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54 8.4 Case Study Ohio Interim The model developed in the interim study produces esti- Freight Model mates of freight truck volumes that match the pattern and magnitude of all existing truck volumes in Ohio, but with the Background additional ability to identify the characteristics of those Context freight movements (origin, destination, payload, value, com- modities carried, etc.). The model is easy to maintain and Federal regulations call for specific consideration of freight adapt and uses standard inexpensive commercially available in the development of statewide plans and programs as a con- software. It is compatible with the forecasts of freight move- dition of Federal funding. This requirement obliged the Ohio ments being developed nationally for the Federal Highway Department of Transportation (ODOT) to address freight in Administration. The forecasts of truck traffic developed from its 2002 update of Access Ohio, its statewide transportation an annual survey of shippers produce a broader geographic plan. Although ODOT was in the process of developing a distribution of truck traffic than is produced by a factored comprehensive, statewide, travel demand forecasting model roadside intercept survey. that would include sophisticated freight-planning capabili- ties, an interim study was needed until the new model was fully functional, sometime in 2005. Objective and Purpose of the Model The interim freight study was designed to provide infor- mation and tools to assess freight trends and impacts on The purpose of the Ohio study was to determine how read- Ohio's roadways.18 The data developed was used in four indi- ily available freight databases could be used to: vidual Ohio case studies each addressing a different aspect of freight movement. The model associated with the study is Provide ODOT with a clear picture of existing and future referred to here as the Ohio Interim Model. Figure 8.4 shows freight movements on Ohio's most critical highway existing truck flows on Ohio highways. corridors; Figure 8.4. Ohio highway truck ton flows.

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55 Forecast freight flows and assess the impact that future Flow Units changes in the freight system and freight movement may Existing and future commodity flows were summarized by have on Ohio's roadways; and Make recommendations to meet these demands, while mode share (truck, rail, water, air) and were presented by weight, value, direction (inbound/outbound), origin, and maintaining Ohio's strong economic growth. destination. Additionally conversion factors were applied to convert tonnage by trucks into annual trucks and then to General Approach daily truck trips. Model class The Ohio Interim Freight Model developed facility freight Data flows by directly obtaining and factoring an O-D table of Forecasting Data commodity freight flows, splitting the commodity flow to modes based on existing shares or a market segmentation BASE AND FORECAST YEAR SOCIOECONOMIC DATA diversion method, and assigning the modal O-D tables The Ohio Interim Freight Model used the 1998 to modal transportation networks using fixed paths. The TRANSEARCH database of freight shipments traveling to, research study found that the O-D tonnage information from, or through Ohio. Forecasts of Ohio's economy were could be converted to daily trucks and mapped to Ohio's obtained from the firm of DRI-WEFA and used to estimate roadways. A detailed description of the O-D factoring freight flows for the year 2025. method is provided in Section 6.2. EXTERNAL MARKETS Modes An assessment of intrastate and through freight move- The model was primarily developed to address truck ments is included in the model. The rail network included the movements on major highways, but includes water, air, and entire country and the highway network included only Ohio two rail submodes (carload rail and intermodal containers). highways, with external stations at the state boundaries. Existing and future commodity flows were summarized by mode share (truck, rail, water, air) and were presented by weight, value, direction (inbound/outbound), origin, and Modal Networks destination. FREIGHT MODAL NETWORKS Shapefiles provided with the TRANSEARCH network were Markets used to assign freight flows. The model was developed to address freight issues throughout the state of Ohio and included information on INTERMODAL TERMINAL DATA the top 13 truck commodities. Key trading partners (states The information in the commodity flow database was at and regions) were identified. The four Ohio case studies the county level. No information was available for zones rep- addressed different markets. resenting intermodal terminals. The methods do not produce estimates of the shipments of nonmanufactured goods, local delivery trucks, construction trucks, service trucks, and other heavy vehicles not involved Model Development Data in the shipment of freight. The forecasts of these localized No model coefficients or parameters were necessary in the truck volumes must be obtained elsewhere. Ohio Interim Freight Model. Framework Conversion Data The purpose of the model and the study was to address CONVERSION OF TONNAGE INTO VALUE Ohio's needs for interim freight information and tools to assess freight trends and impacts on Ohio's roadways while Factors to convert annual tonnage into annual value were ODOT updates its statewide travel demand model. When developed from the CFS conducted by the U.S. Bureau of the complete, the updated statewide model will include more Census and the U.S. Department of Transportation. The 1993 sophisticated freight planning capabilities. Commodity Flow Survey, which reports commodities by

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56 STCC, was chosen in order to be consistent with in a commodity. (For the Ohio Interim Freight Model, the TRANSEARCH. The 1997 CFS reports commodities by the Ohio data contained 40 separate classifications by STCC newer SCTG codes that are not directly transferable to STCC codes, but the separate codes were aggregated into the top at a two-digit level. The values per ton were converted to 1998 13 commodity codes.) dollars using the consumer price index. 4. Determine the percent increase in each commodity's ori- gins and destinations by applying growth factors obtained in Steps 2 and 3. CONVERSION OF TRUCK TONNAGE INTO DAILY TRUCK TRIPS 5. Apply Fratar factoring to each O-D table to achieve the Factors to convert annual tonnage into annual trucks trips percent increases determined in Step 4. were developed from the VIUS conducted by the U.S. Bureau 6. Determine the number of vehicles necessary to carry each of the Census. The VIUS national microdata database con- O-D flow for one equivalent weekday. sists of 105,545 records, with 1,974 records of the trucks based 7. Assign each factored vehicle trip table to its respective in Ohio. Of these, 1,399 included loaded weight information modal network. that make it possible to develop average payloads for the two-digit STCC codes included in the Ohio TRANSEARCH This method assumes that the mode split for any given database. The sample includes expansion factors that equate commodity and for any given O-D pair is a constant. Any to over 82 billion ton-miles of shipments. modal shifts that occur in this method are due to economic growth (or decline) or spatial shifts in economic activity and the resulting effects on commodity production and con- Validation Data sumption patterns. Shifts due to changes in costs, supply The estimates of daily freight trucks produced by chain practices, shipping and transfer times or vehicle tech- TRANSEARCH were qualitatively compared with ODOT's nology are not included. volume counts for all trucks. The method further assumes that the production, con- sumption, and shipping characteristics of commodities remain unchanged. Such assumptions can be eliminated by Model Development careful consideration of changes in a) shipping density of Methods were developed to assign the flow of freight ship- commodities, particularly due to packaging materials; b) ments to Ohio's major roadway using database queries within worker productivity when economic activity forecasts are TRANSEARCH. The resulting network flows were then given in number of workers in an industry; c) value per ton mapped as a roadway network using the ArcView GIS software. when economic activity forecasts are given in monetary units; The Ohio Interim Freight Model developed facility freight d) the routing patterns of the supply chain; and e) competi- flows by directly using a method of freight forecasting tiveness of modes or intermodal combinations to carry described as O-D table factoring and assignment. This specific commodities. method (with some variation) has been used by many states. The most prevalent application of this method follows these Software general steps: The Ohio Interim Freight Model was developed using sev- 1. Obtain base-year O-D tables (in tons per year) by com- eral software packages readily available and familiar to trans- modity and by mode that matches the desired traffic zone portation professionals. These included Microsoft Access, system. Typically, flows between external zones that do Microsoft Excel, ArcView GIS, and the Highway Economic not pass though the internal portions of the network are Requirements System (HERS). While Access and Excel are excluded. (For the Ohio Interim Freight model 1998 common software packages, ArcView GIS and HERS typi- TRANSEARCH databases were used.) cally require specialized knowledge. By using Access queries, 2. Obtain base-year and future-year levels of economic truck flows by highway segment can be exported in DBF activity (by industrial sector) for all zones. (For the Ohio format for use in other programs. Interim Freight Model, forecasts of Ohio's economy were Maps of truck flows can be prepared from the DBF file of obtained from DRI-WEFA and used to estimate freight flows by highway segment ID using ArcView. TRANSEARCH flows for the year 2025.) contains a shapefile containing all the information in the 3. Establish a mapping between industrial sectors and com- highway network. By joining the highway segment field in the modity categories, such that a percent increase in an DBF file with the same field in the network shape file, maps industrial sector can be associated with a percent increase of the flows can be produced.

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57 Commodity Groups/Truck Types ized in the order of the numeric STCC code that they represent, not by the amount of tonnage represented by that group. These Commodity groups serve a function similar to trip pur- commodity groups serve as the basis for the report and poses in the passenger travel demand models. The accompanying tables. Tables 8.9 and 8.10 do not include non- TRANSEARCH commodity database purchased for Ohio manufactured goods. For example, agricultural products includes 40 separate classifications of commodities by STCC transported to a food processing plant are included, but agri- code at the two-digit level and 440 separate classifications at cultural products transported to a supermarket are not. the four-digit level. While this level of detail is useful for iden- tifying specific commodity movements, such a large number of commodity classifications makes reporting and analysis Trip Generation difficult. In order to reduce the commodity groups to a more manageable level, the top 13 commodities at the two-digit Not applicable. The Ohio Interim Freight model used STCC level by tonnage were identified. These 13 represent TRANSEARCH origin/destination data as purchased for this over 93% of the truck tonnages by truck as well as 86% of the particular study. total tonnage originating in Ohio. These STCC codes were each assigned as a single commodity group for analysis and Trip Distribution reporting purposes. The remaining commodities were as- signed to groups in the following categories: agricultural Not applicable. The Ohio Interim Freight Model used products, other nondurable manufactured products, other TRANSEARCH origin/destination data as purchased for this durable manufactured products, minerals, and miscellaneous particular study. freight. The commodity groups for the model are shown in Table Commodity Trip Table 8.9. Shown in each row are the STCC commodities assigned to each group and the total tonnage reported as traveling in Ohio The Ohio Interim Freight Model used TRANSEARCH O-D by all modes and by truck. The commodity groups are organ- data as purchased for this particular study. Table 8.9. Commodity groups used in the Ohio Interim Freight Model. STCC Codes in 1998 Annual 1998 Annual Commodity Group Commodity Tonnage by All Tonnage by Code Name Group Modes Truck 1 Agriculture 1, 7, 8, 9 28,898,426 6,679,545 2 Metallic Ores 10 43,887,516 3 Coal 11 132,797,767 11,135,211 4 Other Minerals 13, 14, 19 26,096,634 5 Food 20 96,036,220 76,781,243 6 Nondurable Manufacturing 21, 22, 23, 25, 27 13,311,467 12,646,266 7 Lumber 24 27,041,926 22,128,079 8 Paper 26 31,175,374 24,416,542 9 Chemicals 28 94,527,499 66,666,943 10 Petroleum 29 46,791,003 29,842,434 11 Rubber/Plastics 30 18,797,786 18,442,466 12 Durable Manufacturing 31, 36, 38, 39 23,187,380 22,128,609 13 Clay, Concrete, Glass 32 70,984,985 64,114,794 14 Primary Metals 33 87,342,217 62,115,438 15 Fabricated Metal Products 34 27,871,702 27,107,319 16 Transportation Equipment 37 47,048,025 31,064,887 17 Miscellaneous Freight 40-48, 5020, 5030 43,143,468 18 Warehousing 5010 82,420,938 82,420,938

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58 Table 8.10. Ohio tonnage to truck conversion factors (tons per truck). Distance Class Short- Long - Local Short Medium Medium Long Two-Digit STCC (500 Codes Commodity Name Miles) Miles) Miles) Miles) Miles) 1 Farm Products 12.04 18.37 19.10 18.71 17.67 8 Forest Products 13.36 11.64 13.27 13.27 13.27 9 Fresh Fish or Marine Products 8.20 8.13 14.42 15.89 16.11 10 Metallic Ores 16.98 18.81 25.77 25.77 25.77 11 Coal 16.98 18.81 25.77 25.77 25.77 13 Crude Petroleum or Natural Gas 14.43 19.58 17.84 17.84 17.84 14 Nonmetallic Minerals 16.98 18.81 25.77 25.77 25.77 19 Ordnance or Accessories 7.05 4.42 11.47 9.84 11.30 20 Food or Kindred Products 8.20 8.13 14.42 15.89 16.11 21 Tobacco Products 11.50 16.25 16.03 11.47 15.96 22 Textile Mill Products 1.34 3.57 18.18 18.16 17.48 23 Apparel or Related Products 1.34 3.57 18.18 18.16 17.48 24 Lumber or Wood Products 10.33 12.35 17.50 17.61 17.83 25 Furniture or Fixtures 2.92 3.25 11.02 11.26 11.38 26 Pulp, Paper, or Allied Products 4.07 7.67 15.66 15.17 14.59 27 Printed Matter 4.07 7.67 15.66 15.17 14.59 28 Chemicals or Allied Products 5.18 15.39 19.55 19.25 19.25 29 Petroleum or Coal Products 14.43 19.58 17.84 17.84 17.84 30 Rubber or Miscellaneous Plastics 7.05 4.42 11.47 9.84 11.30 31 Leather or Leather Products 1.34 3.57 18.18 18.16 17.48 32 Clay, Concrete, Glass, or Stone 10.69 14.47 18.53 18.63 18.81 33 Primary Metal Products 11.82 14.73 19.96 20.14 20.13 34 Fabricated Metal Products 4.00 11.33 14.49 14.49 14.49 35 Machinery 6.97 12.55 17.42 17.21 17.21 36 Electrical Equipment 4.05 7.42 14.81 14.62 14.62 37 Transportation Equipment 2.48 14.12 17.21 16.92 14.18 38 Instruments, Photo Equipment, 6.97 12.55 17.42 17.21 17.21 Optical Equipment 39 Miscellaneous Manufacturing 5.48 5.40 11.63 13.04 14.23 Products 50 Drayage, Warehousing, 7.05 9.67 14.85 14.98 14.93 Distribution Source: Derived from Vehicle Inventory and Usage Survey records for Ohio. The annual tonnage data from TRANSEARCH were con- ual highway segments, including origin, destination, and verted into annual values based on factors from the CFS. The commodity type, can be exported into a DBF file and then annual tonnage data from TRANSEARCH also were con- mapped to Ohio's roadways. verted into number of annual trucks based on VIUS. The The economic model used in this study consisted of a set number of annual trucks was disaggregated into the number of unique commodity flow models that specify the likely pat- of daily truck trips. To simplify summary analysis and report- tern of goods movement by commodity and by transport ing, the 40 separate classification categories of two-digit mode. The forecasts are based on economic factors that affect STCC codes were grouped into the top 13 commodities. changes in demand. The projections are based on regional, Future freight flows for each commodity group were deter- industry, and commodity models and have been developed to mined based on economic model forecasts for 2010 and 2020. support a variety of public agencies and private firms study- Using the TRANSEARCH database, truck flows on individ- ing freight transportation.

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59 Mode Split Ohio VIUS database. The mileage was multiplied by the average payload for that record to obtain weighted annual The base year split among highway, rail, air, and water ton-miles by product class and by distance-class for each modes from TRANSEARCH was assumed too constant into the record. The weighted annual ton-miles, and the weighted future for the Ohio Interim Freight Model. However, a mode annual miles were summed over all records. The average pay- split by market segmentation used to assess potential freight load for each commodity by distance-class was obtained by diversion between highway and rail was developed for the dividing average annual ton-miles by average annual miles. Northern Ohio Rail Highway Corridor case study. This case Calculating payloads by two-digit STCC code is the first study addressed an important issue in Ohio's state planning by step in developing factors to convert tonnage to trucks. This assessing the potential to reduce the number of trucks travel- payload does not include the percentage of miles that a truck ing on the turnpike and the parallel alternate highway routes. travels empty. This percentage of empty miles by commodity It was assumed that only trips longer than a certain length, car- group can also be calculated from the VIUS "No Load" prod- rying only particular commodities, and larger than a certain uct class. The factor to be used to covert from annual tonnage size (weight) would be suitable for diversion to rail. Specifi- to annual trucks must account for the average payload, cally, three major characteristics that influence the diversion including percentage of empty trucks, in each STCC com- potential were analyzed: 1) the origin and destination of the modity class. The values by STCC code and distance-class are traffic; 2) the commodity mix of traffic between these origins given in Table 8.10. and destinations; and 3) the total distance between them. After converting annual tons to annual trucks, the result- ing annual truck trip table is converted into a daily truck trip table. The Highway Capacity Manual (HCM) suggests that an Flow Unit and Time Period Conversion average truck working week consists of five weekdays at full The VIUS microdata includes the empty weight of the capacity and two weekend days at 44% capacity.19 This vehicle; the average loaded weight of the vehicle; expansion equates to 306 truck working days per year. In addition, six factors based on the miles traveled; the percentage of the Federal holidays are excluded from working calculations. It is miles that the vehicle's trip falls in one of five different dis- recommended that the annual truck trips should be divided tance-classes; the percentage of the miles that the vehicle is by 300 average weighted truck working days to calculate daily empty; and, when full, the percentage of the miles that the truck trips. vehicle is used to carry 31 distinct product classes. The 1993 CFS values were used to develop value per ton by Average payloads were calculated by the five distance- STCC code. The values per ton are reported in Table 8.11. classes established in VIUS: 1) local (less than 50-mile trips); The Ohio model converted the observed tonnages to val- 2) short (50- to 100-mile trips); 3) medium-short (100- to ues and annual trucks. The Ohio model used 306 working 200-mile trips); 4) medium-long (200- to 500-mile trips), and days per year to convert from annual to daily trucks. 5) long (over 500-mile trips). The payloads were calculated by distance-class because the average payload and truck size var- Assignment ied by distance-class. Shorter-distance trips tend to be domi- nated by single unit trucks, which carry smaller average The assignment process used a predetermined, fixed path payloads. Longer-distance trips are dominated by combina- routing method based on the National Highway Network tion tractor-trailer trucks, which carry larger average payloads. (NHN) as developed by the Oak Ridge National Laboratory. The product classes used by the VIUS are similar to the two- Reebie Associates has used routing information in the NHN digit STCC codes established for TRANSEARCH. The VIUS to develop a database of highway segments that form the survey records the percentage of the mileage that a truck is car- paths between the geographic centers of each county in the rying certain products, equipment, materials, etc. "No Load" United States. Ohio's purchase of TRANSEARCH includes is treated by VIUS as a separate product category. VIUS also routing information showing all highway paths used within a includes buses and service trucks in the survey. Thus, certain state. VIUS product categories do not correspond to STCC com- The national O-D table of commodity flows between modity classes. A correspondence between the VIUS product counties in the United States is aggregated into the specific classes and the Ohio Model commodity groups was devel- regions developed as part of Ohio's TRANSEARCH database. oped. Passenger and service truck product classes not included While the tonnage flow information is aggregated to these in the commodity data (for example, Craftsmen's Tools or regions, groupings also are maintained by highway path, with Household Possessions) were excluded. origin, destination, and commodity information attached. The weighted annual mileage for each VIUS product car- Total truck flows on individual highway segments can be ried by distance-class was calculated for each record in the identified and selectively chosen to show origin, destination,

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60 Table 8.11. Shipment values per ton by STCC commodity. STCC Code Description Value Per Ton (1998$) 1 Farm Products $1,147 8 Forest Products $40 9 Fresh Fish or Other Marine Products $5,493 10 Metallic Ores $50 11 Coal $24 13 Crude Petroleum, Natural Gas, or Gasoline $31 14 Nonmetallic Minerals $19 19 Ordnance or Accessories $11,590 20 Food or Kindred Products $1,408 21 Tobacco Products, Excluding Insecticides $32,610 22 Textile Mill Products $6,735 23 Apparel or Other Finished Textile Products $25,732 24 Lumber or Wood Products, Excluding Furniture $2,363 25 Furniture or Fixtures $5,465 26 Pulp, Paper, or Allied Products $1,333 27 Printed Matter $3,054 28 Chemicals or Allied Products $2,064 29 Petroleum or Coal Products $239 30 Rubber or Miscellaneous Plastics Products $7,290 31 Leather or Leather Products $29,268 32 Clay, Concrete, Glass, or Stone Products $205 33 Primary Metal Products $1,273 34 Fabricated Metal Products $3,544 35 Machinery, Excluding Electrical $21,980 36 Electrical Machinery, Equipment, or Supplies $28,724 37 Transportation Equipment $13,904 38 Instruments, etc. $39,343 39 Miscellaneous Products or Manufacturing $11,270 40 Waste or Scrap Materials $26 41 Miscellaneous Freight Shipment $4,763 42 Containers Returned Empty $1,120 43 Mail and Contract Traffic $1,333 45 Freight Forwarder Traffic $1,606 46 Mixed Commodity Shipments $1,606 47 Small Packages $1,606 48 Waste Hazardous Materials $291 49 Hazardous and Corrosive Materials $2,064 50 Secondary Cargos and Drayage $1,606 99 Commodity Unknown $8,917 Source: Derived from the Commodity Flows Survey records from Ohio. or commodity. By using the query capabilities of Microsoft Trip Distribution Access, these flow records by highway segment can be No trip distribution validation was conducted. exported as a DBF file for use in other programs. Maps of truck freight flows can be prepared from the DBF file of flows by highway segment ID using ArcView. Mode Choice TRANSEARCH contains a shapefile containing all of the No mode choice validation was conducted. information in the highway network. By joining the highway segment field in the DBF file with the same field in the net- work shapefile, maps of the flows can be produced. Modal Assignment The modal assignment was validated by comparing the Model Validation estimates of daily freight trucks produced by TRANSEARCH with the Ohio DOT's truck volumes. Comparisons were Trip Generation made between the pattern of the modeled freight truck vol- No trip generation validation was conducted. umes and the observed truck volumes crossing screenlines.

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61 The pattern of truck volumes estimated by TRANSEARCH annual rate of 2.3%, faster than the 2.0% annual growth rate was mapped using ArcView and was overlaid on the map of of general traffic on these same corridors. This caused ODOT ODOT truck volumes. The TRANSEARCH freight truck to express concern about performance and funding. flows include only a subset of all heavy trucks counted by The macro-corridors in Ohio were evaluated using the ODOT. They include only trucks involved in the private or Highway Economic Requirements System model and the for-hire transport of freight, not service trucks, construction PONTIS bridge management model. These models represent trucks, local delivery trucks, etc. On rural interstate facilities, the state-of-the-practice in evaluating highway and bridge where freight trucks predominate, the difference between systems and rely on databases that are prepared by the states. observed truck volumes and TRANSEARCH freight trucks is For HERS, the Highway Performance Monitoring System minimal. On urban highways, where urban activity generates data prepared annually by ODOT and submitted to the U.S. significant additional trucking activity, the differences are DOT was used. (No analysis was undertaken using PONTIS. greater. Generally, freight traffic at the statewide level repre- Previous analysis with PONTIS in other states has indicated sents 60% or more of all truck vehicle-miles of travel. that bridge costs and conditions vary little with changes in The selected Ohio screenline locations generally show a re- demand and are instead a function of environmental and lationship between the total observed and the total estimated maintenance factors. For that reason, the case study was truck volumes within the expected levels. The variation exists performed using only HERS.) HERS analysis provided data because the truck observations include all types of trucks on congestion, speeds, pavement conditions, safety, air pol- while the estimate is of one type of truck: trucks carrying lution, and program expenditures. freight. In rural areas, freight trucks will constitute almost 100% of all trucks. In urban areas, the percentage is much CONCLUSIONS lower. The estimated truck volumes were derived by assigning The impacts caused by the growth in truck traffic, which is truck flows to the single shortest highway path between greater than the comparable increase in general traffic, are county centers. TRANSEARCH does not take into account minimal and should be manageable. diversion of traffic among several available routes, nor can it According to HERS, the costs to maintain the existing sys- distinguish shortest paths from points not at the county cen- tem are considerable, but not appreciably greater, when ters. As such, the TRANSEARCH flows are best considered adjusted for growth, than Ohio's current expenditures. general flows along a corridor rather than actual facility flows. Trucks are responsible for a large share of those costs -- approximately 30% according to relationships in the High- Model Application way Cost Allocation Study. HERS produced reasonable and useful results for this The Ohio Interim Freight Model freight data was used in study. ODOT is currently testing HERS/ST, a specially four case studies to address various freight operations and tailored version for state DOTs, and should be encouraged policy issues. Each of these case studies is described below. to implement the software. HERS considers only the direct benefits to users of the highway system. It does not consider the economic devel- Macro-Corridor Case Study opment impacts of changes in transportation costs. The OVERVIEW changes in these costs are available from HERS and consid- eration should be given to applying economic models to This case study examined Ohio's macro-corridors and the identify the larger impacts on Ohio's economy. impact of an increase in truck traffic that is greater than the expected increase in traffic. The 1995 Ohio State Transportation Plan Access Ohio, I-75 Corridor Case Study identified "Transportation Efficiency and Economic Ad- OVERVIEW vancement Corridors," also known as macro-corridors, throughout the state. Macro-corridors form a network of This case study examined how improved truck forecasts approximately 2,300 miles of roads determined to be the might be utilized in a corridor planning study. The freight- most critical. One of the factors used in the designation of a truck forecasts provide detailed information about the macro-corridor is high truck volumes. Based on the analysis industries served and commodities carried now and in the of the Ohio model outputs, those macro-corridors were future on Interstate 75 in Ohio. found to carry over 96% of the freight-truck volumes. Truck I-75 is one of the major trucking corridors in the United traffic on these corridors was found to be growing at an States, running from Miami to Detroit and continuing as

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62 Highway 401 to Toronto. I-75 has been the subject of the port the detailed location of rest areas, since that determi- multi-state I-75 Advantage Program to reduce congestion, nation requires knowledge of the national origin and des- increase efficiency, and enhance the safety of motorists and tination of trucks, their temporal movement over the other users through the application of Intelligent Transporta- national network, and the hours-of-service rules. These tion System (ITS) technologies. ODOT was a major partner issues are beyond the scope of this study. in the I-75 Advantage Program. The truck forecast supports the identification of specific During the preparation of the I-75 study, ODOT became industries and geographic areas served by corridors (such concerned about the accuracy of the truck information on as I-75) that can assist in public outreach and economic I-75. The data, forecasts, and methods developed as part of development efforts. this freight study were examined to determine how they could be used in the I-75 study. The truck data and forecasts can provide detailed information about the industries Northern Ohio Corridor Case Study served and commodities carried on I-75, both now and in OVERVIEW the future. This case study in northern Ohio examined the relative share of 1998 traffic in the Northern Ohio Corridor among CONCLUSIONS truck traffic on the Ohio turnpike, truck traffic on Ohio arte- rial highways, and rail traffic and the factors that might influ- Analysis indicates that of the top five commodities carried ence diversion among these modes. ranked by value, four are industrial commodities (trans- The case study attempted to answer several important portation equipment, general machinery, electrical questions related to Ohio's state planning: Would it be pos- machinery, and fabricated metal), which account for 28% sible, and feasible, to lessen the number of trucks traveling on of the value of freight carried on I-75. This information can the turnpike and the parallel alternate highway routes? Could help identify those industries and firms that will benefit enough traffic be diverted to rail to warrant a public invest- from I-75 improvements. ment in rail infrastructure and operations, or to offer other The truck forecasts for I-75 are specific to the economic incentives to shippers or rail carriers? Is diversion even an forecasts, not historic trends, and are available for individ- issue that can be controlled and managed within the geo- ual sections. In general, I-75 truck volumes are expected to graphic scope of the state's borders? increase by 1.8% per year. This growth is below the aver- Although there were no simple answers, there were ways to age growth of 2.3% per year forecast for all roads in Ohio. analyze freight flow data to intelligently explore the issues These forecasts can support specific truck-related design surrounding the Northern Corridor, and methodologies considerations. were in place to help determine how many trucks might be By providing O-D information for trucks using I-75, the diverted in this corridor. Given the nature of the corridor, it demand for interchanges in specific counties can be iden- could also be assumed that no diversion would take place to tified. The analysis indicated that the major interchanges water or air freight. of I-75, from north to south, include I-280, I-475, U.S. 68, The current profile of traffic in the corridor became the U.S. 36, I-70, SR 43, Ronald Reagan Highway in Hamilton basis for traffic diversion estimates. The current mix of traffic County, and I-71. The interchanges refer generally to the on the Ohio Turnpike and the alternative east-west corridors urban principal arterials and are consistent with the corri- was analyzed in an effort to determine if the traffic exhibits dor level of the TRANSEARCH assignment procedures. characteristics favorable for diversion to rail. Specifically, three The relative growth in truck percentages can support major factors that influence diversion were analyzed. These truck-specific design considerations at interchanges. were: 1) the origin and destination of the traffic; 2) the com- Key features on major interstate highways are weigh sta- modity mix of traffic between these O-D points; and 3) the tions and rest areas. These facilities are particularly impor- total distance between these points. tant for trucks traveling on I-75 without an Ohio origin or destination and for trucks traveling over 500 miles. The truck forecasts for 2020 indicate that 28% of the trucks on CONCLUSIONS I-75 are passing though. The truck forecasts for 2020 fur- ther indicate that 30% of the freight trucks are traveling There are an estimated 13.6 million annual truckloads more than 500 miles and may require a driver rest stop. traveling in the Northern Ohio Corridor. This information can support the sizing of weigh stations The current intermodal rail market carries 7.3% of all loads and indicates the relative need for rest areas. It cannot sup- in the corridor.