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Performance Measures for Freight Transportation (2011)

Chapter: Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries

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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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Suggested Citation:"Appendix A - Summaries of Freight Performance Information for National Report Card Performance Summaries." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
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65 a p p e n D i X a summaries of Freight Performance information for national Report Card Performance summaries

66 CONTENTS 67 Introduction 67 Freight Demand Measures 67 Freight Volumes, All Modes 68 Truck Freight Volumes 68 Rail Freight Volumes 69 Inland Water Freight 70 Containerized Imports/Exports 72 Freight Efficiency Measures 72 Interstate Highway Speeds 73 Interstate Highway Reliability Measure 75 Trend Line of Top Interstate Bottlenecks 77 Composite Class I RR Operating Speed 78 Rail Freight Market Share of Ton Miles 79 Logistics as a Percentage of GDP 81 Freight System Condition Indicators 81 NHS Bridge Structural Deficiencies 81 NHS Pavement Conditions 83 Freight Environmental Measures 83 Truck Emissions 84 Particulates 85 Truck NO x Emissions 85 VOCs 85 Greenhouse Emissions 87 Rail-Produced Greenhouse Gas Emission 88 Water-Produced Greenhouse Gas Emissions 88 Rail VOCs and NO x 89 Ship NO x 90 Freight Safety Measures 90 Truck Injury and Fatal Crash Rates 91 Highway–Rail At-Grade Incidents 92 Freight Investment Measures 92 Investment to Sustain NHS 92 Rail Industry Cost of Capital 94 Estimated Capital to Sustain Rail Market Share 94 Investment to Sustain Inland Waterway System 94 Endnotes

67 Introduction The following section presents summaries of freight per- formance information that would support each individual performance measure from the Freight System Report Card at the national level. The framework is proposed to serve as a Web-based tool. Each line of the report card would link to the summary information that is presented on the following pages. In addition, more extensive source documents would be linked from the summary report to provide the reader with additional detail and analysis. Reports such as the Council of Supply Chain Management Professionals (CSCMP) report, the FHWA Condition and Performance report, or EPA air- quality analyses would be the types of supporting documen- tation provided as supplemental links. The intent of the for- mat is to provide summary, high-level information with the ability for the user to drill down into more detailed analysis if it is desired. In some cases, one succinct document provides the needed context. In other cases, a variety of links may be needed to provide the reader with sufficient summary infor- mation. Although the reliance on supplemental reports does not provide uniformity to the reader, the reliance is unavoid- able at this stage of national freight performance measure- ment. Consistently produced detailed analysis for each per- formance trend does not exist; therefore, the initial proposed framework opportunistically uses what sources are available. The summaries on the following pages are for national measures. Appendix B provides summaries for the regional case studies, which are of Washington State and the Seattle metropolitan area. The two sets of summaries illustrate how the national report card could be replicated at state and metro politan levels. Freight Demand Measures Following are the measures for the category of Freight Demand. Freight Volumes, All Modes Freight performance Trend: increasing volumes Influencing all other freight performance trends has been and likely will continue to be the steady growth in overall freight volumes over the long term. The slight decline in actual volumes in the past 18 months is in sharp contrast to a steady, continuous increase in freight volumes overall since at least the 1960s. Between 1984 and 2004, ton-miles for both trucks and rail rose approximately 85 percent in the United States. The Freight Analysis Framework (FAF) forecast depicted in Figure A.1 is based on composite forecasts that are updated comprehensively every five years and updated provisionally annually. The FAF forecast pre- dicts a steady 2.03 percent rate of growth in freight volumes overall through 2035. Being a long-term esti- mate, the actual rate of growth will vary year to year. The long-term forecast is based on best available esti- mates, which account for the rate of economic growth, changes in sectors of the economy, and the influence of imports and exports. The relative mode splits remain relatively similar through 2035 according to the FAF forecasts, with truck and rail volumes both growing at approximately 2 percent annually, with water at 1.5 percent with one major exception. Intermodal move- ments of imports grow at a significantly faster rate than other types of movements. This FAF table (Table A.1) estimates freight volumes by dollar value. Intermodal movements of imports rise from $716 billion in 2002 to $3,708 billion by 2035, a more than five-fold increase. This reflects U.S. export imbalances and increased glo- balization of the economy. This import growth will affect most significantly the major container ports, rail movements, and truck/rail movements. 2 Freight Volumes, All Modes Figure A.1. Freight volumes, all modes. Freight Performance Trend: Increasing Volumes Influencing all other freight performance trends has been and likely will continue to be the steady growth in overall freight volumes over the long term. The slight decline in actual volumes in the past 18 months is in sharp contrast to a steady, continuous increase in freight volumes overall since at least the 1960s. Between 1984 and 2004, ton miles for both trucks and rail rose approximately 85 percent in the United States. The Freight Analysis Framework (FAF) forecast depicted above is based on composite forecasts that are updated comprehensively every five years and updated provisionally annually. The FAF forecast predicts a steady 2.03 percent rate of growth in freight volumes overall through 2035. Being a long-term estimate, the actual rate of growth will vary year to year. The long-term forecast is based on best available estimates, which account for the rate of economic growth, changes in sectors of the economy, and the influence of imports and exports. The relative mode splits remain relatively similar through 2035 according to the FAF forecasts, with truck and rail volumes both growing at approximately 2 percent annually, with water at 1.5 percent with one major exception. Intermodal movements of imports grow at a significantly faster rate than other types of movements. This FAF table (Table A.1) estimates freight volumes by dollar value. Intermodal movements of imports rise from $716 billion in 2002 to $3,708 billion by 2035, a more than five-fold Freight Volumes by Value (Billions of Dollars) 2002 2035 Total Domesti c Export s Import s Total Domesti c Export s Import s Total 13,22 8 11,083 778 1,367 41,86 9 29,592 3,392 8,884 Truck 8,856 8,447 201 208 23,76 7 21,655 806 1,306 Rail 382 288 26 68 702 483 63 156 Water 103 76 13 13 151 103 31 18 Air, air & truck 771 162 269 340 5,925 721 1,548 3,655 Intermodal 1,967 983 268 716 8,966 4,315 943 3,708 Pipeline 1,149 1,127 1 22 2,357 2,315 1 41 Figure A.1. Freight volumes, all modes.

68 Truck Freight Volumes Freight performance Trend: increasing Truck volumes As illustrated in Figure A.2, truck volumes are predicted to sustain steady growth on the national level.1 The growth is posi- tive as an indicator of long-term economic health but creates additional pressures on the highway network. Though the cur- rent economic environment has reduced truck freight volumes in 2009, long-term growth for the Truckload (TL) and Less-Than- Truckload (LTL) sectors are expected. In general, LTL annual growth rates are forecast to remain higher than TL growth rates. Between 2009 and 2014, the annual rate of growth for the LTL sector is slightly above 2.5 percent per year, and beginning in 2015, the annual rate of growth is forecast to increase to over 3.5 percent. The TL sector, the predominant industry sector, is expected to increase at a slightly slower pace. Tonnage hauled by this sector is forecast to increase nearly 2.5 percent per year until 2014, then experience a higher annual growth rate between 2015 and 2020. The Pacific region (which includes Alaska, California, Hawaii, Oregon, and Washington) experienced an increase in the per- centage of total U.S. tonnage of primary shipments originating in this region from 13.6 percent in 2002 to 14.6 percent in 2007. It should be noted that the economic conditions and tonnage of shipments hauled by trucks originating in California signifi- cantly impacts these regional metrics. The ATA forecast estimates that trucks will haul 13.3 billion tons in 2020. FHWA’s FAF forecasts that by 2035 trucks will haul 22.8 billion tons of freight.2 The severity of the recent challenging economic environment and rapid decline in freight volumes for all modes was largely unanticipated by most industry experts. Though most sectors of the trucking industry have experienced dramatic declines in freight volumes, tonnage hauled by trucks is expected to grow in the long term, driven by population growth and increased economic activity. Rail Freight Volumes Freight performance Trend: increasing Rail freight volumes are expected to increase overall through 2020, putting increasing pressure on an already con- gested national rail network (see Figure A.3). The source of the rail forecast is the next generation Freight Analysis Frame- work FAF2 database. FAF2 freight flow origin and destination (O-D) coverage spans 131 freight analysis zones that include 114 freight O-D zones and 17 major ports, border cross- ings, and freight ports. The FAF2 commodity flow data are benchmarked to 2002 and are forecasted to 2035. This analy- sis of the rail forecast utilizes the 2008 values and the 2035 estimates. The rail information is available for all transport and then divided into three potential submarkets: domestic, border crossings, and sea movements. Table A.2 presents the 2008 and 2035 values for the rail mode.3 The forecast esti- mates that total rail traffic will increase by just under 2 per- cent annually. This increase is present even with an estimated decrease in rail traffic for origin–destination pairs involving sea traffic, with such traffic estimated to decrease by 1.4 per- cent. Domestic rail movements represent the highest growth, at an estimate of 2.1 percent. The forecast rate of freight growth by mode may be defined as the estimated percentage increase in tonnage hauled in future years for the major modes of freight transportation. Baseline figures and forecast tonnage figures are limited to primary shipments (primary shipments are defined as those handled the first time). This measure estimates the rate of freight growth involving rail transport. table A.1. Freight volumes by value (billions of dollars).

69 table A.1. Freight volumes by value (billions of dollars). 4 Truck Freight Volumes Freight Performance Trend: Increasing Truck Volumes As illustrated above, truck volumes are predicted to sustain steady growth on the national level.1 The growth is positive as an indicator of long-term economic health but creates additional pressures on the highway network. Though the current economic environment has reduced truck freight volumes in 2009, long-term growth for the Truckload (TL) and Less-Than-Truckload (LTL) sectors are expected. In general, LTL annual growth rates are forecast to remain higher than TL growth rates. Between 2009 and 2014, the annual rate of growth for the LTL sector is slightly above 2.5 percent per year, and beginning in 2015, the annual rate of growth is forecast to increase to over 3.5 percent. The TL sector, the predominant industry sector, is expected to increase at a slightly slower pace. Tonnage hauled by this sector is forecast to increase nearly 2.5 percent per year until 2014, then experience a higher annual growth rate between 2015 and 2020. The Pacific region (which includes Alaska, California, Hawaii, Oregon, and Washington) experienced an increase in the percentage of total U.S. tonnage of primary shipments originating in this region from 13.6 percent in 2002 to 14.6 percent in 2007. It should be noted that the economic conditions and tonnage of shipments hauled by trucks originating in California significantly impacts these regional metrics. The ATA forecast estimates that trucks will haul 13.3 billion tons in 2020. FHWA’s FAF forecasts that by 2035 trucks will haul 22.8 billion tons of freight.2 The severity of the recent challenging economic environment and rapid decline in freight volumes for all modes was largely unanticipated by most industry experts. Though most sectors of the trucking industry have experienced dramatic declines in freight volumes, tonnage hauled by trucks is expected to grow in the long term, driven by population growth and increased economic activity. Figure A.2. Truck freight volume forecasts. Figure A.2. Truck freight volume forecasts. 5 Rail Freight Volumes Figure A.3. Rail growth forecast. Freight Performance Trend: Increasing Rail freight volumes are expected to increase overall through 2020, putting increasing pressure on an alre dy congested nation l rail network. The source of the rail forecast is the next generation Freight Analysis Framework FAF2 database. FAF2 freight flow origin and destination (O-D) coverage spans 131 freight analysis zones that include 114 freight O-D zones and 17 major ports, border crossings, and freight ports. The FAF2 commodity flow data are benchmarked to 2002 and are forecasted to 2035. This analysis of the rail forecast utilizes the 2008 values and the 2035 estimates. The rail information is available for all transport and then divided into three potential submarkets: domestic, border crossings, and sea movements. Table A.2 presents the 2008 and 2035 values for the rail mode.3 The forecast estimates that total rail traffic will increase by just under 2 percent annually. This increase is present even with an estimated decrease in rail traffic for origin–destination pairs involving sea traffic, with such traffic estimated to decrease by 1.4 percent. Domestic rail movements represent the highest growth, at an estimate of 2.1 percent. Table A.2. Rail volume by category. The forecast rate of freight growth by mode may be defined as the estimated percentage increase in tonnage hauled in future years for the major modes of freight transportation. Baseline figures and forecast tonnage figures are limited to primary shipments (primary shipments are defined as those handled the first time). This measure Segment 2008 Value (tons) 2035 Forecast (tons) Growth Rate Domestic 1,861,312 3,292,228 +2.1% Sea 237,824 164,154 -1.4% Border 145,748 232,987 +1.8% Total 2,244,884 3,689,369 +1.9% Figure A.3. Rail growth forecast. 5 Rail Freight Volumes Figure A.3. Rail growth forecast. Freight Performance Trend: Increasing Rail freight volumes are expected to increase overall through 2020, putting increasing pressure on an already congested national rail network. The source of the rail forecast is the next generation Freight Analysis Framework FAF2 database. FAF2 freight flow origin and destination (O-D) coverage spans 131 freight analysis zones that include 114 freight O-D zones and 17 major ports, border crossings, and freight ports. The FAF2 commodity flow data are benchmarked to 2002 and are forecasted to 2035. This analysis of the rail forecast utilizes the 2008 values and the 2035 estimates. The rail information is available for all transport and then divided into three pote tial submarkets: domestic, border crossings, nd sea movements. Table A.2 p esents the 2008 and 2035 values for the rail mode.3 The forecast estimates that total rail traffic will increase by just under 2 percent annually. This increase is present even with an estimated decrease in rail traffic for origin–destination pairs involving sea traffic, with such traffic estimated to decrease by 1.4 percent. Domestic rail movements represent the highest growth, at an estimate of 2.1 percent. Table A.2. Rail volum by cat gory. The forecast rate of freight growth by mode may be defined as the estimated percentage increase in tonnage hauled in future years for the major modes of freight transportation. Baseline figures and forecast tonnage figures are limited to primary shipments (primary shipments are defined as those handled the first time). This measure Segment 2008 Value (tons) 2035 Forecast (tons) Growth Rate Domestic 1,861,312 3, 92, 28 +2.1% Sea 237,824 164,154 -1.4% Border 145,748 232,987 +1.8% Total 2,244,884 3,689,369 +1.9% table A.2. Rail volume by category. Inland Water Freight Water Freight performance Trend: Mixed Domestic waterborne freight volumes declined slightly from 1991 to 2005 (see Figure A.4) while waterborne imports and exports increased significantly, according to the U.S. Army Corps of Engineers (USACE) Total Waterborne Commerce of the United States.4 These trends are generally attributed to the relative decline of manufacturing in the United States, a sector that relied upon bulk shipments of raw materials. The increasingly globalized economy resulted in increasing import and export volumes. The water information is found for all transport, and then divided into three potential ubmarkets: domestic, border crossings, and sea movements. Table A.3 presents the 2008 and 2035 values for the water mode.5 The forecast estimates that

70 total water traffic will increase by just under 2 percent. Water movements that involve cross-border, origin– destination pairs but are not classified under the Sea category have an estimate for substantially higher percentage growth (5.2 per- cent), but the estimate is based on a very small base forecast (0.26 percent of total traffic), and caution must therefore be given to the rate forecast for this subset. Containerized Imports/Exports Freight performance Trend: steady growth U.S. container traffic through ports has more than doubled since 1995, rising from 22 million TEU6 in 1995 to 45 million in 2007 (see Figure A.5).7 The economic slowdown of 2008 caused units to decline from 45 million in 2007 to 38 million 500 700 900 1100 1300 1500 1700 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 US Waterborne Commerce Foreign Domestic Figure A.4. Waterborne volume. M illi on T o n s Figure A.4. Waterborne volume. 7 Inland Water Freight Water Freight Performance Trend: Mixed Domestic waterborne freight volumes declined slightly in the past 15 years while waterborne imports and exports increased significantly, according to the U.S. Army Corps of Engineers (USACE) Total Waterborne Commerce of the United States.4 These trends are generally attributed to the relative decline of manufacturing in the United States, a sector that relied upon bulk shipments of raw materials. The increasingly globalized economy resulted in increasing import and export volumes. Table A.3. Water freight volumes. Segment 2008 Value (tons) 2035 Forecast (tons) Growth Rate Domestic 519,944 873,863 +1.9% Sea 110,281 161,173 +1.4% Border 1,624 6,457 +5.2% Total 631,849 1,041,394 +1.9% The water information is found for all transport, and then divided into three potential submarkets: domestic, border crossings, and sea movements. Table A.3 presents the 2008 and 2035 values for the water mode.5 The forecast estimates that total water traffic will increase by just under 2 percent. Water movements that involve cross-border, origin–destination pairs but are not classified under the Sea category have an estimate for substanti lly higher percen age growth (5.2 percent), but the estimate is based on a very small base forecast (0.26 percent of total traffic), and caution must therefore be given to the rate forecast for this subset. 500 700 900 1100 1300 1500 1700 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 M ill io n To ns US Waterborne Commerce Foreign Domestic Figure A.4. Waterborne volume. table . Water freight volumes. Figure A.5. U.S. container volume growth.

71 Figure A.4. Waterborne volume. table A.3. Water freight volumes. in 2008. This represents an annualized rate of growth of 4.5 percent for the United States since 1995. The port volumes are not uniform. The top 20 U.S. ports handle more than 96 per- cent of all container movements. Globally, container move- ments tripled from1995 to 2007, rising by 8 percent annually. Three sources of data were identified. Actual data from 2007 are available from USACE’s Navigation Data Center.8 In 2007, U.S. ports handled 17,821,238 TEU of loaded inbound con- tainers, and 10,349,603 TEU of loaded outbound containers. Growth over the last decade was identified through a recent report, America’s Container Ports: Freight Hubs That Connect Our Nation to Global Markets, released by the Bureau of Transportation Statistics (BTS) of the Research and Inno- vative Technology Administration (RITA). The report covers the impact of the recent U.S. and global economic downturn on U.S. port container traffic, trends in container through- put, concentration of containerized cargo at the top U.S. ports, regional shifts in cargo handled, vessel calls and capac- ity in ports, the rankings of U.S. ports among the world’s top ports, and the number of maritime container entries into the United States relative to truck and rail containers. Estimates of growth have been developed by private orga- nizations, but they are generally presented as global estimates. In November 2007, Global Insight, Inc. predicted a global growth rate for 2010 of approximately 6.9 percent.9 More recently, PIERS Trade Horizons forecast a 2.8 percent decline in import volumes in 2009, and a weak recovery to 1.5 per- cent growth in 2010. The same forecast expected exports to contract 6.6 percent in 2009 and fall a further 1.3 percent in 2010. Long-term global growth is expected as China, India, and other developing countries continue to expand their economies. Figure A.5. U.S. container volume growth.

72 Freight Efficiency Measures Following are the measures for the category of Freight Efficiency. Interstate Highway Speeds FHWA sponsored the Freight Performance Measures pro- gram, which is managed by the American Transportation Research Institute (ATRI). It collects and analyzes truck posi- tion data to produce key freight performance measures. As part of this effort, ATRI calculates average speeds over time for a strategic set of U.S. interstate corridors with significant levels of truck activity. The data described in this section are derived from several hundred thousand trucks that operate in the United States. For analytical purposes, interstate routes are divided into 3-mile segments. Truck speeds for each truck movement on one of the 25 interstates studied are calculated and attributed to each segment. The end result is a dataset that allows users to query and conduct customized analyses on more than 60,000 miles (by travel direction) of interstate highway. Freight performance Trend: decreases in overall average speed are expected Interstate highways are a key component of the U.S. freight transportation system. Figures A.6 and A.7 show average truck speeds over a one-month time period on interstate highways in the United States as calculated by the FHWA/ATRI sys- tem. Although these aggregated data over one month do not 10 Interstate Highway Speeds FHWA sponsored the Freight Performance Measures program, which is managed by the American Transportation Research Institute (ATRI). It collects and analyzes truck position data to produce key freight performance measures. As part of this effort, ATRI calculates average speeds over time for a strategic set of U.S. interstate corridors with significant levels of truck activity. Data Description The data described in this section are derived from several hundred thousand trucks that operate in the United States. For analytical purposes, interstate routes are divided into 3-mile segments. Truck speeds for each truck movement on one of the 25 interstates studied are calculated and attributed to each segment. The end result is a dataset that allows users to query and conduct customized analyses on more than 60,000 miles (by travel direction) of interstate highway. Map 1- Northbound Map 2- Southbound Figure A.6. Northbound and southbound IHS speeds. Freight Performance Trend: Decreases in Overall Average Speed Are Expected Interstate highways are a key component of the U.S. freight transportation system. Figures A.6 and A.7 show average truck speeds over a one-month time period on interstate highways in the United States as calculated by the FHWA/ATRI system. Although these aggregated data over one month do not highlight peak periods or incidents and system disruptions, they do indicate that average speeds are higher in rural areas and lower in larger urban regions. As more years of data are analyzed, additional trend lines can be produced to illustrate changes over time. 11 Map 3- Westbound Map 4- Eastbound Figure A.7. Westbound and eastbound IHS speeds. Future Trend Line: Congestion on Interstates Will Increase Recent declines in both truck and automobile travel are in contrast with historical increases in vehicle miles traveled (VMT) In the long term, FHWA predicts that, with no significant increases in capacity, portions of the NHS with recurring congestion will increase four-fold by 2035.10 Comment [JP3]: Author: I’m going by the map labels—you’d better check the maps! The A head and text needed to be moved below the map labels. Figur .7. Westboun and eastbound IHS speeds. Figur .6. Northbou d and southbound IHS speeds. 11 Map 3- Westbound Map 4- Eastbound Figure A.7. Westbound and eastbound IHS speeds. Future Trend Line: Congestion on Interstates Will Increase Recen dec ines in both truck a d automobile trav l are in contrast with historical increases in vehicle miles traveled (VMT) In the long term, FHWA predicts that, with no significant increases in capacity, portions of the NHS with recurring congestion will increase four-fold by 2035.10 Comment [JP3]: Author: I’m going by the map labels—you’d better check the maps! The A head and text needed to be moved below the map labels.

73 Figure A.7. Westbound and eastbound IHS speeds. Figure A.6. Northbound and southbound IHS speeds. highlight peak periods or incidents and system disruptions, they do indicate that average speeds are higher in rural areas and lower in larger urban regions. As more years of data are analyzed, additional trend lines can be produced to illustrate changes over time. Future Trend line: congestion on interstates Will increase Recent declines in both truck and automobile travel are in contrast with historical increases in vehicle miles traveled (VMT). In the long term, FHWA predicts that, with no sig- nificant increases in capacity, portions of the National High- way System (NHS) with recurring congestion will increase four-fold by 2035.10 Figure A.8 offers one method of measuring the performance of the transportation system for freight movements via truck. As shown above, the majority of roadway segments have an average aggregate truck travel speed between 55 mph and 60 mph. The distribution of this curve over time could be a future performance indicator to illustrate change in the num- ber of interstate highway sections with below-average speeds. As shown in Figure A.9, another system performance met- ric is to measure trends related to particular deficiencies. In this case, the focus is on the number of segments with average aggregate speeds that are less than 50 mph; a trend line may be developed as the total number of segments with speeds less than free flow is compared month to month. Figure A.10 identifies the percentage of total segments on each interstate corridor with an average speed less than 50 mph. This measure can be used to compare the performance of various interstates, regardless of overall length. Interstate Highway Reliability Measure In addition to average truck travel speeds or a compari- son of the percentage of segments with average truck speeds less than free-flow, the ATRI/FHWA system can measure the travel-time reliability of corridors and specific segments. Reliability refers to the predictability of travel speeds or travel times. Reliability is highly valued because of the need to pre- dict estimated shipment times. In Figure A.11, Interstate 45 is an example of a highway with a high buffer index, which indicates a large variability in average speed across the entire interstate route. Conversely, Interstates 24 and 65 have lower buffer index scores, suggesting that travel times on the cor- ridors are more reliable and vary less. The ATRI/FHWA Freight Performance Measure (FPM) system features a database that contains historical truck posi- tion data for most of the last decade. The system is updated monthly, and trucks can report position reads as frequently as every 1–5 minutes. Wireless truck position reports are received from approximately 600,000 trucks and cover major highways and surface streets throughout the United States and Canada, as well as Mexico. With the use of this system, it 12 Figure A.8 offers one method of measuring the performance of the transportation system for freight movements via truck. As shown above, the majority of roadway segments have an average aggregate truck travel speed between 55 mph and 60 mph. The distribution of this curve over time could be a future performance indicator to illustrate change in the number of interstate highway sections with below- average speeds. As shown in Figure A.9, another system performance metric is to measure trends related to particular deficiencies. In this case, the focus is on the number of segments with average aggregate speeds that are less than 50 mph; a trend line may be developed as the total number of segments with speeds less than free flow is compared month to month. Figure A.8 Distribution of truck speeds. Figure A.8. Distribution of truck speeds.

74 13 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% I-05 I-10 I-15 I-20 I-24 I-25 I-26 I-35 I-40 I-45 I-55 I-65 I-70 I-75 I-76 I-77 I-80 I-81 I-84 I-85 I-87 I-90 I-91 I-94 I-95 Percentage Co rr id or Percent of Segments < 50 MPH Figure A.9 Segments below 50 mph. Figure A.10 identifies the percentage of total segments on each interstate corridor with an average speed less than 50 mph. This measure can be used to compare the performance of various interstates, regardless of overall length. 14 Figure A.10. Distribution of speeds. Figure A.9. Segments below 50 mph. Figure A.10. Di tribution of speeds.

75 Figure A.9. Segments below 50 mph. Figure A.10. Distribution of speeds. 15 Interstate Highway Reliability Measure 0 5 10 15 20 25 30 35 I-05 I-10 I-15 I-20 I-24 I-25 I-26 I-35 I-40 I-45 I-55 I-65 I-70 I-75 I-76 I-77 I-80 I-81 I-84 I-85 I-87 I-90 I-91 I-94 I-95 Buffer Index Co rr id or Corridor Buffer Index Figure A.11. Buffer index by roadway. In addition to average truck travel speeds or a comparison of the percentage of segments with average truck speeds less than free-flow, the ATRI/FHWA system can measure the travel-time reliability of corridors and specific segments. Reliability refers to the predictability of travel speeds or travel times. Reliability is highly valued because of the need to predict estimated shipment times. In Figure A.11, Interstate 45 is an example of a highway with a high buffer index, which indicates a large variability in average speed across the entire interstate route. Conversely, Interstates 24 and 65 have lower buffer index scores, suggesting that travel times on the corridors are more reliable and vary less. The ATRI/FHWA Freight Performance Measure (FPM) system features a database that contains historical truck position data for most of the last decade. The system is updated monthly, and trucks can report position reads as frequently as every 1–5 minutes. Wireless truck position reports are received from approximately 600,000 trucks and cover major highways and surface streets throughout the United States Fig re A.11. Buffer index by roadway. is possible to conduct a far more focused analysis of average travel rates or system reliability over time. Additional analy- ses could focus on specific days or hours of the day. Data can be analyzed at levels ranging from transcontinental corridors (e.g., Interstate 10) to specific urban intersections. Trend Line of Top Interstate Bottlenecks Table A.4 illustrates how the FPM system can analyze trends in severe highway bottlenecks. The rankings are based on a measure called the total freight congestion value, which is an index that uses truck delay and relative volume informa- tion within bottlenecks as inputs. As evidenced by the high- est total freight congestion value, the top bottleneck affecting freight movement via truck (among the nine listed) occurs in Bergen, New Jersey, on I-95 at SR-4. The ATRI/FHWA FPM system has the ability to produce performance trends for bottlenecks at any freight-significant location, and an index of 100 bottlenecks will be compiled on a quarterly basis during 2010. In the long term, this system could be used to provide trend lines extrapolated over time. Figures A.12, A.13, and A.14 represent graphical depictions of the severity of the Table A.4 freight bottlenecks. Future Trend line: negative The negative impacts of freight bottlenecks are expected to become more severe as the demand for freight transporta- tion continues to grow and peak period congestion increases. Additionally, the annual vehicle miles traveled by passenger vehicles will bolster congestion levels even further. The high quality of data used to identify and rank the top interstate bottlenecks is due to the source of the data—actual trucks that produce a location, time stamp, and speed mea- sure. Before these data are processed by the ATRI/FHWA FPM system, the data undergo extensive data quality procedures. The ATRI/FHWA FPM database contains historical data across much of this decade, and the database is updated monthly. Truck position reports for each truck are produced based on how frequently individual trucks are pinged, which can range between every few minutes to every few hours.

76 17 Table A.4. Significant truck freight bottlenecks. Trend Line of Top Interstate Bottlenecks Table A.4 illustrates how the FPM system can analyze trends in severe highway bottlenecks. The rankings are based on a measure called the total freight congestion value, which is an index that uses truck delay and relative volume information within bottlenecks as inputs. As evidenced by the highest total freight congestion value, the top bottleneck affecting freight movement via truck (among the nine listed) occurs in Bergen, New Jersey, on I-95 at SR-4. The ATRI/FHWA FPM system has the ability to produce performance trends for bottlenecks at any freight-significant location, and an index of 100 bottlenecks will be compiled on a quarterly basis during 2010. In the long term, this system could be used to provide trend lines extrapolated over time. Figures A.12, A.13, and A.14 below represent graphical depictions of the severity of the Table A.4 freight bottlenecks. Bottleneck Number Total Freight Congestion Value 2007 Ranking 2009 Ranking Bottleneck Name/ Location County/State 1 446962 1 1 I-95 @ SR-4 Bergen, NJ 2 446579 4 2 I-95 @ SR-9A (Westside Hwy) New York, NY 3 367781 2 3 I-90 @ I-94 Interchange (“Edens Interchange”) Cook, IL 4 311761 3 4 I-285 @ I-85 Interchange (“Spaghetti Junction”) Dekalb, GA 5 219711 6 5 SR-60 @ SR-57 Interchange Los Angeles, CA 6 198088 8 6 I-45 (Gulf Freeway) @ US-59 Interchange Harris, TX 7 176064 5 7 I-40 @ I-65 Interchange (east) Davidson, TN 8 140206 9 8 I-45 @ I-610 Interchange Harris, TX 9 102906 7 9 I-10 @ I-15 Interchange San Bernardino, CA 18 Future Trend Line: Negative The negative impacts of freight bottlenecks are expected to become more severe as the demand for freight transportation continues to grow and peak period congestion increases. Additionally, the annual vehicle miles traveled by passenger vehicles will bolster congestion levels even further. Figure A.12. I-95 and SR 4 bottleneck data. table A.4. Significant truck freight bottlenecks. Figure A.12. I-95 and SR 4 bottleneck data.

77 table A.4. Significant truck freight bottlenecks. Figure A.12. I-95 and SR 4 bottleneck data. 19 Figure A.13. I-95 and SR 9A bottleneck data. The high quality of data used to identify and rank the top interstate bottlenecks is due to the source of the data—actual trucks that produce a location, time stamp, and speed measure. Before these data are processed by the ATRI/FHWA FPM system, the data undergo extensive data quality procedures. The ATRI/FHWA FPM database contains historical data across much of this decade, and the database is updated monthly. Truck position reports for each truck are produced based on how Figure A.14. I-90 and I-94 bottleneck data. 19 Figure A.13. I-95 and SR 9A bottleneck data. The high quality of data used to identify and rank the top interstate bottlenecks is due to the source of the data—actual trucks that produce a location, time stamp, and speed measure. Before these data are processed by the ATRI/FHWA FPM system, the data undergo extensive data quality procedures. The ATRI/FHWA FPM database contains historical data across much of this decade, and the database is updated monthly. Truck position reports for each truck are produced based on how Figure A.14. I-90 and I-94 bottleneck data. Figure A.13. I-95 and SR 9A bottleneck data. Figure A.14. I-90 and I-94 bottleneck data. granularity: very high Wireless truck position reports are received from approxi- mately 600,000 trucks and cover major highways and surface streets throughout the United States and Canada, as well as Mexico. Data can be analyzed at levels ranging from trans- continental corridors (e.g., Interstate 10) to specific urban intersections. Composite Class I RR Operating Speed Freight performance Trend: slight decline Train speed measures the line-haul movement between ter- minals. The average speed is calculated by dividing train-miles by total hours operated, excluding yard and local trains, pas- senger trains, maintenance-of-way trains, and terminal time. Six major North American railroads voluntarily report train speed on a weekly basis.11 In addition to a composite speed, the railroads report train speed for various compo- nents of their network, such as Intermodal, Multilevel, and Coal Unit. The last 53 weeks of data are available. Table A.5 presents the 53-week unweighted average and standard deviation (across 53 weeks) for each reporting rail- road, for all traffic, as of August 28, 2009. Each railroad also reports the information for multiple categorizations of equipment such as intermodal, coal, or grain trains. While the data are presented in a rolling 53-week format, the presentation of the website is sufficiently simple that an

78 interested state or local agency could easily automate the col- lection of the data each week as they are published. The data are presented for different equipment categoriza- tions, but only at the national level. Estimating speeds for a particular state or region may therefore be challenging. Rail Freight Market Share of Ton Miles Freight performance Trend: growing The market share is defined as the tabulated amount of domestic railroad ton-miles in a particular year divided by the total number of ton-miles of freight transport in the United States. Figure A.15 charts the steady increase in rail freight market share from 1980 through 2006. In 2006, BTS tabulated a total of 1,852,833 tons of rail traffic, out of a total of 4,637,513 tons of traffic across all modes. Rail accounted for 39.95 percent of total traffic in 2006. By comparison, rail accounted for 27.4 percent of total traffic in 1980 and did not pass the 30 percent mark until 1993. The increase in rail ton-miles as a percentage of all ton- miles shipped is credited to several trends. Since deregulation in 1980, the Class I railroads have posted significant increases in efficiencies, timeliness, and volumes. The development of low-sulfur Western coal fields provided significant new mar- kets for the railroads. Also, imports from Asia through West 21 Composite Class I RR Operating Speed Freight Performance Trend: Slight Decline Train speed measures the line-haul movement between terminals. The average speed is calculated by dividing train-miles by total hours operated, excluding yard and local trains, passenger trains, maintenance-of-way trains, and terminal time. Six major North American railroads voluntarily report train speed on a weekly basis.11 In addition to a composite speed, the railroads report train speed for various components of their network, such as Intermodal, Multilevel, and Coal Unit. The last 53 weeks of data are available. Table A.4 presents the 53-week unweighted average and standard deviation (across 53 weeks) for each reporting railroad, for all traffic, as of August 28, 2009. Table A.4. Class I operating speeds. Railroad Operating Speed June 2009 (mph) Operating Speed June 2010 (mph) Burlington Northern Santa Fe 25.97 25.0 Canadian Pacific 25.32 23.4 CSX Transportation 21.40 20.6 Kansas City Southern 27.73 26.6 Norfolk Southern 22.95 21.0 Union Pacific 26.40 25.9 Unweighted Average 24.96 23.75 Each railroad also reports the information for multiple categorizations of equipment such as intermodal, coal, or grai trains. While the data are presented in a rolling 53-week format, the presentation of the website is sufficiently simple that an interested state or local agency could easily automate the collection of the data each week as they are published. The data are presented for different equipment categorizations, but only at the national level. Estimating speeds for a particular state or region may therefore be challenging. table 5. Class I operating speeds. 22 Rail Freight Market Share of Ton Miles Figure A.15. Rail freight market share. Freight Performance Trend: Growing The market share is defined as the tabulated amount of domestic railroad ton-miles in a particular year divided by the total number of ton-miles of freight transport in the United States. Figure A.15 above charts the steady increase in rail freight market share from 1980 through 2006. In 2006, BTS tabulated a total of 1,852,833 tons of rail traffic, out of a total of 4,637,513 tons of traffic across all modes. Rail accounted for 39.95 percent of total traffic in 2006. By comparison, rail accounted for 27.4 percent of total traffic in 1980 and did not pass the 30 percent mark until 1993. The increase in rail ton-miles as a percentage of all ton-miles shipped is credited to several trends. Since deregulation in 1980, the Class I railroads have posted significant increases in efficiencies, timeliness, and volumes. The development of low-sulfur Western coal fields provided significant new markets for the railroads. Also, imports from Asia through West Coast ports provided significant new market opportunities for Class I railroads. The current source for these data is the Bureau of Transportation Statistics.12 BTS is developing more comprehensive and reliable estimates of ton-miles for the air, truck, rail, water, and pipeline modes. Improved estimates for 1960–1989, which will allow more comprehensive and reliable data for the entire period from 1960 to present, are still under development and will be reported when they are completed. It appears that the estimates will be provided on an annual basis, although some of the underlying data used to feed the estimates are not generated annually. The report generally presents information at the national level. The underlying data, however, come from sources with varying levels of granularity. Figure A.15. Rail freight market share.

79 table A.5. Class I operating speeds. Figure A.15. Rail freight market share. Coast ports provided significant new market opportunities for Class I railroads. The current source for these data is the Bureau of Trans- portation Statistics.12 BTS is developing more comprehen- sive and reliable estimates of ton-miles for the air, truck, rail, water, and pipeline modes. Improved estimates for 1960–1989, which will allow more comprehensive and reli- able data for the entire period from 1960 to present, are still under development and will be reported when they are com- pleted. It appears that the estimates will be provided on an annual basis, although some of the underlying data used to feed the estimates are not generated annually. The report gen- erally presents information at the national level. The underly- ing data, however, come from sources with varying levels of granularity. Logistics as a Percentage of GDP performance indicator: paradoxical The cost of logistics as a percentage of GDP fell to the lowest level ever recorded in 2009.13 This precipitous decline generally represents negative trends such as the rapid decline in manufacturing output, the unemployment of thousands of truck drivers, and a significant downturn in truck, rail, air, and water freight movement. As can be seen in Table A.6 and Figure A.16, logistics costs as a percentage of GDP had been generally declining since 1985. The gradual, long-term decline was generally viewed as a positive factor. It repre- sented increased innovation and efficiencies in the logistics industry. That logistics costs were not rising as fast as GDP signaled increased productivity and lower relative costs for moving goods. However, the severe recession of 2008 and 2009 caused logistics volume to fall significantly. The logistics costs decline was viewed as creating unsustainably low prices for goods movements, which were often below the costs of logis- 23 Logistics as a Percentage of GDP Figure A.16. Logistics/GDP. Performance Indicator: Paradoxical The cost of logistics as a percentage of GDP fell to the lowest level ever recorded in 2009.13 This precipitous decline generally represents negative trends such as the rapid decline in manufacturing output, the unemployment of thousands of truck drivers, and a significant downturn in truck, rail, air, and water freight movement. As can be seen in Table A. 6 and Figure A.16, logistics costs as a percentage of GDP had been generally declining since 1985. The gradual, long-term decli e was generally viewed as a positive factor. It represented increased innovation and efficiencies in the logistics industry. That logistics costs were not rising as fast as GDP signaled increased productivity and lower relative costs for moving goods. H wever, the severe reces ion of 2008 and 2009 caused logistics volume to fall significantly. The logistics costs decline was viewed as creating unsustainably low prices for goods movements, which were often below the costs of logistics firms. Layoffs, bankruptcies, and operating losses were prevalent in the logistics industry as a result. Forecast Trend Line: Uncertain The decline in oil prices and extraordinary softness in the economy caused the cost of logistics in relation to GDP to decline in 2008 and 2009, but long-term trends could send the cost upward. After rising 50 Year Transport Inventory Total 1986 6.3 4.9 11.6 1988 6.1 4.9 11.5 1990 6.1 4.9 11.4 1992 5.9 3.7 10 1994 5.9 3.7 10.1 1996 6.0 3.9 10.2 1998 6.0 3.7 10.1 2000 6.0 3.8 10.3 2002 5.6 2.9 8.8 2004 5.6 2.9 8.8 2006 6.1 3.4 9.9 2008 6.1 2.9 9.4 2009 4.9 2.5 7.7 23 Logistics as a Percentage of GDP Figure A.16. Logistics/GDP. Performance Indicator: Paradoxical The cost of logistics as a percentage of GDP fell to the lowest level ever recorded in 2009.13 This precipitous decline generally represents negative trends such as the rapid decline in manufacturing output, the unemployment of thousands of truck drivers, and a significant downturn in truck, rail, air, and water freight movement. As can be seen in Table A. 6 and Figure A.16, logistics costs as a percentage of GDP had been generally declining since 1985. The gradual, long-term decline was generally viewed as a positive factor. It represented increased innovation and efficiencies in the logistics industry. That logistics costs were not rising as fast as GDP signaled increased productivity and lower relative costs for moving goods. However, the severe recession of 2008 and 2009 caused logistics volume to fall significantly. The logistics costs decline was viewed as creating unsustainably low prices for goods movements, which were often below the costs of logistics firms. Layoffs, bankruptcies, and operating losses were prevalent in the logistics industry as a result. Forecast Trend Line: Uncertain The decline in oil prices and extraordinary softness in the economy caused the cost of logistics in relation to GDP to decline in 2008 and 2009, but long-term trends could send the cost upward. After rising 50 Year Transport Inventory Total 1986 6.3 4.9 11.6 1988 6.1 4.9 11.5 1990 6.1 4.9 11.4 1992 5.9 3.7 10 1994 5.9 3.7 10.1 1996 6.0 3.9 10.2 1998 6.0 3.7 10.1 2000 6.0 3.8 10.3 2002 5.6 2.9 8.8 2004 5.6 2.9 8.8 2006 6.1 3.4 9.9 2008 6.1 2.9 9.4 2009 4.9 2.5 7.7 Figure A.16. Logistics/GDP. table A.6. Logistics costs as percentage of GDp. tics firms. Layoffs, bankruptcies, and operating losses were prevalent in the logistics industry as a result. Forecast Trend line: uncertain The decline in oil prices and extraordinary softness in the economy caused the cost of logistics in relation to GDP to decline in 2008 and 2009, but long-term trends could send the cost upward. After rising 50 percent in the previous five

80 years, total logistics costs fell in 2008 and fell further in 2009. Inventory carrying costs plunged primarily in 2008 because interest rates were more than 50 percent lower than they were in 2007. In 2009, transportation costs fell significantly to push logistics as a percentage of GDP to 7.7 percent. In the years leading up to the recession of 2001, logistics costs as a percentage of GDP had been rising until they passed the 10 percent mark. Greater efficiencies and innovations caused the rate to fall in the mid-2000s. The recession of 2008 caused overall freight movement to plummet, driving overall logis- tics costs further downward. When the economy rebounds, there will be fewer trucks in service as the trucking industry has shed excess drivers and vehicles. Also, the recession softened demand for fuel. As the economy rebounds, these factors plus inventory costs could put upward pressure on logistics costs.

81 Freight System Condition Indicators Following are the summaries for the category of Freight System Condition. NHS Bridge Structural Deficiencies performance Trend: positive As illustrated in Figure A.17, the performance trends for the NHS bridges have steadily improved since the early 1990s. Overall, structural deficiencies as a percentage of total bridge deck area on the NHS have dropped nearly 42 percent. In 1992, 13.32 percent of the national network as measured by bridge area was structurally deficient, but that number had declined to 7.7 percent by 2008. (Bridge area captures the size of the deficient inventory, not just the number of bridges.) The improvement in conditions began occurring during the era of increased spending resulting from the Intermodal Surface Transportation Efficiency Act of 1991 and continued through the next two subsequent transportation acts. The primary considerations in classifying structural defi- ciencies are the bridge component condition ratings for the deck, superstructure, and substructure. These structural deficiencies are considered separately from “functional obso- lescence,” which measures geometric issues such as width, approach curvature, or other issues that may reflect cur- rent design standards and not the structural integrity of the bridge. The data quality for the NHS bridge structural deficiencies is rated high because of the extensive data quality protocols in place for the national bridge inspection program. The data must be updated only every other year under federal bridge inspection standards. Bridge data can be accessed by struc- ture but only as a result of manual effort. Changes over time also require additional data processing. The future trend line of NHS bridge structural deficien- cies is uncertain. After 15 years of improvement, significantly higher material prices starting in 2005 have greatly dimin- ished state DOTs’ purchasing power. Although the effects have not yet shown up in the annual inventories, the higher prices will make it more difficult to sustain the progress on bridge conditions. NHS Pavement Conditions performance indicator: rising conditions, uncertain Future As can be seen in Table A.7 and Figure A.18, pavement conditions on the NHS have steadily improved but the over- all condition of the network remains mixed. As of 2004, 52 percent of the vehicle miles traveled (VMT) on the NHS occurred upon “good” pavements. Those are ones with an International Roughness Index of less than 95. The percent- age of VMT occurring on “acceptable” pavements is much higher, 91 percent, in 2004. Data for this measurement are compiled from FHWA’s Highway Performance Management System (HPMS). States collect pavement condition data on a statistically valid sample of roadway sections. The condition data are compiled every two years and provide the basic input into the HPMS, which is used for national system monitoring. The data are available biennially on a state and regional basis. Because the data are sample based, they are not avail- 26 NHS Bridge Structural Deficiencies Figure A.17. NHS bridge deficiencies. Performance Trend: Positive As illustrated above, the performance trends for the NHS bridges have steadily improved since the early 1990s. Overall, structural deficiencies as a percentage of total bridge deck area on the NHS have dropped nearly 42 percent. In 1992, 13.32 percent of the national network as measured by bridge area was structurally deficient, but that number had declined to 7.7 percent by 2008. (Bridge area captures the size of the deficient inventory, not just the number of bridges.) The improvement in conditions began occurring during the era of increased spending resulting from the Intermodal Surface Transportation Efficiency Act of 1991 and continued through the next two subsequent transportation acts. The primary considerations in classifying structural deficiencies are the bridge component condition ratings for the deck, superstructure, and substructure. These structural deficiencies are considered separately from “functional obsolescence,” which measures geometric issues such as width, approach curvature, or other issues that may reflect current design standards and not the structural integrity of the bridge. The data quality for the NHS bridge structural deficiencies is rated high because of the extensive data quality protocols in place for the national bridge inspection program. The data must be updated only every other year under federal bridge inspection standards. Bridge data can be accessed by structure but only as a result of manual effort. Changes over time also require additional data processing. The future trend line of NHS bridge structural deficiencies is uncertain. After 15 years of improvement, significantly higher material prices starting in 2005 have greatly diminished state DOTs’ purchasing power. Although the effects have not yet shown up in the annual inventories, the higher prices will make it more difficult to sustain the progress on bridge conditions. Figure A.17. NHS bridge deficiencies.

82 able for every roadway section. FHWA’s biennial Condi- tion and Performance Report was last updated in 2004. At that time, its modeling indicated that then-current levels of expenditures nationally were adequate to sustain system conditions into the future.14 However, since that forecast, mate rials and construction prices have risen dramatically as a result of higher oil prices in 2005–2007. The higher prices will significantly affect forecast costs of sustaining the sys- tem. With the reauthorization of the federal highway pro- grams on hold and with expenditure levels uncertain, the future condition of the NHS pavement conditions is also uncertain. 27 NHS Pavement Conditions Figure A.18. NHS pavement conditions. Performance Indicator: Rising Costs, Uncertain Future As can be seen in Table A.7 and Figure A.18, pavement conditions on the NHS have steadily improved but the overall condition of the network remains mixed. As of 2004, 52 percent of the vehicle miles traveled (VMT) on the NHS occurred upon “good” pavements. Those are ones with an International Roughness Index of less than 95. The percentage of VMT occurring on “acceptable” pavements is much higher, 91 percent, in 2004. Data for this measurement are compiled from FHWA’s Highway Performance Management System (HPMS). States collect pavement condition data on a statistically valid sample of roadway sections. The condition data are compiled every two years and provide the basic input into the HPMS, which is used for national system monitoring. The data are available biennially on a state and regional basis. Because the data are sample based, they are not available for every roadway section. FHWA’s biennial Condition and Performance Report was last updated in 2004. At that time, its modeling indicated that then-current levels of expenditures nationally were adequate to sustain system conditions into the future.14 However, since that forecast, materials and construction prices have risen dramatically as a result of higher oil prices in 2005–2007. The higher prices will significantly affect forecast costs of sustaining the system. With the reauthorization of the federal highway programs on hold and with expenditure levels uncertain, the future condition of the NHS pavement conditions is also uncertain. 2002 2004 Rural Good 66.6% 68% Acceptable 96.9% 97% Urban Good 38.6% 42.5% Acceptable 86.3% 86.9% Total Good 50% 52% Acceptable 91% 91% Comment [JP5]: Author: Should this be “Costs” instead of “Conditions”? Deleted: Conditions table A.7. NHS pavement conditions. 27 NHS Pavement Conditions Figure A.18. NHS pavement conditions. Performance Indicator: Rising Costs, Uncertain Future As can be seen in Table A.7 and Figure A.18, pavement conditions on the NHS have steadily improved but the overall condition of the network remains mixed. As of 2004, 52 percent of the vehicle miles traveled (VMT) on the NHS occurred upon “good” pavements. Those are ones with an International Roughness Index of less than 95. The percentage of VMT occurring on “acceptable” pavements is much highe , 91 percent, in 2004. Data for this measurement are compiled from FHWA’s Highway Performance Management System (HPMS). States collect pavement condition data on a statistically valid sample of roadway sections. The condition data are compiled every two years and provide the basic input into the HPMS, which is used for national system monitoring. The data are available biennially on a state and regional basis. Because the data are sample based, they are not available for every roadway section. FHWA’s biennial Condition and Performance Report was last updated in 2004. At that time, its modeling indicated that then-current levels of expenditures nationally were adequate to sustain system conditions into the future.14 However, since that forecast, materials and construction prices have risen dramatically as a result of higher oil prices in 2005–2007. The higher prices will significantly affect forecast costs of sustaining the system. With the reauthorization of the federal highway programs on hold and with expenditure levels uncertain, the future condition of the NHS pavement conditions is also uncertain. 2002 2004 Rural Goo 66.6% 68% Acceptable 96.9% 97% Urban Good 38.6% 42.5% Acceptable 86.3% 86.9% Total Good 50% 52% Acceptable 91% 91% Comment [JP5]: Author: Should this be “Costs” instead of “Conditions”? Deleted: Conditions Figure A.18. NHS pavement conditions.

83 Figure A.18. NHS pavement conditions. Freight Environmental Measures Following are the summaries for the category of Environ- mental performance measures. Truck Emissions Over the last several decades the total amount and per- vehicle rates of emissions by large/heavy-duty trucks have declined overall, with the exception of greenhouse emis- sions. These declines may be attributed to the stricter emis- sion standards, cleaner engines and fuel mandates, and voluntary industry efforts to reduce fuel consumption. Fuel consumption reduction efforts include motor carrier participation in EPA’s SmartWay program and the increased use of technologies that reduce the need for drivers to idle trucks. Trucks, cars, railroad locomotives, and marine vessels are defined by EPA as mobile pollution sources. These vehicles/ vessels create emissions during the consumption of fossil fuels, namely, gasoline and diesel fuel. These pollutants are also emitted by stationary sources, including industrial facili- ties and power plants. Air quality planners assign estimated volumes of pollut- ants, typically measured in tons or metric tons, to specific sources of emissions. Emissions included as performance measures for the freight transportation system include: • Particulate matter (PM) • Oxides of nitrogen (NO x ) • Volatile organic compounds (VOCs) • Ozone • Greenhouse gas emissions (GHE) Future Trend line: a continued decline in overall emissions Emissions attributed to large/heavy-duty trucks for the majority of emission types have declined since 2002 (see Fig- ure A.19) and are expected to continue to decline through at least 2020. The exception is for carbon emissions, which are predicted to increase. As a performance measure, emission rates/factors for PM, NO x , and VOC are a more robust mea- sure of future trend lines than is total tonnage of emissions. “Per truck” or “per unit” emission rates are not affected by external factors such as less freight transportation activities due to depressed economic environments. Emission rates/ factors are commonly expressed in grams per brake-horse- power hour (g/bhp-hr), grams per mile, or pounds per gallon of fuel consumed. As of October 10, 2007, air-quality data show that about 144 million people live in areas that violate air-quality standards for ground-level ozone, also called smog, and about 88 million people live in areas that violate air-quality standards from PM. These pollutants contribute to serious public health problems that include premature mortality, aggravation of respiratory and cardiovascular disease, and aggravation of existing asthma, acute respiratory symptoms and chronic bronchitis. Beyond the impact that diesel engines have on our nation’s ambient air quality, exposure to diesel exhaust has been classified by EPA as being likely carcinogenic to humans. Children, people with heart and lung diseases, and the elderly are most at risk.15 Figure A.19. Truck emission reductions.

84 Particulates There are two types of particulate matter: PM less than 2.5 microns in diameter (PM-2.5) and PM less than 10 microns in diameter (PM-10). Emission factors—the rates at which known sources emit pollutants—for PM-2.5 (commonly referred to as “fine particulates”) are an emerging area of air- quality metrics. As illustrated in Figure A.20, the performance trend for PM-10 produced by large trucks is positive in terms of pol- lutants being reduced. EPA estimates that total heavy-duty truck PM-10 emissions will have declined by nearly half (46 percent) between 2002 and 2010.16 EPA further estimates that total emissions from these vehicles will decline by more than two-thirds (71 percent) by 2020 to 34,760 tons. This is the highest percentage decline in PM emissions for any freight transportation mode. This decline is based on EPA’s Mobile 6 model (v. 6.2), which assumes that by 2020 nearly all trucks engaged in freight transportation will have met the 2007 engine stan- dards.17 In addition, the introduction of low-sulfur diesel fuel contributes to particulate reductions. EPA’s National Emissions Inventory estimates that in 2005, the latest year for which data are available, heavy-duty diesel trucks produced 101,174 tons of Primary PM-10 and 87,306 tons of Primary PM-2.5.18 The increased use of newer, cleaner diesel engines and ultra-low sulfur diesel (ULSD) has reduced and will con- tinue to reduce PM for diesel engines (Figure A.21)19. PM rate reductions were first mandated in the mid-1980s. As shown in Figure A.21, the 2010 and 2020 total PM-10 emission factors for the three primary configurations of trucks, classified by EPA as heavy-duty trucks, are signifi- cantly lower than the 2002 factors.20 For combination diesel 31 Particulates Heavy-Duty Truck PM-10 Emissions - 20,000 40,000 60,000 80,000 100,000 120,000 140,000 2002 2010 2020 Year To ta l T on s Figure A.20. Particulate reductions. There are two types of particulate matter: PM less than 2.5 microns in diameter (PM-2.5) and PM less than 10 microns in diameter (PM-10). Emission factors—the rates at which known sources emit pollutants—for PM-2.5 (commonly referred to as “fine particulates”) are an emerging area of air-quality metrics. As illustrated above (Figure A.20), the performance trend for PM-10 produced by large trucks is positive. EPA estimates that total heavy-duty truck PM-10 emissions will have declined by nearly half (46 percent) between 2002 and 2010.16 EPA further estimates that total emissions from these vehicles will decline by more than two-thirds (71 percent) by 2020 to 34,760 tons. This is the highest percentage decline in PM emissions for any freight transportation mode. This decline is based on EPA’s Mobile 6 model (v. 6.2), which assumes that by 2020 nearly all trucks engaged in freight transportation will have met the 2007 engine standards.17 In addition, the introduction of low-sulfur diesel fuel contributes to particulate reductions. EPA’s National Emissions Inventory estimates that in 2005, the latest year for which data are available, heavy-duty diesel trucks produced 101,174 tons of Primary PM-10 and 87,306 tons of Primary PM-2.5.18 The increased use of newer, cleaner diesel engines and ultra-low sulfur diesel (ULSD) has reduced and will continue to reduce PM for diesel engines (Figure A.21)19. PM rate reductions were first mandated in the mid-1980s. 32 Heavy Duty Truck PM-10 Emission F ctors, U ban Highway 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 Single-Unit Gasoline Single-Unit Diesel Combination Diesel Truck Type G ra m s/ M ile 2002 2010 2020 Figure A.21. Total PM-10 truck emission factors. As shown in Figure A.21, the 2010 and 2020 total PM-10 emission factors for the three primary configurations of trucks, classified by the EPA as heavy-duty trucks, are significantly lower than the 2002 factors.20 For combination diesel trucks, the emission rate in 2020 will be 82 percent lower than in 2002. Figure A.20. Particulate reductions. Figure A.21. Total PM-10 truck emission factors.

85 Figure A.20. Particulate reductions. Figure A.21. Total PM-10 truck emission factors. trucks, the emission rate in 2020 will be 82 percent lower than in 2002. Truck NOx Emissions Oxides of nitrogen, NO x , are a precursor, along with VOCs, of ground-level ozone. Ozone, informally known as smog, is a significant pollutant that has been attributed to thousands of premature deaths annually. It forms when NO x and VOCs interact with sunlight, particularly at higher temperatures. The performance trends for truck-generated NO x are also positive and show significant decreases from 2002 to 2010 (Figure A.22). EPA estimates total NO x emissions from large trucks will decline from 3.78 million tons in 2002 to 2.19 mil- lion tons in 2010, a 42 percent decrease.21 By 2020, NO x emis- sions from large trucks are expected to decrease 82 percent below 2002 levels to 662,600 tons. This is the highest percent- age decline in total NO x emissions for any freight transporta- tion mode. The reduction in NO x is attributable to the use of ULSD and the phase-in of cleaner diesel engines that must meet 2007 emission standards (see Figure A.23). As the cur- rent fleet is retired and new vehicles purchased, the emission reductions will increase. VOCs volatile organic compounds Similar to PM and NO x emission factors, VOC emission factors for the three main types of truck configurations are expected to decline significantly from 2002 to 2020 (Figure A.24).22 These declines include an approximate decrease of 90 percent for single-unit gasoline vehicles, 36 percent for single-unit diesel vehicles, and a 53 percent for combination diesel vehicles. Again, the reductions are attributable to cleaner fuels and cleaner vehicles. Greenhouse Emissions Greenhouse emissions (GHE) consist of six types of pol- lutants, including carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), and fluorinated gases. Of the GHE, CO 2 is the primary gas produced during fossil fuel consump- tion. At least some amounts of these gases are found in the atmosphere naturally. GHE are not currently regulated by the federal government, though EPA has recently proposed a rule mandating that large sources of GHE annually report amounts of GHE emitted. 33 Truck NOx Emissions Oxides of nitrogen, NOx, are a precursor, along with VOCs, of ground-level ozone. Ozone, informally known as smog, is a significant pollutant that has been attributed to thousands of premature deaths annually. It forms when NOx and VOCs interact with sunlight, particularly at higher temperatures. The performance trends for truck-generated NOx are also positive and show significant decreases from 2002 to 2010 (Figure A.22). The EPA estimates total NOx emissions from large trucks will decline from 3.78 million tons in 2002 to 2.19 million tons in 2010, a 42 percent decrease.21 By 2020, NOx emissions from large trucks are expected to decrease 82 percent below 2002 levels to 662,600 tons. This is the highest percentage decline in total NOx emissions for any freight transportation mode. The reduction in NOx is attributable to the use of ULSD and the phase-in of cleaner diesel engines that must meet 2007 emission standards. As the current fleet is retired and new vehicles purchased, the emission reductions will increase. Heavy Duty Truck NOx Emission Factors, Urban Freeway 0 5 10 15 20 25 30 Single-Unit Gasoline Single-Unit Diesel Combination Diesel Truck Type G ra m s/ M ile 2002 2010 2020 Figure A-23. Heavy-duty NOx emission factors. Heavy-Duty Truck NOx Emissions - 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000 4,000,000 2002 2010 2020 Year An nu al T on s Figure A.22. NOx reductions. 33 Truck NOx Emissions Oxides of nitrogen, NOx, are a precursor, along with VOCs, of ground-level ozone. Ozone, informally known as smog, is a significant pollutant that has been attributed to thousands of premature deaths annually. It forms when NOx and VOCs interact with sunlight, particularly at higher temperatures. The performance trends for truck-generated NOx are also positive and show significant decreases from 2002 to 2010 (Figure A.22). The EPA estimates total NOx emissions from large trucks will decline from 3.78 million tons in 2002 to 2.19 million tons in 2010, a 42 percent decrease.21 By 2020, NOx emissions from large trucks are expected to decrease 82 percent below 2002 levels to 662,600 tons. This is the highest percentage decline in total NOx emissions for any freight transportation mode. The reduction in NOx is attributable to the use of ULSD and the phase-in of cleaner diesel engines that must meet 2007 emission standards. As the current fleet is retired and new vehicles purchased, the emission reductions will increase. Heavy-Duty Truck NOx Emission Factors, Urban Freeway 0 5 10 15 20 25 30 Single-Unit Gasoline Single-Unit Diesel Combination Diesel Truck Type G ra m s/ M ile 2002 2010 2020 Figure A-23. Heavy-duty NOx emission factors. Heavy-Duty Truck NOx Emissions - 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500,000 4,000,000 2002 2010 2020 Year An nu al T on s Figure A.22. NOx reductions. Figure A.23. Heavy-duty NOx emission factors. Figure A.22. NOx reductions.

86 Between 1990 and 2007, EPA estimates that CO 2 emissions, the primary GHE produced by medium- and heavy-duty trucks, increased significantly (Figure A.25).23 EPA attributes this increase to growth in demand for freight movement by truck and the subsequent increase in miles traveled by these vehicles. In 2007, truck VMT in the United States was 318.8 billion miles.24 As shown in Figure A.26, EPA allocates the majority (61 percent) of transportation-related CO 2 to the consump- tion of gasoline by light-duty vehicles (cars, light trucks, SUVs).25 Medium- and heavy-duty trucks are allocated nearly a quarter (22 percent) of total CO 2 transportation sector emissions. Total GHE are typically measured in metric tons or tera- grams26 of CO 2 equivalent (Tg CO 2 Eq.). Between 1990 and 2007, the total amount of CO 2 emitted by all modes of trans- portation increased from 1,484.5 teragrams of CO 2 equiva- lent (Tg CO 2 Eq.) to 1,887.4 Tg CO 2 Eq. 27 EPA attributes this growth in CO 2 emissions to an increase in the demand for transportation, low fuel prices, and economic growth. Dur- ing this same time period, total GHE from all sources (mobile and stationary) increased from 5,076.7 Tg CO 2 Eq to 6,103.4 Tg CO 2 Eq. Future estimates of GHE attributed to “Freight Trucks,” defined as trucks with a gross vehicle weight rating (GVWR) over 10,000 pounds, are provided by the U.S. Department of Energy, Energy Information Administration (EIA). EIA esti- mates that in 2009, these vehicles will emit 335.34 Tg CO 2 Eq., a lower amount than in 2007.28 By 2030, EIA forecasts that these vehicles will emit 446.43 Tg CO2 Eq. (Figure A.27). GHE estimates for mobile sources are based on the vol- umes of diesel and/or gasoline taxed and the estimated VMT in each state.29 A common methodology for estimating truck- related GHE includes: determining/estimating total fuel 34 VOCs Heavy-Duty Truck VOC Emission Factors, Urban Freeway 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Single-Unit Gasoline Single-Unit Diesel Combination Diesel Truck Type G ra m s/ M ile 2002 2010 2020 Figure A.24. VOC reductions. Volatile Organic Compounds Similar to PM and NOx emission factors, VOC emission factors for the three main types of truck configurations are expected to decline significantly from 2002 to 2020 (Figure A.24).22 These declines include an approximate decrease of 90 percent for single-unit gasoline vehicles, 36 percent for single-unit diesel vehicles, and a 53 percent for combination diesel vehicles. Again, the reductions are attributable to cleaner fuels and cleaner vehicles. Greenhouse Emissions Greenhouse emissions (GHE) consist of six types of pollutants, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorinated gases. Of the GHE, CO2 is the primary gas produced during fossil fuel consumption. At least some amounts of these gases are found in the atmosphere naturally. GHE are not currently regulated by the federal government, though the EPA has recently proposed a rule mandating that large sources of GHE annually report amounts of GHE emitted. Between 1990 and 2007, EPA estimates that CO2 emissions, the primary GHE produced by medium- and heavy-duty trucks, increased significantly (Figure A.26).23 EPA attributes this increase to growth in demand for freight movement by truck and the subsequent increase in miles traveled by these vehicles. In 2007, truck VMT in the United States was 318.8 billion miles.24 Figure A.24. VOC reductions. 36 Carbon Dioxide Emissions Medium and Large Trucks 0 50 100 150 200 250 300 350 400 450 1990 2007 Ye r C O 2 E m is si on s (T g) o r An nu al V M T (b ill io n ve hi cl e m ile s tr av el ed ) C02 VMT 79% increase in CO2 emissions 55% increase in VMT Figure A.26. Truck carbon emissions. Estimated Future Carbon Dioxide Emissions to 2030, Freight Trucks 300 325 350 375 400 425 450 475 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 26 20 27 20 28 20 29 20 30 Year M et ric T on s Eq ui va le nt Figure A.27. Forecast carbon emissions. Future estimates of GHE attributed to “Freight Trucks,” defined as trucks with a gross vehicle weight rating (GVWR) over 10,000 pounds, are provided by the U.S. Department of Energy, Energy Information Administration (EIA). EIA estimates that in 2009, these vehicles will emit 335.34 Tg CO2 Eq., a lower amount than in 2007.28 By 2030, EIA forecasts that these vehicles will emit 446.43 Tg CO2 Eq. (Figure A.27). GHE estimates for mobile sources are based on the volumes of diesel and/or gasoline taxed and the estimated VMT in each state.29 A common methodology for estimating truck-related GHE includes: determining/estimating total fuel consumption by fuel type and sector;30 adjusting up these estimates based on VMT data; estimating CO2 emissions, and allocating transportation emissions by vehicle type. Figure A.25. Truck carbon emissions.

87 35 Percentage of Carbon Dioxide Emissions, by Transportation Sector Use Car/Light-Duty Trucks/SUVs, 61% Medium and Heavy- Duty Trucks, 22% Commercial Aircraft, 8% Other, 10% Figure A.25. Greenhouse emission sources. As shown above in Figure A.25, EPA allocates the majority (61 percent) of transportation-related CO2 to the consumption of gasoline by light-duty vehicles (cars, light trucks, SUVs).25 Medium- and heavy-duty trucks are allocated nearly a quarter (22 percent) of total CO2 transportation sector emissions. Total GHE are typically measured in metric tons or teragrams26 of CO2 equivalent (Tg CO2 Eq.). Between 1990 and 2007, the total amount of CO2 emitted by all modes of transportation increased from 1,484.5 teragrams of CO2 equivalent (Tg CO2 Eq.) to 1,887.4 Tg CO2 Eq. 27 EPA attributes this growth in CO2 emissions to an increase in the demand for transportation, low fuel prices, and economic growth. During this same time period, total GHE from all sources (mobile and stationary) increased from 5,076.7 Tg CO2 Eq to 6,103.4 Tg CO2 Eq. Figure A.26. Greenhouse emission sources. Figure A.24. VOC reductions. Figure A.25. Truck carbon emissions. consumption by fuel type and sector;30 adjusting up these estimates based on VMT data; estimating CO 2 emissions; and allocating transportation emissions by vehicle type. In addition, these estimates may also be based on surveys of truck usage by motor carriers and vehicle-miles-per-gallon averages. Estimates of future amounts of GHE attributable to freight movement are based on assumptions of future economic activity (and subsequent freight volumes) as well as pub- lic policies that make an effort to curtail greenhouse gases. Though government agencies increasingly are making efforts to regulate GHE, the long-term impact of these policies is uncertain. Additionally, the future impact of industry efforts to reduce fuel consumption and GHE by participating in voluntary environmental programs and the increased use of idle-reduction technologies is also not easily quantified. EPA annually updates the National Inventory of U.S. Greenhouse Gas Emissions and Sinks. Included in the update are new estimation methodologies and revised calculations of all previous years’ estimates. As compared to other truck-related emissions, GHE are typically a function of how much fuel is consumed in a spe- cific jurisdiction. Unlike PM, NO x , and VOC, the age of the truck’s engine is less important than fuel economy. On the national level, the amount of fuel consumed (derived from the amount of fuel taxed or purchased) multiplied by the emission factor likely provides a reasonable estimate of the amount of GHE emitted by different vehicle types operating in the United States. This is based on the assumption that fuel taxed/purchased in this country is likely consumed here. On the state level, interstate motor carriers track these activities on the individual truck level and can provide fuel consumption by state (though the reported figures are typi- cally based on fleet averages). On the metropolitan/local level, however, the relationship between where fuel is purchased and where it is consumed is not known to an acceptable degree of certainty. Rail-Produced Greenhouse Gas Emission Data available from EPA indicate that GHE from rail transport have steadily increased from 1990 (38.1 MMT) until 2006 (51.8 MMT), then dropped slightly in 2007 (50.8 Figure A.27. Forecast carbon emissions. 36 Carbon Dioxide Emissions Medium and Large Trucks 0 50 100 150 200 250 300 350 400 450 1990 2007 Year C O 2 E m is si on s (T g) o r An nu al V M T (b ill io n ve hi cl e m ile s tr av el ed ) C02 VMT 79% increase in CO2 emissions 55% increase in VMT Figure A.26. Truck carbon emissions. Estimated Future Carbon Dioxide Emissions to 2030, Freight Trucks 300 325 350 375 400 425 450 475 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 26 20 27 20 28 20 29 20 30 Year M et ric T on s Eq ui va le nt Figure A.27. Forecast carbon emissions. Future estimates of GHE attributed to “Freight Trucks,” defined as trucks with a gross vehicle weight rating (GVWR) over 10,000 pounds, are provided by the U.S. Department of Energy, Energy Information Administration (EIA). EIA estimates that in 2009, these vehicles will emit 335.34 Tg CO2 Eq., a lower amount than in 2007.28 By 2030, EIA forecasts that these vehicles will emit 446.43 Tg CO2 Eq. (Figure A.27). GHE estimates for mobile sources are based on the volumes of diesel and/or gasoline taxed and the estimated VMT in each state.29 A common methodology for estimating truck-related GHE includes: determining/estimating total fuel consumption by fuel type and sector;30 adjusting up these estimates based on VMT data; estimating CO2 emissions, and allocating transportation emissions by vehicle type.

88 MMT).31 The increase was attributable to increased rail freight volume, and the decline was attributable to a reduc- tion in train traffic. As shown in Figure A.28, this growth rate is approximately twice the growth rate of total national greenhouse gas emissions.32 EPA’s annual inventory of U.S. Greenhouse Gas Emissions and Sinks does not distinguish between freight rail and pas- senger rail. For the purposes of this report, the entire GHE value is used as a proxy for freight rail GHE. Since 2005, data have been published on an annual basis. The information pub- lished in 2009 contained data through calendar year 2007.33 Water-Produced Greenhouse Gas Emissions Freight performance Trend: Mixed EPA data indicate that greenhouse gases from water trans- port in 2005 (45.4 MMT) through 2007 (50.8 MMT) have decreased from a peak in 2000 (61.0 MMT). Figure A.29 shows how water-freight-based GHE have varied when com- pared to all national GHE. There have been three bands of relative performance: a growth period during the 1990s, a sharp decline early in the decade, and new growth over the past several years. The EPA inventory information published in 2009 con- tained data through calendar year 2007.34 Note that the analy- sis does not distinguish between freight water transport and passenger water transport such as ferries; for the purposes of this report the entire greenhouse gas value is used as a proxy for waterborne freight GHE. Rail VOCs and NOx Freight performance Trend: decreasing EPA standards adopted in 2008 are forecast to lead to a near 90 percent reduction in railroad locomotive emissions for the three primary pollutants, VOCs, NO x , and particulates.35 The standards will rely on a new generation of locomotive engine technology, with intermediate engine technology required for remanufactured locomotives. EPA anticipates that these engines may account for an even greater share of overall emissions over the next few decades 38 Rail-Produced Greenhouse Gas Emission Rail Greenhouse Gas Growth Compared to All US Greenhouse Gas (1990-2007) 30 35 40 45 50 55 1990 1995 2000 2005 2010 Year R ai l G H G (M M T C O 2) Rail GHG All US GHG (Indexed) Figure A.28. Rail greenhouse emissions. Data available from EPA indicate that GHE from rail transport have steadily increased from 1990 (38.1 MMT) until 2006 (51.8 MMT), then dropped slightly in 2007 (50.8 MMT).31 The increase was attributable to increased rail freight volume, and the decline was attributable to a reduction in train traffic. As shown in Figure A.28 above, this growth rate is approximately twice the growth rate of total national greenhouse gas emissions.32 EPA’s annual inventory of U.S. Greenhouse Gas Emissions and Sinks does not distinguish between freight rail and passenger rail. For the purposes of this report, the entire GHE value is used as a proxy for freight rail GHE. Since 2005, data have been published on an annual basis. The information published in 2009 contained data through calendar year 2007.33 Water-Produced Greenhouse Gas Emissions 39 Water Greenhouse Gas Growth Compared to All US Greenhouse Gas (1990-2007) 35 40 45 50 55 60 65 70 1990 1995 2000 2005 2010 Year W at er G H G (M M T CO 2) Water GHG All US GHG (Indexed) Figure A.29. Water-freight greenhouse emissions. Freight Performance Trend: Mixed EPA data indicate that greenhouse gases from water transport in 2005 (45.4 MMT) through 2007 (50.8 MMT) have decreased from a peak in 2000 (61.0 MMT). Figure A.29 above shows how water-freight- based GHE have varied when compared to all national GHE. There have been three bands of relative performance: a growth period during the 1990s, a sharp decline early in the decade, and new growth over the last several years. The EPA inventory information published in 2009 contained data through calendar year 2007.34 Note that the analysis does not distinguish between freight water transport and passenger water transport such as ferries; for the purposes of this report the entire greenhouse gas value is used as a proxy for waterborne freight GHE. Rail VOC and NOx Figure A.29. Water-freight greenhouse emissions. Figure A.28. Rail greenhouse emissions.

89 Figure A.29. Water-freight greenhouse emissions. Figure A.28. Rail greenhouse emissions. as other emission control programs take effect for cars and trucks and other nonroad emissions sources. Estimates show that, without the emission reductions, by 2030 locomotive and marine diesel engines would contribute more than 65 percent of national mobile-source diesel PM2.5, or fine par- ticulate, emissions and 35 percent of national mobile-source NO x emissions, a key precursor to ozone and secondary PM formation. According to the EPA 2005 National Emissions Inventory, rail equipment primarily engaged in freight transportation emitted 1,118,786 tons of NO x .36 The forecast trend, as dem- onstrated in Figure A.30,37 is that these emissions will be largely eliminated within 30 years. Ship NOx Freight performance Trend: decreasing As with locomotives, EPA is developing new standards for ocean-going ships, which it estimates will by 2030 reduce NO x emission rates by 80 percent and PM emis- sion rates by 85 percent, compared to the current limits applicable to these engines. EPA has finalized more strin- gent standards for marine transport.39 EPA estimates that by 2030 the management of the program according to the revised standards will reduce annual emissions of NO x by about 1.2 million tons and PM emissions by about 143,000 tons. 40 Rail Emission Forecasts 0 0.2 0.4 0.6 0.8 1 1.2 20 06 20 09 20 12 20 15 20 18 20 21 20 24 20 27 20 30 20 33 20 36 20 39 E m is si on F ac to rs /G al lo n VOC PM NOX Figure A.30. Rail emissions. Freight Performance Trend: Decreasing EPA standards adopted in 2008 are forecast to lead to a near 90 percent reduction in railroad locomotive emissions for the three primary pollutants, VOCs, NOx, and particulates.35 The standards will rely on a new generation of locomotive engine technology, with intermediate engine technology required for remanufactured locomotives. EPA anticipates that these engines may account for an even greater share of overall emissions over the next few decades as other emission control programs take effect for cars and trucks and other nonroad emissions sources. Estimates show that, without the emission reductions, by 2030 locomotive and marine diesel engines would contribute more than 65 percent of national mobile-source diesel PM2.5, or fine particulate, emissions and 35 percent of national mobile-source NOx emissions, a key precursor to ozone and secondary PM formation. According to the EPA 2005 National Emissions Inventory, rail equipment primarily engaged in freight transportation emitted 1,118,786 tons of NOx.36 The forecast trend, as demonstrated in Figure A.30 above,37 is that these emissions will be largely eliminated within thirty years. Comment [CE7]: Author: Is it okay to change figure title to “Forecast rail VOC and NOx emissions.” Figure A.30. Forecast rail VOC and NOx emissions.

90 Freight Safety Measures Following are the Safety performance measure summaries. Truck Injury and Fatal Crash Rates Freight performance Trend: positive The performance trends for large-truck injury and fatal crash rates show significant improvement (Figures A.31 and A.32). Large trucks are defined as vehicles with a gross vehicle weight rating (GVWR) greater than 10,000 pounds. Crash rates are the number of crashes per 100 million vehicle miles of travel (VMT). Between 1988 and 2007, the large-truck injury crash rate decreased from 67.9 to 31.8.40 The 2007 rate is the lowest on record. The large-truck fatal crash rate has also declined. In 2007, this rate was 1.85, down from a peak of 5.21 in 1979.41 Future Trend line: positive Preliminary figures indicate that the number of large trucks involved in both injury and fatal crashes again declined in 2008.42 FMCSA cautions, however, that these numbers may understate the actual number of large-truck crashes. 44 The data quality for large-truck injury crash rates is rated as inadequate, primarily because of FMCSA’s determination that not all states report every “FMCSA-eligible” crash to the Motor Carrier Management Information System (MCMIS) Crash File. FMCSA-eligible crashes are defined as those that meet FMCSA’s SAFETYNET definition of a reportable accident.43 FMCSA has acknowledged the deficiency of data contained in MCMIS and is working with several states to improve data collection and reporting. Large Truck Injury Crash Rate 0 10 20 30 40 50 60 70 80 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 Year In ju ry C ra sh es p er 1 00 M T ru ck V M T Figure A.31. Truck injury crash rate. More reliable truck fatal crash statistics are collected by National Highway Traffic Safety Administration (NHTSA) and entered into the Fatal Accident Reporting System (FARS). FARS is widely recognized as the most reliable source of fatal truck crash data.44 Truck VMT estimates can also affect the accuracy of vehicle crash rates. These estimates are reported by the states to FHWA and published annually in FHWA’s Highway Stats. Since 1999, national truck VMT estimates generally show moderate annual growth. Conversely, some state-specific VMT estimates can fluctuate significantly from year to year. Additionally, because of the data collection methods used for determining VMT, some have questioned the accuracy of available VMT data sources. The granularity of data used as inputs to crash-rate calculations is rated as adequate. These metrics are available at the state and national levels. 43 Truck Injury and Fatal Crash Rates Large Tru k Fatal Crash Rate 0 1 2 3 4 5 6 19 75 19 76 19 77 19 78 19 79 19 80 19 81 19 82 19 83 19 84 19 85 19 86 19 87 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 Year Fa ta l C ra sh es p er 1 00 M T ru ck V M T Figure A.32. Truck fatal crash rates. Freight Performance Trend: Positive The performance trends for large-truck injury and fatal crash rates show significant improvement (Figures A.31 and A.32). Large trucks are defined as vehicles with a gross vehicle weight rating (GVWR) greater than 10,000 pounds. Crash rates are the number of crashes per 100 million vehicle miles of travel (VMT). Between 1988 and 2007, the large-truck injury crash rate decreased from 67.9 to 31.8.40 The 2007 rate is the lowest on record. The large-truck fatal crash rate has also declined. In 2007, this rate was 1.85, down from a peak of 5.21 in 1979.41 Future Trend Line: Positive Preliminary figures indicate that the number of large trucks involved in both injury and fatal crashes again declined in 2008.42 FMCSA cautions, however, that these numbers may understate the actual number of large-truck crashes. Declines in large-truck crash rates may be attributed to several factors, including targeted enforcement of less safe motor carriers and high-risk truck drivers, national driver training/credentialing initiatives, increased use of onboard safety systems in large trucks, improvements in truck and car safety designs, and public outreach efforts to educate all roadway users on highway safety issues. Figure A.31. Truck injury crash rate. Figure A.32. Truck fatal crash rates.

91 Figure A.31. Truck injury crash rate. Figure A.32. Truck fatal crash rates. Declines in large-truck crash rates may be attributed to several factors, including targeted enforcement of less safe motor carriers and high-risk truck drivers, national driver training/credentialing initiatives, increased use of onboard safety systems in large trucks, improvements in truck and car safety designs, and public outreach efforts to educate all road- way users on highway safety issues. The data quality for large-truck injury crash rates is rated as inadequate, primarily because of FMCSA’s determination that not all states report every “FMCSA-eligible” crash to the Motor Carrier Management Information System (MCMIS) Crash File. FMCSA-eligible crashes are defined as those that meet FMCSA’s SAFETYNET definition of a reportable accident.43 FMCSA has acknowledged the deficiency of data contained in MCMIS and is working with several states to improve data collection and reporting. More reliable truck fatal crash statistics are collected by the National Highway Traffic Safety Administration (NHTSA) and entered into the Fatal Accident Reporting System (FARS). FARS is widely recognized as the most reliable source of fatal truck crash data.44 Truck VMT estimates can also affect the accuracy of vehi- cle crash rates. These estimates are reported by the states to FHWA and published annually in FHWA’s Highway Stats. Since 1999, national truck VMT estimates generally show moderate annual growth. Conversely, some state-specific VMT estimates can fluctuate significantly from year to year. Additionally, because of the data collection methods used for determining VMT, some have questioned the accuracy of available VMT data sources. The granularity of data used as inputs to crash-rate calcu- lations is rated as adequate. These metrics are available at the state and national levels. Highway–Rail At-Grade Incidents A highway–rail at-grade crash is any impact between a rail user and a highway user at a crossing site, regardless of sever- ity. This includes motor vehicles and other highway, roadway, and sidewalk users at both public and private crossings. The overall performance trend for highway–rail at-grade crashes in the United States has improved since 1998, most notice- ably from 2000 to 2003 and from 2006 to 2008.45 The frequency of these incidents declined significantly in 2008 (see Figure A.33). Although the number of these incidents has decreased, FRA has named as a top research strategy the modernizing of grade crossings and the evaluation of public education and aware- ness strategies to reduce incidents on railroad rights-of-way.46 Railroads operating in the United States are required to submit monthly accident reports to FRA. This report must include any collision between an on-track piece of equipment and any user of a public or private crossing.47 Data quality is further bolstered by the required use of a standardized form for reporting these types of incidents. In addition, FRA provides an online tool for railroads and states to compare and reconcile crossing location inventories with the USDOT National Crossing Inventory File.48 Crash data updates are published monthly. An FRA web- site allows users to query the incident database with a wide range of filters, including railroad, state, county, public and/ or private crossings, and start/end date. 45 Highway–Rail At-Grade Incidents Highway-Rail Incidents at Public and Private Crossings 1998-2008 0 500 1000 1500 2000 2500 3000 3500 4000 Year N um be r of In ci de nt s Accidents 3508 3489 3502 3237 3077 2977 3080 3060 2939 2767 2398 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Source: Federal Railroad Administration, Office of Safety Analysis Figure A.33. RR crossing incidents. A highway–rail at-grade crash is any impact between a rail user and a highway user at a crossing site, regardless of severity. This includes motor vehicles and other highway, roadway, and sidewalk users at both public and private crossings. The overall performance trend for highway–rail at-grade crashes in the United States has improved since 1998, most noticeably from 2000 to 2003 and from 2006 to 2008.45 The frequency of these incidents declined significantly in 2008 (see Figure A.33). Although the number of these incidents has decreased, FRA has named as a top research strategy the modernizing of grade crossings and the evaluation of public education and awareness strategies to reduce incidents on railroad rights-of-way.46 Railroads operating in the United States are required to submit monthly accident reports to FRA. This report must include any collision between an on-track piece of equipment and any user of a public or private crossing.47 Data quality is further bolstered by the required use of a standardized form for reporting these types of incidents. In addition, FRA provides an online tool for railroads and states to compare and reconcile crossing location inventories with the USDOT National Crossing Inventory File.48 Figure A.33. RR crossing incidents.

92 Freight Investment Measures These are the summaries for the Investment measures. Investment to Sustain NHS Freight performance Trend: increasing The key indicator of the highway system’s future condition is the ratio of the total estimated national investment in the NHS over the next 10 years to the amount necessary to sus- tain current performance. The source of the national investment amounts in NHS is the Conditions and Performance Report that FHWA prepares and submits biannually to the Congress. The currently avail- able analysis uses the 200449 and 200650 Status of the Nation’s Highways, Bridges, and Transit: Conditions and Performance Report. Both reports contain data from two years before the report date: The 2004 report uses 2002 data, and the 2006 report uses 2004 data. Highway rehabilitation and system expansion investments are modeled by the Highway Eco- nomic Requirements System (HERS), whereas the National Bridge Investment Analysis System (NBIAS) model analyzes rehabilitation and replacement investment for all bridges, including those on the NHS. Approximately $12.3 billion was spent on NHS rural arte- rials and collectors in 2004, and another $22.3 billion on NHS urban arterials and collectors. Reported state govern- ment spending on NHS routes functionally classified as rural local or urban local was negligible in the year 2004. It is not currently possible to identify spending by local governments on these routes, which would mainly consist of intermodal connectors and Strategic Highway Network (STRAHNET) Connectors. STRAHNET is a national set of roadways that provide access to defense facilities. Of the total $34.6 billion spent by all levels of government for the capital improve- ments to the NHS in 2004, approximately 45.0 percent was used on the interstate component of the NHS. Average delay and average travel time costs on rural NHS routes would be maintained at an average annual investment level of $5.4 billion, while $22.2 billion would be required to maintain the same performance on urban NHS routes. The biannual report is theoretically available every two years, but in some cases three years have passed between reports. The underlying data are at the local level. FHWA has versions of both systems that can be used by agencies to gen- erate forecasts for varying geographic regions. Future Trend line: uncertain As can be seen from the last line of Table A.8, the ratio of total funds expended to sustain the NHS was positive in both 2002 and 2004 overall. However, in 2002, only 95 per- cent of what was necessary to sustain urban NHS conditions was spent and in 2004 the ratio of what was spent compared to what was required was exactly 1.0. As noted above, these figures for 2004 were calculated in 2006. Since 2004, highway construction cost inflation has risen significantly, in many regions by more than 50 percent between late 2004 and early 2009. Declining oil prices caused by the recession have mod- erated construction price increases, but they remain substan- tially above 2004 levels. Therefore, it is uncertain, given the severely constrained purchasing power of the past several years (2008–2010), whether recent expenditures on the NHS have been sufficient to sustain both condition and perfor- mance of the system. Rail Industry Cost of Capital performance Trend: improving but incomplete Earning more than the cost of capital is a basic measure of financial health in any industry. The Surface Transporta- tion Board calculates annually the cost of capital for the U.S. 1 Table A.8. NHS investment level adequacy. 2002 2004 Trend (annual percentage) Rural Urban Total Rural Urban Total Rural Urban Total Total NHS Investment ($B) $14.9 $20.4 $35.3 $12.3 $22.3 $34.6 -9.1% 4.6% -1.0% Average Investment Needed to Maintain Average Delay and Travel Time Costs $7.0 $21.5 $28.5 $5.4 $22.2 $27.6 -12.2 1.6% -1.6% Ratio of Total vs. Average Needed 2.13 .95 1.24 2.28 1.0 1.25 3.4% 2.9% .6% table A.8. NHS invest t l l adequacy.

93 Class I railroads. For 2008, STB estimated that the railroad’s cost of capital was 11.75 percent51 (see Figure A.34). The rail- roads are a very capital-intensive industry because of their need for tracks, locomotives, train cars, and related equip- ment and facilities. If they do not earn more than their cost of capital, it is an indicator that investments in rail capital are economically inefficient and that other investments would earn a higher economic return. Despite significant gains in productivity and profitability since the 1980s Staggers Act deregulation, the Class I railroads still struggle to earn their cost of capital; railroads earn only about 8 percent on net capital, according to the FRA.52 This is a modest rate of return compared to some other industries. For decades, American railroads earned the lowest rates of return of any major U.S. industry. Between 1960 and 1979 the average annual return on shareholder equity was 2.3 per- cent.53 U.S. railroads have estimated that up to 40 percent of their revenues are devoted to capital assets, a percentage that is significantly higher than most industries. The high cost of maintenance for track, rolling stock, and yards requires sub- stantial capital investments, which are not liquid or mobile. Investing in capital represents a significant long-term invest- ment for a railroad. If national policy develops that seeks to expand railroad capacity so that rail absorbs a larger percentage of national freight traffic, the cost-of-capital calculation can be an impor- tant metric to assess the industry’s ability to finance its capital expansion. This metric may be defined as the required return necessary to make the capital budgeting projects worthwhile in the rail freight industry. Cost of capital is the weighted average computed using proportions of debt and equity as determined by their market values and current market rates.54 STB annually determines the cost of capital (with input from AAR) and uses it in evaluating the adequacy of indi- vidual railroads’ revenues each year. The figure is also used in maximum rate cases, feeder-line applications, rail-line aban- donments, trackage rights cases, rail-merger reviews, and more generally in the STB’s Uniform Rail Costing System. The railroad cost of capital determined here is an aggregate measure. It is not intended to measure the desirability of any individual capital investment project. Although the cost of debt is observable and readily avail- able, the cost of equity (the expected return that equity inves- tors require) can only be estimated. How best to calculate the cost of equity is the subject of a vast amount of literature covering the fields of finance, economics, and regulation. In each case, however, because the cost of equity cannot be directly observed, estimating the cost of equity requires adopting a financial model and making a variety of simplify- ing assumptions. As noted above, the 2008 composite after-tax cost of capital for the railroad industry was 11.75 percent. The procedure used to develop the composite cost of capital is consistent with the Statement of Principle established by the Railroad Accounting Principles Board: “Cost of capital shall be a weighted average computed using proportions of debt and equity as determined by their market values and current mar- ket rates.” The 2008 cost of capital was 0.42 percentage points higher than the 2007 cost of capital (11.33%).55 Although the methodology has been recently updated and the cost of debt is observable, the abundance of litera- ture regarding variations in calculating the cost of equity is a source of potential concern. The analysis is conducted each year to support existing financial decision-making processes. It is unlikely that the frequency of this analysis will decline. The analysis is conducted only at the national level. It may be impossible to identify and quantify significant variations for specific localities. Figure A.34. Rail cost of capital. 50 Rail Industry Cost of Capital Figure A.34. Rail cost of capital. Performance Trend: Improving but Incomplete Earning more than the cost of capital is a basic measure of financial health in any industry. The Surface Transportation Board calculates annually the cost of capital for the U.S. Class I railroads. For 2008, STB estimated that the railroad’s cost of capital was 11.75 percent51 (see Figure A.34). The railroads are a very capital-intensive industry because of their need for tracks, locomotives, train cars, and related equipment and facilities. If they do not earn more than their cost of capital, it is an indicator that investments in rail capital are economically inefficient and that other investments would earn a higher economic return. Despite significant gains in productivity and profitability since the 1980s Staggers Act deregulation, the Class I railroads still struggle to earn their cost of capital; railroads earn only about 7 percent on net capital, according to the FRA.52 This is a modest rate of return compared to some other industries. For decades, American railroads earned the lowest rates of return of any major U.S. industry. Between 1960 and 1979 the average annual return on shareholder equity was 2.3 percent.53 U.S. railroads have estimated that up to 40 percent of their revenues are devoted to capital assets, a percentage that is significantly higher than most industries. The high cost of maintenance for track, rolling stock, and yards requires substantial capital investments, which are not liquid or mobile. Investing in capital represents a significant long-term investment for a railroad. If national policy develops that seeks to expand railroad capacity so that rail absorbs a larger percentage of national freight traffic, the cost-of-capital calculation can be an important metric to assess the industry’s ability to finance its capital expansion. This metric may be defined as the required return necessary to make the capital budgeting projects worthwhile in the rail freight industry. Cost of capital is the weighted average computed using proportions of debt and equity as determined by their market values and current market rates.54 STB annually determines the cost of capital (with input from AAR) and uses it in evaluating the adequacy of individual railroads’ revenues each year. The figure is also used in maximum rate cases, feeder-line Deleted: The Rail Freight Industry Earning Cost of Capital Comment [JP11]: Author: Please review these changes. The definition device doesn’t work, but I want to be sure I’m saying what you mean.

94 Estimated Capital to Sustain Rail Market Share Freight performance Trend: increasing The estimated rail capital investment to sustain market share has not traditionally been a publicly calculated value. However, it represents an estimate based on a definitive study of what level of investment is needed for Class I railroads to sustain their current market share in the face of rising freight volumes. The National Surface Transportation Policy and Revenue Study Commission, charged by Congress to develop a plan of improvements to the nation’s surface transportation systems to meet the needs of the twenty-first century, requested that AAR commission a study56 to estimate the system’s long-term capacity needs. The study, released in 2007, identified that the investment will have to increase over the study’s planning horizon of 28 years if Class I railroads are to keep up with expected freight demand. The total investment required is $148 bil- lion, or a straight-line average investment of $5.3 billion per year. The study also identified that this amount was increasing as time passed without higher investment levels. The AAR reported that between 2005 and 2007 Class I rail- roads invested an average of $1.5 billion annually for expan- sion, leaving an annual investment gap of $3.3 billion. The railroads estimated that through increased revenues and productivity, they could generate $3.4 billion annually of the $4.8 billion needed to invest in capacity. That leaves an “investment gap” of $1.4 billion annually to be funded from railroad investment tax incentives, public-private partner- ships, or other sources. Tracking the investment gap would provide an ongoing metric of the sufficiency of investment in the nation’s rail network. The network used in the methodology is corridor based, with corridors being specified by the Class I railroads par- ticipating in the study. The beginnings and ends of the cor- ridors are major urban areas corresponding with the USDOT Freight Analysis Framework Version 2.2 (FAF2.2) zones, major rail traffic generators such as the Powder River Basin coal fields, port complexes, and major rail traffic junctions. The number of trains on the network is based on the 2005 Surface Transportation Board Carload Waybill Sample. The 2007 report was the first of its type. It is unclear if the AAR intends on replicating the study calculations on a peri- odic basis. However, the framework proposes investigating if the AAR would assist in reporting the annual investment gap. Investment to Sustain Inland Waterway System The inland waterway system comprises 12,000 navigable miles connecting 41 states, including all states east of the Mississippi River. On this system are 230 locks, which had an average age in 2007 of 56.7 years.57 Many were built in the 1930s with an expected design life of 50 years. Seven of the locks were built in the 1800s, and the oldest operating lock is from 1839. USACE reports that locks were available 92 per- cent of the time in calendar 200758 and that lock downtime created 157,430 hours of delay. Although inland water volumes have been relatively sta- ble in the past decade overall, the inland system is impor- tant to many bulk commodities. The marine transportation and inland waterway system moves an estimated 60 percent of grain exports and an estimated 95 percent of soybean exports.59 It also is disproportionately important for ore, chemicals, and mining products. The suggested performance metric for the inland water- way system is proposed to be the average age of the locks. Based upon current levels of investment in lock replacement, the average age of the inland locks has increased annually, with no reduction in average age in many years. Although age alone may not be an indicator of lock performance, it is proposed as the initial metric for the performance of the inland waterway system. The data quality of this measure is high because of the fixed and static nature of the waterway system. The data also are highly granular, because age and performance data are available for every lock. That would allow any region to track the age of the waterway system in its area Endnotes 1 American Trucking Associations and IHS Global Insight. U.S. Freight Trans- portation Forecast to…2020. Arlington, VA. 2 FHA. Freight Facts and Figures: 2008. 3 2008 FAF Provisional Database and FAF2 Forecast. 4 USACE. Table 1-1, in Total Waterborne Commerce of the U.S. 1966–2005, 2007, p. Totals 1-3. 5 2008 FAF Provisional Database and FAF2 Forecast. 6 20-ft equivalent units. 7 http://www.bts.gov/press_releases/2009/dot084_09_01/html/dot084_09. html. 8 http://www.iwr.usace.army.mil/ndc/wcsc/by_portname07.htm. 9 Paul Bingham (Global Insight, Inc.). The Importance of Ports for Trade and the Economy, Torrance, CA, November 15, 2007. http://www.futureports. org/events/bingham_globalinsight_111507.pdf. 10 FHWA. Freight Facts and Figures: 2008. 11 http://www.railroadpm.org/. 12 USDOT, BTS. Table 1-46b, National Transportation Statistics 2009. http:// www.bts.gov/publications/national_transportation_statistics/html/ table_01_46b.html. 13 CSCMP. Annual State of the Logistics Report, 2010. 14 FHWA. Status of the Nation’s Highways, Bridges, and Transit: Conditions and Performance, 2006. http://www.fhwa.dot.gov/policy/2006cpr/index.htm.

95 15 http://www.epa.gov/oms/regs/nonroad/420f08004.htm. 16 FHWA. Assessing the Effects of Freight Movement on Air Quality at the Natio- nal and Regional Level—Final Report, prepared by ICF Consulting, Fairfax, VA, April 2005. 17 FHWA. Chapter 2, Assessing the Effects of Freight Movement on Air Quality at the National and Regional Level – Final Report, prepared by ICF Consulting, Fairfax, VA, April 2005. http://www.fhwa.dot.gov/environment/freightaq/. 18 EPA. National Emissions Inventory Data and Documentation, 2005. http:// www.epa.gov/ttn/chief/net/2005inventory.html#inventorydata (accessed September 25, 2009). 19 Figure provided by the American Trucking Associations. 20 FHWA. Chapter 2, Assessing the Effects of Freight Movement on Air Quality at the National and Regional Level – Final Report, prepared by ICF Consulting, Fairfax, VA, April 2005. http://www.fhwa.dot.gov/environment/freightaq/. 21 FHWA. Chapter 2, Assessing the Effects of Freight Movement on Air Quality at the National and Regional Level – Final Report, prepared by ICF Consulting, Fairfax, VA, April 2005. http://www.fhwa.dot.gov/environment/freightaq/. 22 FHWA. Chapter 2, Assessing the Effects of Freight Movement on Air Quality at the National and Regional Level – Final Report, prepared by ICF Consulting, Fairfax, VA, April 2005. http://www.fhwa.dot.gov/environment/freightaq/. 23 EPA. Energy (chapter 3), in U.S. Greenhouse Gas Inventory Report, 2009. http://www.epa.gov/climatechange/emissions/usinventoryreport.html. 24 FHWA. Highway Stats, 2007. 25 EPA. Energy (chapter 3), in U.S. Greenhouse Gas Inventory Report, 2009. http://www.epa.gov/climatechange/emissions/usinventoryreport.html. 26 One teragram is equal to one million metric tons. 27 EPA. Table ES-2, Executive Summary, in U.S. Greenhouse Gas Inventory Report, 2009. http://www.epa.gov/climatechange/emissions/usinventoryre- port.html. 28 Energy Information Administration. Table 19, Energy-Related Carbon Di- oxide Emissions by End Use. In Report: An Updated Annual Energy Outlook 2009 Reference Case Reflecting Provisions of the American Recovery and Rein- vestment Act and Recent Changes in the Economic Outlook. http://www.eia. doe.gov/oiaf/servicerpt/stimulus/excel/aeostimtab_19.xls (accessed Novem- ber 13, 2009). 29 FHWA. Highway Stats, 2007. 30 Fuel consumption data are provided by the U.S. Department of Energy, En- ergy Information Administration 31 US EPA. Draft Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2009, 2011 Table 2-15. 32 US EPA. Draft Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2009, 2011 Table 2-15. 33 EPA. Inventory of U.S. Greenhouse Gas Emissions and Sinks, Report EPA 430- R-09-004, April 15, 2009. 34 EPA, Report EPA 430-R-09-004. 35 http://www.epa.gov/oms/regs/nonroad/420f08004.htm. 36 http://www.epa.gov/ttn/chief/net/2005inventory.html#inventorydata. 37 http://www.epa.gov/oms/locomotives.htm. 38 http://www.epa.gov/otaq/regs/nonroad/marine/ci/420f09068.htm. 39 http://www.epa.gov/oms/regs/nonroad/420f08004.pdf. 40 FMCSA. Large Truck and Bus Crash Facts 2007. January 2009. 41 FMCSA. Large Truck and Bus Crash Facts 2007. 42 FMCSA. Crash Statistics, National Summary Report, http://ai.volpe.dot.gov/ CrashProfile/n_overview.asp (accessed November 9, 2009). 43 For example, large-truck crashes that result in a towed vehicle, an injury, or a fatality. 44 FMCSA. Large Truck and Bus Crash Facts 2007. January 2009. 45 FRA, Office of Safety Analysis. Table 5.11, Highway/Rail Incidents Summary Tables, http://safetydata.fra.dot.gov/officeofsafety/publicsite/Query/gxrtab. aspx (accessed October 30, 2009). These statistics were generated from a query of a FRA database at this site. 46 FRA. Research Needs Workshop, July 14–16, 2009, http://www.fra.dot.gov/ us/content/1735 (accessed November 13, 2009). 47 FRA, Office of Safety. FRA Guide for Preparing Accident/Incident Reports. Effective May 2003. 48 FRA, Office of Safety. Crossing Inventory Data File Reconciliation (CIR), http://safetydata.fra.dot.gov/CIR/Default.aspx (accessed November 1, 2009). 49 http://www.fhwa.dot.gov/policy/2004cpr/pdfs.htm. 50 http://www.fhwa.dot.gov/policy/2006cpr/pdfs/cp2006.pdf. 51 Surface Transportation Board Decision STB Ex Parte No. 558 (Sub-No. 12) Railroad Cost of Capital — 2008 Decided: September 24, 2009. 52 FRA. “Freight Railroads Background”, a briefing paper on America’s freight railroads accessed Feb. 15, 2010, http://www.fra.dot.gov/downloads/policy/ freight2008data.pdf, p.4. 53 Stover, John F. American Railroads, 2nd ed. University of Chicago Press, Chicago, Ill, 1997, p. 153. 54 Railroad Accounting Principles Board. Final Report, vol. 1 (1987). 55 STB Ex Parte No. 558 (Sub-No.12), http://www.stb.dot.gov/Decisions/ ReadingRoom.nsf/UNID/524C43CE35BD8E2F8525763C00428C4A/ $file/40078.pdf. 56 Cambridge Systematics, Inc. National Freight Rail Infrastructure Capacity and Investment Study (prepared for the Association of American Railroads), Cambridge, MA, 2007. 57 USACE. The U.S. Waterway System—Transportation Facts, Nov. 2008, p. 3. 58 USACE, U.S. Waterway System, p. 3. 59 USACE, Challenge: Marine Transportation System, 2000, US Army Corps of Engineers briefing paper to accompany public outreach sessions related to the future of the Marine Transportation System.

Next: Appendix B - Statewide and Metropolitan Freight Performance Metrics Examples »
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TRB’s National Cooperative Freight Research Program (NCFRP) Report 10: Performance Measures for Freight Transportation explores a set of measures to gauge the performance of the freight transportation system.

The measures are presented in the form of a freight system report card, which reports information in three formats, each increasingly detailed, to serve the needs of a wide variety of users from decision makers at all levels to anyone interested in assessing the performance of the nation’s freight transportation system.

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