appropriate modal selection toaddress issues of environmental sustainability.

4.2.1.3. Land availability. This criterion measures the ability of a regionto expand. New logistics developments will likely require more landand infrastructure so alternatives with excess capacity will be more ca-pable of supporting new logistics developments. Included in an analysisof this criterion is an evaluation of land usage and appropriateness fordevelopment based on environmental factors and protected land classi-fications or status.

4.2.1.4. Government and industry support. This criterion measures thelevel of support that logistics developments get from both regional eco-nomic development agencies and local industry. Alternatives that havestrong support from both of these groups will be more receptive tologistics developments.

Industrial development was measured based on the existence ofdedicated logistics development organizations in the region and the

supporting logistics industry, such as distribution and warehousingfirms.

4.2.1.5. Labor supply. This criterion takes into account the demographicsof a region. Areas that are made up mostly of industrial laborers andhave a history of industrial developmentwill bemost receptive to logis-tics developments.

Regional demographic information gathered from the Bureau ofLabor Statistics can be used to understand its employment characteris-tics. Of the total non-farm employment, the proportion of people withjobs in manufacturing, trade, transportation and utilities, and mining,logging and construction can be used as ameasure of the region's indus-trial worker population.

4.2.2. Second-level criteria

4.2.2.1. Distance between origin and destination. Although none of theinterview respondents explicitly stated that “distance between originand destination” was an important variable for hub evaluation, it isclosely related to the supply and demand aspects of market reach.Richardson (2005) identifies this as an indicator of sustainability. Build-ing from this concept, freightflowdata can be analyzed as ameasure foran inland freight hub. The Federal Highway Administration compilesfreight data from several different sources to make estimates on freightflows between regions. The result is an origin-destination matrix thatshows the amount of freight, by tonnage and dollar value, moving be-tween 114 regions and 17 international gateways within the U.S. Thisdata can be used to measure economic sustainability and evaluates aproposed freight location with regards to its historic freight flows.This indicates potential cost–benefit relationships that are involvedwithmoving freight into and out of the region at a proposed or existinglocation.

4.2.2.2. Congestion. Congestion was not specifically mentioned by therespondents, but there is considerable research to support this factoras relevant to inland hub success. Government studies highlighting thesignificance of freight congestion at ports and distribution hubs includereports from the Federal HighwayAdministrationOffice ofManagementand Operations (2008) and the U.S. Government Accountability Office

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(Oberstart & DeFazio, 2008). Richardson (2005) also suggests that con-gestion is amain indicator of transportation sustainability. TheAmericanTransportation Research Institute (ATRI) measures congestion in whatthey consider freight significant corridors. (American TransportationResearch Institute, 2008) In their annual reports, ATRI uses data collectedfrom wireless onboard communication systems within trucks to gatherinformation about truck position and speed. Each of the corridors thatthey analyze is given a “Total freight congestion value” that is calculatedas the sum of the hourly product of miles per hour below free flow andvehicle population by hour.

The criteria identified through this research provide a roadmap forfreight hub location evaluation. Rather than responding to a list ofpre-determined factors, the subject matter experts interviewed inthis research self-selected phrases, issues, and relevant factors topresent. The vast majority stressed the importance of understandingthe regulatory and societal issues facing freight hub location, includ-ing community readiness, environmental sustainability and economicvitality.

5. Conclusions and implications for practice

Decisions to locate new logistics facilities or infrastructure generallyinvolve significant resources and a variety of stakeholder groups. Deter-mining which criteria are the most important must be done with all ofthe stakeholders in mind. For instance, a private railroad companywill have different priorities than the community in which they wantto locate a new facility. The railroad company will be more focused ontheir profits while the communitywill be focused on the economic ben-efits that they will receive and the environmental costs that they willincur. Therefore, it is important to gain an accurate perspective fromeach stakeholder group to determine the priority that each identifiedcriteria should receive.

It is apparent that only looking at one criterion is not sufficient for get-ting a comprehensive look whether or not a location can serve the pres-ent and future needs of the transportation system. Rather, all of thecriteria must be considered according to the needs of the stakeholders.

This research identifies “best practices” from existing multimodalfacilities that can aid developers of new locations in evaluating thepotential of a region for improving multimodal freight capabilitiesand stimulating regional economic growth. The criteria identifiedprovide an important process for determining the sustainability of apotential site as a long-term multimodal freight hub based on quanti-tative factors, such as freight flows, labor supply, and existing infra-structure and qualitative factors, such as community readiness andlivability.

Hub development decisions often come down to a few alternativesthat seem very close in their development potential. Using the criteriaidentified in this research along with a multi-criteria decision analysistool allows decision makers to more effectively make distinctions be-tween inland freight hub capabilities. Developers looking for a locationfor their transportation-reliant activities can use this procedure tomakethese distinctions between their alternatives and choose the locationbased on their specific needs. Regional development organizationsor local governments can also use this process to see how theirregion is being compared against the others in terms of multimodaltransportation capabilities and determine what areas should be slatedfor improvement.

Overall, the criteria developed in this research provide a solid basisfor determining the strengths and weaknesses of a region for multi-modal hub development. The importance of each criterion and the al-ternatives chosen for comparison will vary based on the conditionsand decision makers, but the criteria are relevant for all hub develop-ment decisions.

The methodology presented in this research considers many im-portant aspects of inland freight hubs, but it relies heavily on havingaccurate data for full implementation. Freight data is not nearly as

complete as it could be and further research into getting more accu-rate and more up-to-date data is warranted. There is also value inobtainingmore perspectives relating to the criteria that are importantto measuring the sustainability of inland hubs. Additional researchshould expand the number of subject matter expert interviews inorder to validate or modify the criteria established in this research.In addition, evidence of co-linearity, proper weightings betweenprimary and secondary criteria should be established to fully utilizethe model.

Acknowledgments

The authors would like to express gratitude to the University of Mis-souri Research Board and the Missouri Department of Transportationfor partially funding this research and to the subject-matter expertswho provided key insights during narrative interviews that were vitalto framing well-grounded conclusions. Finally, we wish to thank theanonymous reviewers for their insights and assistance with significantlyimproving the quality and usefulness of this manuscript.

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