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101 Assignment modeled flows and the target flows. For example, the lack of intracounty traffic being assigned by the model to the road- The daily truck trip table was assigned to the highway ways will consistently give low estimates because the traffic network using a FORTRAN program that used an "all or count data includes these flows. The overall model explained nothing" assignment procedure based on the travel time 48% of the variation in total commercial traffic using the between zones. Based on initial results, adjusted speeds were flows assigned at 40 rural locations. developed based on the following formula to account for the over-assignment of vehicles to interstate links compared RAIL ASSIGNMENT to other roadways: New Speed = Old Speed + (2 (65-Old Speed)) No route segment-specific data on rail flows was available to which the assigned values could be compared. A visual exami- Rail assignment procedures were somewhat different nation of rail flows was made to assess their reasonableness. because rail carriers tend to consider the use of mainline trackage as an equal or more important variable than the Model Application directness of the route. For this reason, a new "cost of move- ment" variable was developed for rail that incorporated a The Indiana Commodity Transport Model has not been distance minimizing component as well as a component applied to date, although the 1998 year trip tables crated by related to the magnitude of volume of the rail-line. This the model are being used as the basis for the development of measure lessens the length of line segments by dividing the InDOT's freight truck trip table in an update of the Indiana segment by its traffic density and takes the form: Statewide Travel Demand Model, now under development. I = (L(1/(D + 1))) Performance Measures and Evaluation where I = the index of spatial separation; No performance measures were developed for this research L = the length of the line segment of the network; and, model. D = the traffic density of the line in millions of gross ton- miles per year. 8.9 Case Study Florida Intermodal Statewide Highway Freight Model Validation Model (FISHFM) Trip Generation Background Context No data was available to validate the trip generation model. In 2001, the State of Florida had a gross state product of nearly $500 billion, or 5% of the gross domestic product of Trip Distribution the United States.23 If Florida were a separate country, its No data was available to validate the trip distribution economy would be the 12th largest in the world, larger than model. that of India, South Korea, Netherlands, and Australia.24 The U.S. Census Bureau's CFS shows that in 1997 $214 billion of goods shipments representing 397 million tons originated in Mode Choice Florida. The CFS also indicates that of those shipments 73% No data was available to validate the mode choice model. by value and 78% by weight moved by truck. In 1997, Florida's seaports and airports handled $64 billion of exports and imports, with trucks the predominant mode of transport Modal Assignment to and from these facilities.25 A study by Cambridge System- atics, Inc. for the Florida Chamber of Commerce, Transporta- TRUCK ASSIGNMENT tion Cornerstone Florida, concluded that the key to the state's The 21 categories of goods were aggregated to create total economic growth and competitiveness is an efficient inter- flow trip tables assigned to the roadway network using an all modal transportation system. Transportation costs, including or nothing assignment procedure. The resulting truck vol- trucking, currently constitute 5% of the price of goods both umes were then compared against actual traffic count data on nationally and in Florida. Indiana's highways from 1991 to 1994. Adjustments were The Florida Department of Transportation (FDOT), recog- made to account for inherent inconsistencies between the nizing the importance of intermodal freight in the state's econ-

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102 omy, has advanced the freight planning process by sponsoring Many truck trips in Florida begin or end at intermodal ter- the Florida Freight Stakeholders Task Force and initiating a minals, either as long-distance movements or as short-haul Strategic Intermodal System (SIS) Plan. A map of the SIS is connections between intermodal terminals. Because rail, air, shown in Figure 8.16. Transportation Cornerstone Florida calls and water serve as important components of the freight sys- for focused investment on trade corridors and international tem, the model determines how freight traffic is allocated and gateways and greater attention to freight mobility and eco- routed among all freight modes in order to produce truck nomic development in the planning process. forecasts. While a primary purpose of the model is to forecast truck volumes on highways, the data and forecasts of other Objective and Purpose of the Model freight modes are important as well. FISHFM was designed to support the project-related work of FDOT and Florida's metropolitan planning organizations, General Approach which are required by Federal law to consider factors of Model Class freight mobility. The purpose of the model was to identify deficiencies and needs and to test solutions on major freight The FISHFM is a four-step commodity forecasting model. corridors throughout the state. These freight corridors suffer Florida has a statewide highway model in which total truck from considerable congestion as they pass through metropol- trips are forecasted based on total employment and are itan areas. For example, I-95 in South Florida is not only a assigned together with auto trips. An existing four-step model major international freight corridor, it is also the main thor- for passenger auto and total truck traffic provided the state oughfare for local travel in major metropolitan areas, includ- zone structure, highway network, and employment data that ing Miami, Daytona and Jacksonville. I-4 in Central Florida served as the structure for developing the commodity model. is heavily used by both truckers and tourists and is the site of The four-step commodity forecasting model is described in a growing high-technology industry. In addition, the local detail in Section 6.4. highway connections between major freight corridors and intermodal terminals --warehouses, seaports, and airports-- are often the weakest link in the intermodal highway chain. Modes The truck freight model will be integrated with MPO trans- Even though the primary purpose of the FISHFM was portation models to ensure that needs and deficiencies at the to analyze freight truck traffic, the model development rec- local level that impact efficient freight transportation can eas- ognized that over 80% of the freight by tonnage serving ily be identified. Florida's major commercial airports, deepwater ports, and rail container terminals is transported by truck. These inter- modal facilities generate significant truck volumes at concen- trated locations. The model development further recognized that the rail, water, and air freight systems are important competitors to truck freight. Understanding the demands of other modes was deemed a critical component of the model development. A primary purpose of FISHFM was to forecast truck volumes on highways. However, the data and forecasts of other freight modes also were determined to be valuable as FDOT prepares to implement a Statewide Intermodal Systems Plan and re- sponds to its Transportation Land Use Study Committee's recommendation that the Florida Intrastate Highway System (FIHS) be expanded to a Florida Intermodal Transportation System (FITS) covering all modes. Markets Trucking in Florida consists of very different markets: Source: Strategic Intermodal System Plan, Florida Department of Transportation, long-haul interstate/international, intrastate, private/for- April 2004. hire, truckload/less-than-truckload, local/metropolitan de- Figure 8.16. Florida's Strategic Intermodal System. livery, and drayage (truck shipment between ports, airports,

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103 and rail terminals). These markets have different needs, use demand estimation model. Base year values for these data different vehicles (combination vehicles versus panel trucks) are used to calibrate the trip generation (production and at- and are sensitive to different variables. Based on the data traction) equations. Forecast values for these data are then available to support the development of the model and the used in the generation (production and attraction) equa- role of MPOs in planning for local/metropolitan delivery, the tions to predict the number of freight trips that will be gen- markets selected for inclusion in FISHFM were interregional erated in future years. freight shipments within Florida, drayage movement to and Population serves as an input variable in the trip genera- from intermodal terminals, and interstate freight shipments tion (attraction) equations. Population is one of the key of all kinds. In order to properly account for the various char- variables that determine regionwide consumption of goods acteristics influencing the interstate shipment of freight, the originating from other areas of Florida and nationwide. Base model had to cover all of North America, although at a level year data were collected from the U.S. Census Bureau's 1998 of zone and network detail more geographically aggregated U.S. Census of population, Florida MPOs, local planning de- than that for Florida alone. partments, and FSUTMS data (ZDATA1) sets. Future year data were forecast from Florida's Long-Term Economic Framework Forecast, Florida Population Studies-population projections for Florida counties, MPO forecasts, and FSUTMS data Florida's Model Task Force decided that the structure of (ZDATA1) forecasts. the FISHFM should follow the basic framework of the four- Employment by commodity sector serves as an independ- step Florida Standard Urban Transportation Model Structure ent variable in trip generation (production and attraction) (FSUTMS) passenger process. This requires that tons of com- equations for freight tonnage produced and attracted by modities be generated and distributed and that a mode split commodity group. Employment data by industry code are component be used to determine which tons are shipped by the principal explanatory variables in the trip generation truck and other modes. Truck trips identified in the mode equations. Base year data were collected from the Regional split process then are assigned to the statewide highway net- Economic Information System (employment by standard work. All model components operate as part of the FSUTMS industrial classification, or SIC), County Business Patterns software. Following the FSTUMS approach results in a model (SIC employment by county), SIC employees by TAZ, that is easily understood by users and ensures compatibility Florida MPOs, local planning departments, FSUTMS data with FSUTMS and the statewide passenger model. (ZDATA2) sets, and the Florida Department of Labor. Future year data were estimated using the Florida Long-Term TRUCK TYPES Economic Forecast. The FISHFM focuses primarily on long-distance commod- ity freight movements. It captures large trucks moving on the FORECAST GROWTH OF EXTERNAL MARKETS FIHS, the shipment of commodities between regions in While population and employment were chosen to be the Florida, and the shipment of freight between Florida and the forecasting data for freight shipments to and from Florida rest of North America. These truck trips currently represent TAZs, the data were not available or suitable to forecast freight about 25% of the total truck trips in Florida, but 45% of the shipments for the zones located outside Florida. For these total truck vehicle-miles traveled within the state. These zones, freight forecasts were developed by factoring existing freight movements are surveyed as part of Reebie Associates' flows using the growth rates by industry and state provided by TRANSEARCH database. The FISHFM does not address the Bureau of Economic Analysis's BEA Projections to 2045. local delivery or service trucks, which primarily serve regional markets and are best modeled at the regional or urban area level as part of the MPO planning process. As such, FISHFM Modal Networks does not attempt to model the two-axle trucks not commonly FREIGHT MODAL NETWORKS used in commodity freight shipments. While the FISHFM is a multimodal commodity model, Data the assignments were only to be made to a highway network. Information from the other modal networks, such as dis- Forecasting Data tances, travel times, or costs, was inferred from the highway network. The highway network for Florida was the existing BASE AND FORECAST YEAR SOCIOECONOMIC DATA Statewide Model highway network to ensure compatibility The forecasting data include population and employ- with that model. The highway network outside Florida was ment, used as input to the trip generation step of a freight drawn from the NHPN, as shown in Figure 8.17.

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104 ever, the 1997 CFS uses a different system, the SCTG. To allow the direct use of the value information by STCC commodity the 1993 CFS, which also used the STCC system, was used to de- velop values per ton which were adjusted to 1998 dollars using the Consumer Price Index for those years. DAILY VEHICLES FROM LOAD WEIGHTS AND DAYS OF OPERATION Commodity flow data are given in terms of tons per year. Because transportation planning functions require model out- put in the form of vehicles (trucks) per day, it is necessary to determine the amount of goods carried in a vehicle and the number of vehicle operation days in a year. Payloads in tons per day were obtained from the U.S. Census Bureau's VIUS. Figure 8.17. Highway network for Florida Inter- modal Statewide Highway Freight Model. Validation Data Validation data consisted of the truck counts by vehicle INTERMODAL TERMINAL DATA (SEAPORTS, RAIL YARDS, AIRPORTS) class. Classification truck counts on highways are needed to separate truck traffic from passenger car traffic. Truck counts The location of the intermodal terminals (X Y coordinate by vehicle class were used for the validation of the model- or zip code) and the activity (ton shipments from/to for both estimated truck volume. These data are available from the base year and forecast year) at the major ports and intermodal 1999 AADT Report for Florida and Truck Weight Study Data terminals by commodity were obtained to locate these facili- for the U.S. These truck counts include all trucks, not just ties in FISHFM as special generators. The locations were ob- freight trucks. The FAF's loaded highway network was used to tained from the 1999 National Transportation Atlas Data- estimate the %age of freight trucks observed in truck counts. bases for the U.S. and Florida, the Strategic Investment Plan to Implement the Intermodal Access Needs of Florida's Sea- ports (Part II, U.S. and Florida seaports), Federal Aviation Model Development Administration Forecasts for the fiscal years 2000-2011, the Software North America Airport Traffic Report, the Port Facilities In- ventory (U.S. and Florida water ports), the U.S. Maritime Ad- FISHFM was designed to run using TRANPLAN software ministration's Office of Intermodal Development, and pub- and FSUTMS scripts. lished reports from port operators. Two FORTRAN programs were written specifically to run FISHFM components. The freight trip generation program, FGEN, generates production and attraction files representing Model Development Data the number of tons of goods generated in each zone by com- The TRANSEARCH commodity flow database as pur- modity group. The mode split program, FMODESP, allocates chased for Florida was chosen to represent the survey of commodities to modes, and converts annual tons of truck existing freight flows. The STCC codes in that database were commodities to daily truck trips. All other components of the used to develop commodity groups for the model, the exist- FISHFM run using the TRANPLAN program within the ing mode shares were chosen, flows were treated as revealed- FSUTMS structure. preference surveys, the total tonnage originating in a zone was chosen to be the production of freight, and the total of Commodity Groups tonnage destined for a zone was chosen to represent the at- traction of freight to that zone. The average trip length be- In FISHFM, commodity groups serve a function similar to tween zones was used for the pattern of trip distribution. that of trip purposes in passenger travel demand models. The shipments within a commodity group have similar character- istics. A total of 14 commodity groups were defined for the Conversion Data FISHFM, as shown in Table 8.36. VALUES PER TON Trip Generation The TRANSEARCH data used for the model is in the STCC code. The dollar value per ton by commodity can be obtained The FISHFM estimates the total freight tonnage by all from the Commodity Flow Survey records for Florida. How- modes--truck, carload rail, intermodal rail, water, and air--

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105 Table 8.36. Commodity groups. Standard Transportation Commodity Code Description Codes 1 Agricultural 1, 7, 8, 9 2 Nonmetallic Minerals 10, 13, 14, 19 3 Coal 11 4 Food 20 5 Nondurable Manufacturing 21, 22, 23, 25, 27 6 Lumber 24 7 Chemicals 28 8 Paper 26 9 Petroleum Products 29 10 Other Durable Manufacturing 30, 31, 33-39 11 Clay/Concrete/Glass 32 12 Waste 40 13 Miscellaneous Freight 41-47, 5020, 5030 14 Warehousing 5010 produced (originating) and attracted (terminating) in Florida. independent variable was guided by the employment by SIC in Production and attraction equations for the 14 commodity the industry associated with the STCC commodity for the pro- groups were based on population and employment relation- duction equations and with the industries determined by an I-O ships that were identified by statistical regressions with the model to be the principal consumers of the commodity for the TRANSEARCH freight database. The trip generation equations attraction equations. Productions and attractions of freight ton- were produced by a linear regression of observed county pro- nage at ports and airports are treated as special generators. duction and attraction tonnage by commodity group as the The trip generation equations were programmed into dependent variable and the employment by industry and/or FGEN for inclusion in the FSUTMS package. population variable for that county as the independent variable, as shown in Tables 8.37 and 8.38. The regression assumed a Trip Distribution zero-intercept (that is, no freight productions or attractions if the independent variable is also zero). A variety of independent FISHFM uses a standard gravity model for the distribution variables were tested to determine the best fit. The choice of of freight tonnage between zones. The average trip lengths for Table 8.37. Trip production equations. Code Name Coefficient Variable (Employment) Commodity Groups 1 Agricultural 45.597 SIC07 2 Nonmetallic Minerals 6,977.771 SUM(SIC10-14) 3 Coal No Production Employment 4 Food 245.464 SIC20 5 Nondurable Manufacturing 90.120 SUM(SIC21,22,23,25,27) 6 Lumber 241.464 SIC24 7 Chemicals 678.583 SIC28 8 Paper 190.814 SIC26 9 Petroleum Products 795.117 SIC29 10 Other Durable Manufacturing 212.202 SUM(SIC30,31,33-39) 11 Clay, Concrete, Glass 1498.501 SIC32 12 Waste 0.500 TOTEMP 13 Miscellaneous Freight 0.599 TOTEMP 14 Warehousing 314.852 SIC50 + SIC51

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106 Table 8.38. Trip attraction equations. Code Name Coefficient Variable Coefficient Variable Commodity Groups 1 Agricultural 23.537 SIC20 2 Nonmetallic Minerals 1461.302 SIC28 3 Coal 178.639 SIC49 4 Food 109.51 SIC51 5 Nondurable Manufacturing 24.698 SIC51 6 Lumber 147.624 SIC25 0.448 Pop 7 Chemicals 83.247 SIC51 8 Paper 23.924 SIC51 9 Petroleum Products 0.228 Pop 10 Other Durable Manufacturing 46.762 SIC 50 11 Clay, Concrete, Glass 2.964 Pop 12 Waste 68.089 SIC33 13 Miscellaneous Freight 2.886 SUM (SIC42,44,45) 14 Warehousing 2.926 Pop each commodity group were calculated from TRANSEARCH. quency distributions also showed the close correspondence That average trip length was used as the coefficient of TRAN- between the estimated and actual tables. The average trip dis- PLAN's gravity model deterrence function. The deterrence tance and deterrence coefficient by commodity group are function calculates friction factors using an exponential decay shown in Table 8.39. function of the impedance variable. Distance in miles was The model trip length frequency distributions of all 14 used to determine the impedance variable that produced the commodity groups are reasonable matches to the observed best fit to the observed trip distributions. A trip length fre- trip length frequencies from the Reebie database. For exam- quency distribution was prepared for both the estimated and ple, Figure 8.18 presents trip length frequency distributions the actual trip tables. For all commodity groups except min- for the food commodity group. erals and coal the R2 was above 0.646. For petroleum and Since the trip distribution used the standard TRANPLAN nondurable manufactured goods the R2 was above 0.95. The gravity model program, no special programs were needed to coincidence ratio of the actual and estimated trip length fre- operate with FSUTMS. Table 8.39. Average trip distance and deterrence coefficient by commodity group. Average Deterrence CG Group Description Distance Coefficient 1 Agricultural 1,260 0.00079 2 Nonmetallic Minerals 332 0.00301 3 Coal 764 0.00131 4 Food 681 0.00147 5 Nondurable Manufacturing 528 0.00189 6 Lumber 606 0.00165 7 Chemicals 790 0.00127 8 Paper 406 0.00246 9 Petroleum Products 768 0.00130 10 Other Durable Manufacturing 712 0.00140 11 Clay/Concrete/Glass 244 0.00410 12 Waste 1,034 0.00097 13 Miscellaneous Freight 748 0.00134 14 Warehousing 250 0.00400

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107 Trips (in Percent) 12 Model Reebie 10 8 6 4 2 0 100 500 900 1,300 1,700 2,100 2,500 2,900 3,300 3,700 300 700 1,100 1,500 1,900 2,300 2,700 3,100 3,500 3,900 Minutes Figure 8.18. Reebie versus model TLF distribution. Mode Split/Daily Truck Conversion The explanatory variables applied in the model were the nat- ural log of travel time multiplied by commodity value per ton FISHFM was developed to estimate annual tons shipped by and travel cost. For the travel time variable, the highway truck, bulk/carload rail, container/intermodal rail, air, and uncongested (free-flow speed) skim file, as created by water. The mode split model is in the form of an incremental TRANPLAN, was used. The highway cost is $0.0575 per mile logit mode choice model. This model pivots from the base traveled. The carload rail cost is $12 + $0.025 per mile. The in- mode shares as identified in the TRANSEARCH database. termodal rail cost is $26 + $0.028 per mile. The highway time is The base water and air mode splits are assumed to remain INT((distance/50 + 8)/18) * 8 + distance/50, which represents unchanged. For all O-D pairs, the mode share for each other travel at 50 mph and an eight-hour rest period after every mode (truck, carload rail, and intermodal rail) for each com- 10 hours of travel, in accordance with the hours of service reg- modity is the base year mode share as adjusted by an incre- ulations. The carload rail time is 60 hours plus distance/20 mph. mental logit model. The coefficients of the utility equation The intermodal rail time is 24 hours + distance/22.75 mph. were calculated using ALOGIT and the TRANSEARCH data The coefficients of the utility equation are given in Table as a revealed-preference survey. 8.40. For commodity groups 2, 3, and 13 (minerals, coal, and The mode split model is an incremental logit model, as waste, respectively) no truck tonnage is given in the base year, shown below. the truck mode split is 0% for all alternatives, and no coeffi- Si Exp ( U i ) cients are given. For commodity groups 12 and 14 (clay/ Si = J concrete and warehousing, respectively), all tonnage is by Exp(U j ) truck in the base year, the truck mode split is 100% for all I =1 alternatives, and no coefficients are given. While the utility where constants for carload rail and intermodal rail differ, the util- Si = New share of mode i; ity coefficients for time and cost are the same for both carload Si = Original share of mode i; and intermodal rail. Ui = Utility of mode i in the choice set J (j = 1,2,3, . . .,J); FISHFM develops daily truck assignments. It is therefore = Modal Constanti + bv (Explanatory Variableiv; and necessary to convert the annual truck table of tonnages to b = Coefficient for Explanatory Variable (e.g., travel time). v daily truck trips. The table of annual shipments of tonnage by

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108 Table 8.40. Mode choice model utility coefficients. Commodity Intermodal Carload Group Value per Ton Constant Constant Time Cost 1 $171.49 -2.05 -0.69 -0.00757 -0.00417 2 $24.33 No Truck 3 $27.01 No Truck 4 $684.14 -1.85 -0.15 -0.00194 -0.00189 5 $7,175.17 2.86 3.92 -0.00069 0.0281 6 $276.15 -0.68 -2.47 -0.00473 -0.00388 7 $865.91 -3.37 -0.96 -0.00092 -0.00861 8 $1,041.00 -0.45 -1.75 -0.00126 -0.00240 9 $175.93 3.00 9.16 0.000217 0.0868 10 $5,143.68 -0.48 1.88 -0.00048 0.0145 11 $103.62 -1.57 1.72 -0.02075 0.0164 12 $4,612.67 All Truck 13 $7,264.31 No Truck 14 $1,618.00 All Truck truck between the origins and destinations is converted into increased as distance increased. The growth function is truck trips using payload factors established from the Florida defined as follows: data in VIUS. These factors are specific to each commodity Payload Factor = exp (bo + (b1 * Distance)) group and vary by the distance traveled between zones. The factors include the percentage of mileage that a truck travels This modification ensured a better fit with observed truck empty, based on VIUS. flows. The calibrated tons per daily truck by commodity During the model validation process, truck conversion group are shown in Table 8.41. factors were modified by smoothing the values. The In order to implement the mode split component and the smoothing method was used to fit values to a growth func- conversion to daily truck trips in FSUTMS, a special program tion as a calibration parameter so that the average truck load known as FMODESP was written in FORTRAN. Table 8.41. Calibrated tons per daily truck by commodity group. Miles Greater Commodity Group Less Than 50 50 to 100 100 to 200 200 to 500 Than 500 Agricultural 13.59 16.04 18.92 22.32 26.34 Nonmetallic Minerals 19.35 20.92 22.63 24.46 26.45 Coal 19.35 20.92 22.63 24.46 26.45 Food 12.19 14.92 18.28 22.38 27.40 Non-durable Manufacturing 3.94 5.79 8.51 12.51 18.38 Lumber 10.80 14.12 18.46 24.14 31.57 Chemicals 10.93 13.29 16.15 19.63 23.87 Paper 15.53 17.99 20.85 24.16 27.99 Petroleum Products 24.58 24.99 25.40 25.82 26.24 Other Durable Manufacturing 6.32 8.92 12.58 17.76 25.07 Clay/Concrete/Glass 19.57 21.29 23.16 25.20 27.41 Waste 12.45 14.99 18.06 21.76 26.21 Miscellaneous Freight 7.79 10.49 14.13 19.02 25.62 Warehousing 8.25 9.93 11.95 14.38 17.30

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109 Table 8.42. Ratio of estimated volume-to-count by facility type. Number of Links with Estimated Volume/ Area Type Facility Type Counts Volume Truck Count Count Ratio 10 10 228 714,290 712,350 1.00 10 20 2 395 410 0.96 10 60 12 24,373 16,662 1.46 Total 242 739,058 729,422 1.01 Assignment The volume-over-count ratios by facility type are pre- sented in Table 8.42. The overall volume-to-count ratio is a The daily truck trip table is assigned to the highway net- perfect match for interstate freeways (FT 10) with a ratio of work, which includes the Florida Intrastate Highway System 1.00. The highest is for toll roads (FT 60), at 1.46. The lowest plus major arterials and collectors and the skeletal network is for other freeway types (FT 20), at 0.96 where the values of developed from the National Highway Planning Network volumes and counts are low. The overall ratio of 1.01 indi- outside Florida. The North American network was connected cates that the model performs extremely well relative to these to the Florida Statewide Model network at nodes shared by performance measures. Table 8.43 shows the volume over external station connectors in the Statewide Model network, count ratios for major interstate freeways (I-75, I-95, and as shown in Figure 8.17. The freight trucks are assigned based I-10) at the Florida state line. Other major statewide screen- on free flow paths and preloaded to the network prior to any line volume-over-count ratios are presented in Table 8.44. assigning of general vehicle trips. The majority of estimates were within 10% of the observed screenline volumes. The RMSE summary is shown in Table Model Validation 8.45. The overall RMSE is well below the maximum desirable percent RMSE established for urban passenger models by Model validation was completed with the same data used FDOT. in developing the models. During the model validation process, the need to calibrate the model was studied and iden- tified for each model step, including trip generation, trip Model Application distribution, mode split/truck conversion, and truck assign- ment. Validation of the assignment of daily freight trucks was The FISHFM is still under development and is being con- compared against observed truck counts. verted to a new statewide model zone structure and network. It is being considered for use in a variety of applications including: Trip Assignment Existing and forecast productions and attractions of an- The truck volumes loaded in the model were validated nual freight tonnage for each TAZ in Florida for 14 specific against the truck counts on major corridors, across the screen commodities; lines and external stations. Estimates such as VMT, vehicle- The existing and forecast O-D table of annual freight ton- hours traveled by truck, and RMSE statistics were reviewed nage moving between TAZs and the external zones cover- and compared with existing statewide freight models and ing North America, for 14 specific commodities; urban freight/truck models. The model was validated on cor- The existing and forecast table of annual freight tonnage by ridors, screen lines, area types and facility types as well. mode and by commodity derived from the total O-D table; Table 8.43. Florida state line volume/count ratio. Interstate Freeway Model Volume Observed Count Volume/Count I-75 10,175 9,600 1.06 I-95 4,125 4,350 0.95 I-10 4,062 4,450 0.91 Total 18,362 18,400 1.00