GENERATING COUNTY-LEVEL FREIGHT DATA USING FREIGHT ANALYSIS FRAMEWORK (FAF2.2) FOR REGIONAL TRUCK EMISSIONS ESTIMATION

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1 GENERATING COUNTY-LEEL REIGHT DATA USING REIGHT ANALYSIS RAMEWORK (A2.2) OR REGIONAL TRUCK EMISSIONS ESTIMATION BY Minyan Ruan PhD andidate, Department of Civil and Materials Engineering, University of Illinois at Chiago, 842 W. Taylor Street (M/C 246), Chiago, IL, 60607, U. S. A. address: (M. Ruan) Jie (Jane) Lin Assistant Professor, Corresponding author. Department of Civil and Materials Engineering, University of Illinois at Chiago, 842 W. Taylor Street (M/C 246), Chiago, IL, 60607, U. S. A., Tel: , fax: , addresses: (J. Lin) Number of Words: 5,79 Number of Tables: 5 Number of igures: 6 Corresponding Author

2 Ruan and Lin 2 ABSTRACT Current publily available national ommodity flow database (e.g., reight Analysis ramework, Commodity low Survey, et.) is restrited to the state or metropolitan region as a whole, hene is of little use to regional freight transportation planning, whih requires detailed data at a sub-regional level (e.g., ounty, traffi analysis zone (TAZ)). High-resolution proprietary databases (e.g., TRANSEARCH database), typially at the ounty level, provide limited information on their methodologies and validations. Some loal agenies have onduted regional surveys to obtain freight data in areas as small as the TAZ or ZIP ode level, but high ost and unertainties assoiated with them are hindrane to the availability and usefulness of the data. Therefore, the objetive of this paper is to explore different synthesis methodologies to generate high geographi resolution freight outbound shipment data. Three methods are investigated, (i.e., proportional weighting, diret regression and optimal disaggregation model. Proportional weighting is the most popular and straightforward synthesis method used in existing freight studies, followed by diret regression method. Optimal disaggregation models are developed in eonomi researh and applied in freight data in this paper. A ase study of mahinery outbound shipment values in State of Wisonsin suggests that proportional weighting and optimal disaggregation are promising tools. Limitations of the methods and future researh are also identified at the end of the paper. The outome of this researh will be a set of appliable algorithms and national ounty level ommodity flow database that an be used for regional truk emissions estimation. INTRODUCTION reight ativities in the United States ontinue to grow at a rapid pae. This inrease, along with the ontinuing growth in passenger vehile miles, plae unpreedented strain on the nation s already hoking highway system. Compared to passenger travel demand modeling, however, freight modeling in long-range regional transportation planning is rather primitive. One of the biggest diffiulties in freight modeling is the issue assoiated with freight data. As the National Cooperative Highway Researh Program (NCHRP) Synthesis 298 () points out, The inreased importane of truk ativity both in transportation engineering and planning has reated a need for truk-oriented analytial tools. A partiular planning need is for trip generation data that an be used to estimate truk traffi patterns, beginning with the ability to estimate truk trips generated by a variety of ommon land uses. However, the urrent state of the pratie in truk trip generation data falls short of the needs of today s transportation engineers and transportation planners. Muh of the freight movement and ommodity shipment information is proprietary and guarded with strit onfidentiality. Aggregate data, usually at the state level, is publily available but of limited use for regional and loal foreasting. Commerially available data produts, e.g., TRANSEARCH by Global Insight, In and IMPLAN by Minnesota IMPLAN Group, are prohibitedly expensive for aademi researh. More importantly, the raw data and the data reation methodologies are a blak box to the publi, a least desirable situation to aademi researhers. Currently, the best publi data soures are the state level Commodity low Survey (CS) and the

3 Ruan and Lin 3 ederal Highway Administration s reight Analysis ramework (A), an enhaned version of CS available at the A region level. A regions are geographi units omposed of 4 domesti regions, 7 import and export gateways and 7 international regions (2). Domesti regions are defined based on metropolitan statistial areas (MSAs), onsolidated metropolitan statistial areas (CMSAs), and states or balanes of states. In addition, a few regions have performed regional freight surveys, suh as the Tampa Bay goods Movement Survey, Atlanta Area Commerial ehile Survey, et. Overall, the limited availability of good freight data at sub-state levels imposes a big hallenge in freight modeling. This paper is aimed at exploring existing and potential synthesis methodologies in disaggregating freight data available in large geographi areas into small geographi areas. Setion 2 briefly introdues the existing nationwide freight data, and setion 3 present the existing synthesis studies on freight data. In setion 4, three synthesis methods are explored, followed by the ase study of mahinery outbound shipments in Wisonsin in Setion 5. Setion 6 onludes the study and suggests the future work. BRIE OERIEW O EISTING REIGHT DATA Commodity low Survey 2002 (CS 2002) The CS 2002 (3) is part of the Eonomi Census performed by the U.S. Census Bureau and U.S. Department of Commere (DOC), partnered with the Bureau of Transportation Statistis (BTS) and the U.S. Department of Transportation (DOT). The survey ontains a total of 50,000 establishment samples of ommodity shipment information (weight, value, ommodity ategory, and transportation mode) originated and destined in 50 states and Washington DC. The transportation modes inluded are truk (both private and for-hire truks), railroad, waterway (shallow draft, the Great Lakes and deep draft), air (inluding truk-to-air), pipeline, multi-mode (inluding parel, USPS or ourier, truk-water, water-rail, truk-rail, and other multiple modes) and other unspeified/unknown modes. The CS 2002 overs business establishments in manufature, mining, wholesale trade, seleted retail trade industries (i.e., eletroni stores, mail-order houses), and auxiliary establishments (i.e., warehouses and management offies), all of whih were oded based on the 997 North Amerian Industry Classifiation System (NAICS). Certain industrial setors were out-of-sope of the CS 2002, inluding servies, transportation, onstrution, and most retail industries. In addition, establishments like farms, fisheries, foreign- and most government-owned ones were exluded from the survey, although they belong to the in-the-sope industries. The shipment information from these establishments to proessing enters, whih are omprised primarily of loal and short-haul movements, was therefore exluded. or example, the shipments of agriulture produts from proessing failities to distribution enters were ounted in the CS but the shipments from farms to proessing enters or terminal elevators were not reported. Therefore, the survey overs only 54% of the total freight shipments in tons, 67% in ton-miles and 63% in dollar values. Publi aessible CS 2002 data provides the shipment distane and weight information (in tons, dollar values and ton-miles) in the state or metropolitan level by mode of transportation. More detailed state level origin-destination (O-D) matries by 43

4 Ruan and Lin 4 ommodity lasses and 7 modes/mode ombinations are available upon request. It is worth mentioning that those O-D matries ontain a number of empty ells. Besides the reason disussed above, an empty ell ould also be due to small sample size (and therefore produing unreliable estimates) or a true zero value. Speial attention must be paid to handling the empty ells when using the CS. Supplemental data soures are needed to fill in the missing information. TRANSEARCH Database TRANSEARCH may be onsidered the best available nationwide ounty level ommodity flow data soure. The 200 TRANSEARCH Database is derived from various data soures (up to 00 soures) on ommodity volumes (in the 4-digit STCC) and modal values in 72 business eonomi area (BEAs), 50 states and 3,45 U.S. ounties. Seven transportation modes are inluded, i.e., trukload, less-than-trukload, private motor arrier, rail arload, rail intermodal, waterborne and air. The most ruial input data soure is the Motor Carrier Data Exhange program, whih inludes the partiipation of trukload arriers and distributors in providing shipment information on size, origin-destination, and annual flows, et. TRANSEARCH provides the O-D matries in the state level that were then disaggregated into the BEA and the ounty level. The two-step disaggregation proedure involved separate alloation of ommodities based on employment shares at origins and employment and population shares at destinations. Mani and Prozzi (4) pointed out the main limitations of TRANSEARCH. oremost, there are inevitable biases in regions and types of ommodities due to limited number of partiipated arriers. Seond, detailed shipment information was available only in four modes, whih are truks, rail, domesti air and domesti waterborne. Shipment information was also exluded for international air and petroleum, unproessed agriulture and mining. Last but not the least, the validation information of the TRANSEARCH data was not available owing to the proprietary ownership. reight Analysis ramework (A) The ederal Highway Administration (HWA) s reight Analysis ramework (A) has been developed into ersion 2.2, A 2.2, whih is based on year 2002 and foreasts into years 200 through 2035 in a 5-year interval the ommodity flows origins and destinations, as well as the truk ounts on the national highway network. A is built on the CS within-sope and out-of-sope setors, and non-cs data soures, with CS as the ore database. Non-CS data soures inlude the ounts missing in CS but in other freight resoures like Waterborne ommere or Waybill, et. A ontains four datasets of ommodity movements, eah in terms of ommodity value and weight, in domesti ativities, trans-border ativities with Canada and Mexio, seaport international ativities and air international ativities. The domesti dataset ontains all freight movements between domesti A regions to be defined later in this paragraph, while all other datasets are assigned the international movements by mode with the origin or destination in a domesti A region. The international datasets ontain information of

5 Ruan and Lin 5 origin/destination A regions, port of entry or exit, mode of the domesti leg, and ommodity type in 2-digit SCTG. Speifially, the trans-border dataset aptures the ommodity movements between the U.S. and Canada and Mexio via land; the sea/air dataset desribes the international movements via water/airplane. A regions definition adopts the 4 domesti geographi regions from CS, whih inludes 64 metropolitan areas, and 50 other ompleted states or the rest of the states. In addition, 7 ruial international gateways not inluded in the metropolitan areas are seleted from over 400 gateways around the ountry. The foreign market is divided into 7 regions, Canada, Mexio, South Ameria, Asia, Europe, Middle East and the rest of the world. Colletively, there are a total of 38 A regions inlusive in the A O-D tables. EISTING REIGHT DATA SYNTHESIS STUDIES lorida Study A lorida state study (5) disaggregated the A regional ommodity O-Ds to the ounty level O-Ds. Regression models were fitted for the produtions and attrations respetively for eah ommodity group from the freight data in all the 4 domesti A regions, relating the prodution or attration annual tonnage with the 3-digit North Amerian Industry Classifiation System (NAICS) employment rates, population and total employment within the A region. Goodness of fit results range from 0.09 for the Coal attration to 0.94 for Lumber prodution. With the regression models, lorida ounty level produtions and attrations were obtained and the ounty-to-region ratios were alulated. The ounty level O-D matrix was then augmented by multiplying the A region O-Ds by the ounty-to-region ratios. The results in one A region were ompared with the TRANSEARCH data in terms of ounty perentage trends within the A region and found a fair degree of similarity. The authors defended their results by noting the disrepanies between A2.2 and TRANSEARCH. irst of all, the ommodity lassifiation systems, STCC and SCTG, are different. Seondly, TRANSEARCH ontains unlinked base ommodity trips while the A onsiders linked trips. In short, this study implemented a diret regression disaggregation method enabling the inlusion of multiple ovariates, but some ommodities produed poor goodness of fit, whih suggests the need for more robust disaggregation methods. New Jersey Study Another statewide (6) A data disaggregation projet was onduted in New Jersey, whih ontains three A regions. Using the matrix-disaggregate funtion in TRANSCAD, the authors explored the proportional weighting method to be explained in next setion by employing six separate sets of proportional parameters, inluding employment, population, inome, Truk ehile Miles Traveled (TMT), number of truks going out and in, et. Again, the results were ompared with TRANSEARCH. The authors laimed the results for all the models were aeptable, espeially for the model using truks going out and oming in, and the other using employment adjusted and Inome (25-54 yrs) adjusted TMT. The study is valuable in attempting different proportional surrogate variables to disaggregating

6 Ruan and Lin 6 ommodity flows from A regions to ounties, but finer disaggregation methods need to be explored further. SYNTHESIS METHODS Proportional Weighting The most widely used alloation method is proportional weighting, whih alloates values from a soure unit to target units proportional to some surrogate variable. The soure unit typially ontains the target units geographially. The relationship of the surrogate variable with the variable of interest is ruial to the errors of the results. The steps used to perform proportional weighting are as follows: Step : Identify the appropriate surrogate variable. Step 2: Calulate the values of the surrogate variable in the ounties and sum them up to the A regions. Step 3: Calulate the perent proportions of eah hild ounty to the parent A region by the surrogate variable. Step 4: Alloate the A region ommodity outbound totals proportionally to ounties based on the perentage shares as a result of Step 3. The seletion of the surrogate variable is ritial to the quality of disaggregation results. Past studies like the Stammer and Pratt s (7) found employment by industry was a reasonable surrogate variable. A Wisonsin study (8) alloated ommodity flows to ounties based on employment share by produing eonomi setor. Another study in Los Angeles metropolitan area (9) alloated prodution and attration ommodities based on employment share by produing and onsuming setor. In this researh, total employment of industries related to the ommodity of interest is used as the surrogate variable in proportional weighting. Diret Regression The diret regression method, similar to that in Chang and Liu s study (0), involves two steps: first, an outbound shipment regression model is developed for the A regions, and then the model is applied to estimating the ounty level outbound shipments. The underlying assumption is that the same relationship between the outbound shipments and explanatory variables applies to both an A region and its inlusive ounties. The linear relationship used in diret regression is expressed as follows: Y β u () Y β u (2) where Y is a vetor of the A regional total outbound shipment values; Y is a vetor of

7 Ruan and Lin 7 the ounty total outbound shipment values; is a vetor of the A regional explanatory variables; estimated in (). is a vetor of the ounty explanatory variables. Coeffiient vetor β is Optimal Disaggregation Model The diret regression method may produe unbalaned estimations between the upper and lower levels. That is, there is no guarantee the sum of the lower level (i.e., ounties) estimates returns the value of the upper level (i.e., the A region ontaining those ounties). To address this problem, we adopt an optimal disaggregation model proposed by Chow and Lin (). It is essentially a best linear unbiased regression models, one at the ounty level and the other at the A regional level, linked by a disaggregation matrix that onverts the A regional value to the inlusive ounty values. By introduing the disaggregation matrix it is guaranteed that the ounty level estimates will add up to the regional total. The best linear unbiased regression is estimated by minimizing the trae of the ovariane matrix of residuals. In mathematial language, the optimum disaggregation model is formulated as follows. Suppose Y β (3) u where Y is a vetor of A regional values of a ommodity type; is a matrix of independent variables; u is the error vetor of the A region model. Let Y be the vetor of the ounty total values of the same ommodity type and u be the error vetor of the ounty model. Introdue also the disaggregate matrix D between an A region and inlusive ounties. Thus, ^ Y DY D( β u ) (4) ^ ( and, E Y Y ) E[D( β u ) ( β u )] (D )β 0 (5) rom (4) and (5), we have D 0 and Y Y Du u. So, ^ Y Y ) E[ (Du u )(Du u ) ] ov( (6) D D D D ^ where denotes E u u and denotes E. or the best linear unbiased u u ^ estimation, set ov( Y Y ) 0, and hene the trae of (6), tr D D D D ), ( is to be minimized. Solving for the minimal trae, we finally obtain the following:

8 Ruan and Lin 8 D ^ Y ^ β ( ( ) ) Y (I DYˆ βˆ ( )uˆ (7) ( ) ) and the A region model error vetor is ^ μ Y (I βˆ ( ) )Y (8) matries, All above parameters of interest are dependent on the variane and ovariane and struture to the error terms., whih must be estimated by assuming some variane-ovariane This will be elaborated further in the results setion. CASE STUDY IN WISCONSIN A pilot study is performed to investigate the three aforementioned disaggregation methods, i.e., proportional weighting, diret regression and the optimal disaggregation model, in alloating Wisonsin Mahinery outbound shipments into ounties. Wisonsin A Data The State of Wisonsin has two A regions and they are, as shown in igure, Zone 2: Milwaukee-Raine-Waukesha, whih ontains 5 ounties Zone 3: The rest of Wisonsin, whih ontains 67 ounties Table lists the top 0 ommodities that have the highest total outbound shipping value in Wisonsin. As an be seen, the ommodity with the highest outbound shipment value in Wisonsin is mahinery. Colletively, the sum of these 0 ommodities aounts for 64% of the total outbound shipments values of 43 ommodity types in Wisonsin. Pilot study will be performed for the top ommodity-mahinery.

9 Ruan and Lin 9 IGURE Wisonsin A Regions And Counties TABLE Commodity Outbound Shipment alues In The State of Wisonsin SCTG Commodity alue (Million Dollars) 34 Mahinery 34, Mixed freight 30, Other foodstuffs 26, Motorized vehiles 22, Eletronis 4, Mis. mfg. prods. 2, Artiles-base metal 0, Textiles/leather 9, Paper artiles 9, Unknown 8,746.7 Surrogate ariables In Proportional Weighting As suggested in the literature, total employment of an industry is a good indiator of the total ommodity outbound shipments in an area. Hene, the total employment in mahinery related industries is hosen as the surrogate variable proportional weighting disaggregation. Aording to the NAICS to SCTG mapping provided by HWA (2), the industries assoiated with mahinery prodution are listed in Table 2.

10 Ruan and Lin 0 TABLE 2 Mahinery Related Industries NAICS ode Desription All other misellaneous textile produt mills 360 Leather and Hide Tanning and inishing Sanitary Paper Produt Manufaturing Prepress Servies Hand and Edge Tool Manufaturing Power Boiler and Heat Exhanger Manufaturing Other Metal Container Manufaturing Mahine Shops Ball and Roller Bearing Manufaturing Industrial Pattern Manufaturing Constrution Mahinery Manufaturing 3333 Mining Mahinery and Equipment Manufaturing Oil and Gas ield Mahinery and Equipment Manufaturing Sawmill and Woodworking Mahinery Manufaturing Paper Industry Mahinery Manufaturing Textile Mahinery Manufaturing Printing Mahinery and Equipment Manufaturing ood Produt Mahinery Manufaturing All Other Industrial Mahinery Manufaturing 3333 Automati ending Mahine Manufaturing 3334 Air Purifiation Equipment Manufaturing Heating Equipment (exept Warm Air urnaes) Manufaturing Air-Conditioning and Warm Air Heating Equipment and Commerial and Industrial Refrigeration Equipment Manufaturing 3335 Industrial Mold Manufaturing Mahine Tool (Metal Cutting Types) Manufaturing Mahine Tool (Metal orming Types) Manufaturing Rolling Mill Mahinery and Equipment Manufaturing 3336 Turbine and Turbine Generator Set Units Manufaturing Mehanial Power Transmission Equipment Manufaturing

11 Ruan and Lin Other Engine Equipment Manufaturing Air and Gas Compressor Manufaturing Measuring and Dispensing Pump Manufaturing Elevator and Moving Stairway Manufaturing Conveyor and Conveying Equipment Manufaturing Overhead Traveling Crane, Hoist, and Monorail System Manufaturing Pakaging Mahinery Manufaturing Industrial Proess urnae and Oven Manufaturing luid Power Cylinder and Atuator Manufaturing luid Power Pump and Motor Manufaturing Sale and Balane (exept Laboratory) Manufaturing Analytial Laboratory Instrument Manufaturing Household Refrigerator and Home reezer Manufaturing Household Laundry Equipment Manufaturing 3362 Motor ehile Body Manufaturing Airraft Engine and Engine Parts Manufaturing Guided Missile and Spae ehile Propulsion Unit and Propulsion Unit Parts Manufaturing Wood Television, Radio, and Sewing Mahine Cabinet Manufaturing astener, Button, Needle, and Pin Manufaturing Possible Covariates In Diret Regression and Optimal Disaggregration Regression In this pilot study, possible ovariates for building the regression models are designated to be employment ounts (EMP) in related industries and number of inter-modal failities (see Table 3), inluding those listed in Table 2. The main advantage for using these variables, in addition to their wide suess in previous studies reviewed above, is the detailed and ompleted data that an be obtained from CBP Table 3 has inluded industries diretly related to four transportation modes (i.e., air, railroad, water, truk) that arry a majority of mahinery ommodity shipments. The numbers of workers in freight transportation related industries (e.g., sheduled freight air transportation, line-haul railroads, et.) are indiators of the sale of the freight industry. Besides, eleven freight transportation supporting ativities (servies) are onsidered, inluding rail transportation support ativities, port & harbor operations, freight transportation arrangement, et. Two other industries, general warehousing and storage and

12 Ruan and Lin 2 other warehousing and storage, are inluded to aount for ommodity shipments originated at warehouses. Lastly, the number of inter-modal failities suggests the sale of inter-modal freight shipments in the area. Inter-modal faility ounts data are from the National Atlas. TABLE 3 List Of Possible Covariates (In Addition To Table 2) Category Air Transportation Railroad Transportation Water Transportation Truk transportation Transportation support ativities Warehousing & storage Inter-modal aility Counts Desription EMP in Sheduled freight air transportation EMP in Nonsheduled hartered freight air trans EMP in Line-Haul Railroads EMP in Short Line Railroads EMP in Deep sea freight transportation EMP in Coastal & Great Lakes freight transportation EMP in Inland water freight transportation EMP in General freight truking, loal EMP in General freight truking, long-distane, TL EMP in General freight truking, long-distane, LTL EMP in Speialized freight (ex used) truking, loal EMP in Speialized freight (ex used) truking, Ldist EMP in Air traffi ontrol EMP in Other airport operations EMP in Other air transportation support ativities EMP in Rail transportation support ativities EMP in Port & harbor operations EMP in Marine argo handling EMP in Navigational servies to shipping EMP in Other water transportation support ativities EMP in Other road transportation support ativities EMP in reight transportation arrangement EMP in All other transportation support ativities EMP in General warehousing & storage EMP in Other warehousing & storage Number of inter-modal failities

13 Ruan and Lin 3 Model Results Proportional Weighting (PW) Total mahinery outbound shipment values in A region 2 and 3 are million dollars and million dollars. Total employments in all t48 industries listed in Table 2 are alulated for eah ounty. After that, the total employments in A region 2 and 3 are obtained by adding the inlusive ounties total employments respetively. Employment shares of eah ounty within the A region are then alulated by dividing the ounty total by that A region total. inally, the ounty outbound shipment values are alloated based on the employment shares of ounties. Aording to the proportional weighting results, outbound shipment values of the 72 ounties within Wisonsin range from 0 to million dollars, with mean value 482 million dollars. igure 2 below shows the value distribution of mahinery among Wisonsin ounties from the proportional weighting disaggregation results of A region data. Color oding in the map represents the normalized outbound shipment values of the ounties to the state total. The normalized TRANSEARCH estimates are olor oded in the same sale in igure 3. To ompare the result with that in TRANSEARCH, the ommodity oding must first math up beause TRANSEARCH uses the STCC oding system. Aording to the mapping provided in Quik Response reight Manual II (3), mahinery ommodities oded as SCTG 34 are essentially equivalent to STCC 35. Consequently, only the ommodities oded as 35 in TRANSEARCH are used for omparison. In omparison, the PW estimates show a onsistent spatial distribution pattern to that of the TRANSEARCH estimates. Mahinery outbound shipments in Wisonsin are highly onentrated in the Milwaukee-Raine-Waukesha Metropolitan area and nearby ounties loated in the southeast part, whih is home to several major transportation equipment and mahinery manufaturers in Wisonsin. Companies in these ategories inlude the Kohler Company, Rokwell Automation, Johnson Controls, Briggs & Stratton, Miller Eletri, Milwaukee Eletri Tool Company, Buyrus International. Counties in the north and southwest appear to have the least shares of the mahinery outbound shipments. In the west and northwest areas, the perentages of mahinery outbound shipments of the proportional results are generally a little higher than those of the TRANSEARCH database. One reason for this disrepany may be that the labor produtivity in these ounties is lower than the other ounties.

14 Ruan and Lin 4 IGURE 2 Proportional Weighting Results: County Mahinery Outbound Shipment alues Perentages Within Wisonsin IGURE 3 TRANSEARCH Results: County Mahinery Outbound Shipment Weights Perentages Within Wisonsin

15 Ruan and Lin 5 Diret Regression (DR) Model parameters were estimated using the stepwise linear regression fitting. Square transformations of some of the independent variables were found neessary after the initial model residuals were examined. After many rounds of trial and error, the final model speifiation is shown in Table 4. Only the ovariates with signifiant oeffiients are presented. In this model, the interept has been fored to zero beause it is assumed that a region with zero employment in any related industries does not produe or ship mahinery produts. The model has an adjusted R-square value of 0.834, whih means almost 85% of the variation in the data is aounted for in the final model. All model oeffiients signs are intuitively orret. The largest oeffiient value in freight transportation arrangement employment implies that the value per employment produes is the highest among all the mahinery related industries. Number of inter-modal failities is shown to be highly signifiantly and positively related to the outbound shipment values, suggesting that the presene of inter-modal failities in the same A region will inrease the total outbound shipment values. Likewise, employment in air purifiation equipment manufaturing, air and gas ompressor manufaturing are found to be positively related to outbound mahinery shipments values. TABLE 4 Diret Regression Model Speifiations Model Coeffiient Std. Error T-stat Sig. Employment of reight Transportation Arrangement Employment of Air Purifiation Equipment Manufaturing (Square) Employment of Air and Gas Compressor Manufaturing (Square) aility ounts (hundred) Dependent ariable: Mdol_total By applying the final model in Table 4, the ounty level outbound shipment total values are alulated for the State of Wisonsin. The normalized estimated results are shown in igure 4. Compared to igure 3, the southeast ounties in both plots are shown aounting for the largest portions. Overall, the diret regression results are shown quite a different pattern than the TRANSEARCH. There are several reasons to explain suh large disrepanies observed in the diret regression results. irstly and most importantly, diretly applying the regional model to alulating for the ounty values implies that the same model speifiation and the values of the oeffiients at the A regional level are transferable to the ounty level. This is

16 Ruan and Lin 6 undoubtedly a very strong assumption. or example, the number of employment in freight transportation arrangement in a ounty does not neessary translate into high outbound shipment values in that ounty, beause the servie may be provided in a nearby ounty within the same A region. In addition, employments in some small industries not inluded in the final model may in fat have a signifiant effet on ounties. Consider the following senario. One ounty produes one type of mahinery produt that is not produed in any other ounty of the same A region and the total outbound shipment value makes up a omparatively small portion of the total outbound shipment value of all mahinery produts. In suh a ase, that partiular industry produing the produt would mostly likely fall off the regional model suh that ounty s outbound shipment value would be underestimated. Lastly, the sizes of the inter-modal failities are not available. So the apaity of the inter-modal failities, whih is more ritial than the ount, is not available. This is an important limitation to the regression model. IGURE 4 Diret Regression Results: County Mahinery Outbound Shipment alues Perentages Within Wisonsin Optimal Disaggregation Model-Unorrelated Residual With ariane 2 (ODM) As stated in the synthesis methods setion, alulating the estimator Y^ requires the assumption of some struture to the residuals of the ounty regression. Supposed that the diret regression model speifiations in Table 4 in the A regional level, the optimal

17 Ruan and Lin 7 disaggregation model is estimated by assuming that ounty level residuals are unorrelated, eah with variane 2. Sine 2 2 E uu D σ and DD σ, in equation (7) will be redued to D uˆ, where ni is the number of ounties that the i th A region n i has, e.g., 5 for A region 2 and 67 for A region 3. This struture implies that the A region residuals are equally distributed into the ounties within the region. Estimation results are shown below in igure 5. As an be seen, the mahinery outbound shipment values perentages in some ounties (i.e., Washington, Ozaukee, Dunn Douglas, Kewaunee, et.) are more onsistent with the TRANSEARCH database than those in the DR model, suggesting that ODM enhaned the DR model in some extent. However, the estimations in some other ounties, like Lafayette, Burnett and Dane, are worse. IGURE 5 Optimal Disaggregation Model Results: County Mahinery Outbound Shipment alues Perentages Within Wisonsin Optimal Disaggregation Model -Unorrelated Residual With Proportional ariane (ODM_P) This study explored another model with residuals that are unorrelated but proportional variane. It is assumed that the residual variane is proportional to employment share within the A region. The model results are shown in igure 6. Notieably the ODM_P model has improved the results over the ODM model, suh as those of Lafayette and Menominee, while worsen those in Douglas, Rihland and Marinette.

18 Ruan and Lin 8 IGURE 6 Optimal Disaggregation Model With Proportional ariane Results: County Mahinery Outbound Shipment alues Perentages Within Wisonsin inally, Table 5 summarizes the orrelations of the models estimation results. Proportional weighting ranks the highest (0.9), while diret regression poorly related to the TRANSEARCH (0.48). Table 5 and the results plots above suggested that the optimal disaggregation models improve the diret regression results but still not outperformed by the proportional weighting method. TABLE 5: Correlation Of Synthesis Results TRANSEARCH.00 TRANSEARCH PW DR ODM ODM_P PW DR ODM ODM_P

19 Ruan and Lin 9 CONCLUSION AND UTURE WORK This paper explores three synthesis methods in generating high geographi resolution freight data, whih is of great value in the regional freight modeling. A ase study performed in mahinery outbound shipment values in Wisonsin shows that proportional weighting method provides relatively good quality results when ompared with TRANSEARCH database. Diret regression gives relutant results, mainly due to the strong assumption that the A region speifiations an be transferable to the ounty level. The optimal disaggregation models, assured the total sum equal the A region total, notieably improves the diret regression estimates. It is worth pointing out that the above onlusions are based on the omparisons with the TRANSEARCH data. More robust validation should be to ompare the estimated results with true regional samples (e.g., from a regional survey). There is ongoing investigation of these and other synthesis methods in other ommodity types and states. After that, shipments values will be onverted to trips by mode, based on whih the traffi assignment will be performed to obtain the ounty level MT. inally, the ounty level freight MT will be used as input to the MOES model for estimating the national ounty-level freight emissions inventories. ACKNOWLEDGEMENT This study is funded by the National Center for reight and Infrastruture Researh and Eduation (CIRE) at University of Wisonsin, Madison. We thank our olleague Dr. Kazuya Kawamura of Urban Transportation Center at the University of Illinois at Chiago for his support and insight to the subjet matter. REERENCE. National Cooperative Highway Researh Program Synthesis Truk Trip Generation Data: a synthesis of highway pratie. Transportation Researh Board, National Researh Counil. Washington D.C., reight Analysis ramework (A) ersion 2.2, User Guide. ederal Highway Administration (HWA), U.S. Department of Transportation (USDOT). Aess on May 7, Commodity low Survey (CS), Bureau of Transportation Statistis, Mani, A., Prozzi, J.: State-of-the-Pratie in reight Data: A Review of Available reight Data in the U.S. Projet Report for Development of Soures and Methods for Seuring Truk Travel Data in Texas (0-473-P2), iswanathan, K., Beagan, D., Mysore,. and Srinivasan, N.: Disaggregating A 2 Data or lorida: Methodology and Results. Transportation Researh Board Annual Meeting

20 Ruan and Lin 20 CD-ROM, National Researh Counil, Washington, D.C., Rowinski, J., Opie, K. and Spasovi, L.N.: Development of a Method to Disaggregate the 2002 A 2 Data Down to the County Level or New Jersey. Transportation Researh Board Annual Meeting CD-ROM, National Researh Counil, Washington, D.C., Stammer, R.E. and Pratt, G..: Statewide Modeling Praties and Prototype Statewide Model Development for Tennessee. inal Report Prepared for Tennessee Department of Transportation In Cooperation with U.S. Department of Transportation, ederal Highway Administration, April Sorratini, J.A. and Smith, R.L.Jr.: Development of a Statewide Truk Trip oreasting Model Based on Commodity lows and Input-Output Coeffiients. Journal of the Transportation Researh Board, Transportation Researh Reord 707, pp , isher, M., Ang-Olson, J. and La, A.: External Urban Truk Trips Based on Commodity lows: A Model. Journal of the Transportation Researh Board, Transportation Researh Reord 707, pp , Chang, C. and Liu, T. Monthly Estimates of Certain National Produt Components, , The Review of Eonomis and Statistis, III, , 95.. Chow, C., Lin, A. Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by related Series. The Review of Eonomis and Statistis, ol 53, No 4, , Nov Methodology for the reight Analysis ramework-2: oreasts of Inter-regional Commodity lows. ederal Highway Administration (HWA), U.S. Department of Transportation (USDOT). 5a. Aess on May 7, ederal Highway Administration: Quik Response reight Manual II. HWA Publiation Number: HWA-HOP-08_00 (EDL 4396), 2007.