Identification and Evaluation of Freight Demand Factors (2012) / Chapter Skim
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From page 62... ...
Appendix A - Peer Exchange Synthesis The following pages present a synthesis of feedback on an earlier version of the work presented in this report from a Peer Exchange held May 18, 2010, at the Beckman Center in Irvine, CA. Participants invited to the Peer Exchange represented potential users of the project findings, including private-sector supply chain companies and trade associations (rail, trucking, and others)
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From page 63... ...
o Many of the factors used in our model are also used by the private sector (e.g., railroads, trucking industry) in forecasting demand.
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From page 64... ...
o There was agreement that variability in data is increasing sharply, with recent examples (past ~18 months) from all sectors (e.g., fuel price, trans-border freight flows)
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o "Peak oil" effects on fuel cost. o Government/regulatory policy with regard to climate change (e.g., in California)
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List of Peer Exchange Attendees (excluding Project Team members) Name Affiliation Beningo, Steve BTS/Research and Innovative Technology Admin.
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From page 67... ...
Literature Review to Investigate Factors Affecting Transportation Demand The Research Team used a "case" approach to review and analyze a variety of recen t and relevant studies on what factor affect the demand for transportation. The literature analysis was developed in two steps.
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From page 68... ...
Economic Indices for the Transportation Sector Kajal Lahiri &Vincent Wenxiong Yao, Transportation Research Part A 40 (2006) 872-887 Indicators are developed for the transportation services sector to identify its current state and predict its future.
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From page 69... ...
Nationwide Freight Generation Models: A Spatial Regression Approach David C Novak, Christopher Hodgdon, Feng Guo, and Lisa Aultman-Hall Networks and Spatial Economics This paper investigates the application of linear regression models and modeling techniques in predicting freight generation at the national level within the U.S.
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From page 70... ...
B-4 Forecasting freight demand using economic indices Jonathon T
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From page 71... ...
Manufacturing sales of nondurable goods Manufacturing finished goods inventories Manufactures' total unfilled orders Manufacturer's' unfilled orders of nondurable goods Manufacturers' new order of nondurable goods Total retail store sales Retail store sales of nondurable goods Retail store sales of furniture stores Retail store sales of apparel and accessory stores Retail store sales of general merchandise stores Retail inventories for durable goods stores Retail inventories for apparel stores Retail inventories for food stores Retail inventories for general merchandise stores Total wholesale inventories Wholesale inventories of nondurable goods stores Producer commods. price index of all food Producer commods.
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From page 72... ...
Truck Volume Estimation via Linear Regression under Limited Data Maria Boilé and Michail Golias The authors utilize a series of linear regression algorithms to ‘train models' when training data is limited. Four algorithms are developed, including Ridge Regression (RR)
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From page 73... ...
Forecasting Truck VMT Growth at the County and Statewide Levels Feng Liu and Robert G Kaiser In this article, the authors develop statistical models to forecast truck VMT growth of four facility categories at the county and statewide levels.
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Future Freight Transportation Demand (National Urban Freight Conference 2006) Paul Bingham The presentation focuses on underlying factors that drive freight demand.
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Monthly Output Index for the U.S. Transportation Sector Kajal Lahiri, Herman Stekler, Wenxiong Yao, and Peg Young In this article, the authors develop a monthly output index for the U.S.
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From page 76... ...
Final Report on Contract Number NCTIP97-21: Development of a Freight Forecasting Model to Forecast Truck Flow Between NJ Counties Themselves and Between NJ Counties and Other States Kenneth Lawrence and Gary Kleinman The aims of the project are to develop a model with the capability of predicting commodity flow information via trucking. In the context of New Jersey, the authors develop a series of database tools to allow for decision makers to easily view the available data.
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From page 77... ...
Title: A Survey of the Freight Transportation Demand Literature and a Comparison of Elasticity Estimates Chris Clark, Helen Tammela Naughton, Bruce Proulx, Paul Thoma Prepared for Institute for Water Resources, U.S. Army Corps of Engineers This study reviews various aggregate and disaggregate choice methodologies employed to estimate freight demand.
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Title: Commodity Flow Modeling William R Black Transportation Research Circular (1999)
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From page 79... ...
Container Demand in North American Markets: A Spatial Autocorrelation Analysis Wilson, William W and Camilo Sarmiento.
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Freight Travel Demand Modeling – Synthesi s of Approaches and Development of a Framework, Pendyala, Ram, V Shankar and R
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Container Demand in North American Markets: A Spatial Autocorrelation Analysis Wilson, William W and Camilo Sarmiento.
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1. The Freight Story: A National Perspective on Enhancing Freight Transportation, Federal Highway Administration, 2008 Examines the nature of freight movem ent, identifies challenges to improving freight productivity and security, and presents st rategies to increase freight productivity.
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From page 84... ...
efforts. As with othe r reports, there is the general sense that freight dem and modeling is often not a priority and too of ten influenced by inadequate tools originally designed for passenger demand modeling.
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From page 85... ...
1) From subsection "Current Freight Models and Their Use of Data" - National Road Traffic Forecasts (NRTF)
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From page 86... ...
- There is a relationship between GDP and freight movement, but the increasing importance of sustainability could result in a degree of uncoupling - Increased international trade – materials/products are more likely to be transported over greater distances - Changes in supply chain structures/ supply chain integration – longer distance freight transport movements/increase in volumes on key routes/reliance on a smaller number of supply chain partners - Reductions in lead time/Just in Time – usually more frequent deliveries of smaller quantities, but there have been some JIT strategies that effectively consolidate flows/minimize transport intensity - Developments in e-commerce – changes patterns, but impact on freight demand not entirely clear - The increasing role of logistics service providers *
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constrained to accept methodologies due to regulatory requirements (federal transport planning and air quality regulations)
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From page 88... ...
Freight Demand Factors Cited: Historical Demand /Freight Flow Patterns – Trends from past years can be projected in the future for reasonable estimates, but the impact of other external factors should also always be considered. In practice, the historical/current demand is often used as a starting point for calculating future demand.
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From page 89... ...
- International Transportation Agreements - Intermodal Operating Agreements - Single-Source Delivery of International LTL Shipments - Carrier-Shipper Alliances - Fuel Prices - Publicly Provided Infrastructure - User Charges - Other Taxes - Government Subsidization of Carriers - Environmental Policies and Restrictions – emissions regulations, phase-out of single hull tankers, fuel quality requirements, air noise restrictions - Safety Policies and Restrictions – speed limits, route restrictions for hazmat - Effects of changes in Truck Size and Weight Limits - Congestion - Technological Advances *
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From page 90... ...
the usual EOQ model inputs - Disaggregate-utility maximization: level of service and commodities attributes - Spatio-temporal interaction: spatial and temporal freight flows, sociodemographic data - Urban Freight Scope - Gravity: total productions and attractions in each zone, impedance (usually distance or travel time) between zones - Input Output: technical coefficients, survey and non survey based data.
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From page 92... ...
This survey report investigates the pe rception gap am ong freight transportation stakeholders in order to discover the root causes of congestion and the capacity crisis, the resulting effects on business and the solutions that are or should be utilized to assuage these impacts. Following a discussion of the findings, recommendations for short and long term strategies to enhance communication among stakeholders are offered.
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From page 93... ...
New Hours of Service agreem ent (HOS) not a major factor for carriers & govt.
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From page 94... ...
Key investments: • Improving existing highways • Building new highways as (Govt ranks neither of these in the top 5) • airport or seaport investment not considered that important.
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From page 95... ...
alleviating these. Any shortcomings in the transport infrastructure network will certainly impact freight operations and these factors should be considered when modeling demand.
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From page 97... ...
- Changes in the type of transport equi pment: longer truck tr ailers, doublestack containers and m assive ocean ves sels – this m ay not be a m ajor issue for commodity-based models, but vehicle-based demand models will be impacted - Port and Route diversification: logistic s professionals are finding ways to get around deficiencies in the US transport infrastructure - will reorient supply chain to minimize the impact of disruptions (even though it may not be the optimal use of infrastructure) This will shift demand from one part of the system to another, possibly without apparent cause - Rise of "value-added" warehousing: shifts part of the "m anufacture" of products to retail distribution centers - Increasing community awareness/action: opposition to freight movement through communities can im pact public transport policy, thus inf luencing the freight network and indirectly impacting freight flows 13.
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From page 99... ...
Relevance to NCFRP 11: Moderate relevance to NCFRP 11 since the paper focuses more on the m ode choice com ponent than dr ivers of freight dem and at the aggregate level. Freight Demand Factors Cited: Some of the variables us ed can be divided into commodity attributes (value and density of th e shipped cargo)
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Sources of Freight Data: US Bureau of Census, Commodity Transportation Survey Surface Transportation Boards' Railroad Waybill Sample US Bureau of Census, Annual survey of Manufacturers US Bureau of Census, VIUS Survey FAF State to State commodity flow database *
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econometric studies to identify key determ inants of demand across the cross section of countries. Relevance to NCFRP 11: Moderate relevance to NCFRP 11.
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From page 103... ...
Additionally a survey was conduc ted of a sa mple of firms in the UK and results of that survey produced what the main drivers of freight were a tightening of custom ers requirements driven by desire to cut th eir inventory levels and the second m ost important factor was the firms own desire to cut its inventory levels.
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Relevance to NCFRP 11: Direct relevance to NCFRP 11. Freight Demand Factors Cited: Direct Factors: i)
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vi) Central Warehousing: As transportation systems have become more efficient there has been a trend towards using fewer warehouses.
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v) Publicly Provided Infrastructure: Air, water, and tru ck carriers are all dependent on publicly provi ded infrastructure.
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xi) Technological Advances: The use of com puters and telecom munication equipment has had an important effect on the freight industry.
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(Note: This type of modeling is currently more common at the State-level and above (long-haul) , where relevant data is more likely to be available and disaggregated details less significant.)
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Network & Routing Factors 11) Network availability (road, railway and waterway)
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From page 111... ...
B-45 To be included in bibliography (not in lit review) Behavioral insights in to the mod eling of freight transportation and distribution systems Hensher and Figliozzi, Transportation Research, 2007 Assessing Rail Freight Solutions to Roadway Congestion: Final Report Bryan, Joseph , Glen Weisbrod, and Carl Martland.
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From page 112... ...
Table C-1 - Correlations of Candidate Demand Factors Absolute Correlation Matrix Rail Tons Rail Ton- Miles Rail Train- Miles Rail Car- Miles Rail Rev Ton-Miles Annual Truck TonMiles Truck VMT Water Tons Water TonMiles Real GDP 0.939 0.983 0.955 0.976 0.982 0.978 0.978 -0.010 -0.979 Real GDP per Capita 0.928 0.981 0.954 0.977 0.979 0.981 0.981 0.037 -0.971 Real Personal Consumption 0.938 0.977 0.943 0.966 0.976 0.969 0.969 -0.046 -0.980 Real Income Per Capita 0.925 0.971 0.934 0.961 0.969 0.971 0.971 -0.023 -0.975 Total Housing Starts 0.466 0.414 0.839 0.827 0.422 0.428 0.428 -0.307 -0.481 Industrial Production Index 0.950 0.988 0.965 0.984 0.983 0.977 0.977 0.014 -0.980 Industrial Manufacturing Index 0.951 0.987 0.966 0.985 0.983 0.975 0.975 0.003 -0.981 Purchasing Managers' Index 0.166 0.199 0.321 0.260 0.219 0.252 0.252 0.091 -0.137 Trade Wt. Broad Cur.
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C‐2 TABLE C-2 Filtered Correlations by Mode (Rail) in Actual, 1980 - 2007 Variables with which Correlations are > 0.75 Demand Measures (Dependent Variables)
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C‐3 TABLE C-3 Filtered Correlations by Mode (Truck) in Actual, 1980–2007 Variables with which Correlations are > 0.75 Demand Measures (Dependent Variables)
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C‐4 TABLE C-4 Rail Tonnage Sub-Sample Correlation Ranks (in actual) Rank Full Sample Rank ShortTerm Sample 1 Rank ShortTerm Sample 2 Rank ShortTerm Sample 3 Rail Tons (Correlation Ranks in Actual)
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C‐5 TABLE C-5 Rail Ton-Miles Sub-sample Correlation Ranks (in actual) Full Sample Short-Term Sample 1 Short-Term Sample 2 Short-Term Sample 3 Rail Ton-Miles (Correlation Ranks in Actual)
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From page 117... ...
C‐6 TABLE C-7 Rail Train-Miles Sub-Sample Correlation Ranks (in actual) Full Sample Short-Term Sample 1 Short-Term Sample 2 Rail Train Miles (Correlation Ranks in Actual)
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C‐7 TABLE C-8 Rail Car-Miles Sub-Sample Correlation Ranks (in actual) Full Sample Short-Term Sample 1 Short-Term Sample 2 Rail Car Miles (Correlation Ranks in Actual)
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From page 119... ...
C‐8 TABLE C-9 Truck Vehicle-Miles Sub-Sample Correlation Ranks (in actual) Full Sample Short-Term Sample 1 Short-Term Sample 2 Short-Term Sample 3 Truck VMT (Correlation Ranks in Actual)
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C‐9 TABLE C-10 Truck Ton-Miles Sub-Sample Correlation Ranks (in actual) Full Sample Short-Term Sample 1 Short-Term Sample 2 Short-Term Sample 3 Truck Ton Miles (Correlation Ranks in Actual)
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C‐10 TABLE C-11 Waterborne Tonnage Sub-Sample Correlation Ranks (in actual) Full Sample Short-Term Sample 1 Short-Term Sample 2 Short-Term Sample 3 Truck VMT (Correlation Ranks in Actual)
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C‐11 TABLE C-12 Waterborne Ton-Miles Sub-Sample Correlation Ranks (in actual) Full Sample Short-Term Sample 1 Short-Term Sample 2 Short-Term Sample 3 Truck Ton Miles (Correlation Ranks in Actual)
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TABLE D-1 Rail Ton Log-Actual Regressions Candidate Demand Factor Weights Model 1 2 3 4 5 6 7 Period of Estimation 1981-07 1981-07 1981-07 1981-07 1981-07 1981-07 1981-07 R - Squared (adjusted)
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From page 124... ...
TABLE D-2 Rail Ton Log-Actual Diagnostics Rail Ton Diagnostics Tests 1 2 3 4 5 6 7 Number of Observations 28 27 27 28 27 27 28 AIC (Information Criteria) -116.79 -104.26 -87.90 -91.10 -109.72 -100.32 -99.35 BIC (Information Criteria)
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From page 125... ...
TABLE D-3 Rail Ton–Miles Log-Actual Regressions Candidate Demand Factor Weights Model 1 2 3 4 5 Period of Estimation 1981-07 1981-07 1981-07 1981-07 1981-07 R - Squared (adjusted)
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From page 126... ...
TABLE D-4 Rail Ton-Miles Log-Actual Diagnostics Rail Ton Miles Log – Actual Diagnostics Model 1 2 3 4 5 Number of Observations 28 28 28 28 28 AIC (Information Criteria) -84.68 -118.38 -109.90 -97.42 -116.17 BIC (Information Criteria)
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From page 127... ...
TABLE D-5 Rail Train Miles -- Log Actual Regressions Candidate Demand Factor Weights Model 1 2 3 4 5 Period of Estimation 1990-07 1990-07 1990-07 1990-07 1990-07 R - Squared (adjusted)
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From page 128... ...
TABLE D-6 Rail Train-Miles Log-Actual Diagnostics Rail Train Miles Log – Actual Diagnostics Model 1 2 3 4 5 Number of Observations 18 18 18 18 18 AIC (Information Criteria) -82.35 -89.55 -88.07 -97.55 -85.47 BIC (Information Criteria)
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From page 129... ...
TABLE D-7 Rail Car Miles -- Log Actual Regressions Candidate Demand Factor Weights Model 1 2 3 4 5 Period of Estimation 1990-07 1990-07 1990-07 1981-07 1981-07 R - Squared (adjusted)
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From page 130... ...
TABLE D-8 Rail Car Miles -- Log Actual Diagnostics Rail Car Miles Log – Actual Diagnostics Model 1 2 3 4 5 Number of Observations 18 18 18 18 18 AIC (Information Criteria) -95.97 -80.18 -116.86 -103.49 -77.43 BIC (Information Criteria)
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From page 131... ...
TABLE D-9 Rail Revenue Ton–Miles (Annual) -- Log Actual Regressions Candidate Demand Factor Weights Model 1 2 3 4 5 6 Period of Estimation 1981-07 1981-07 1981-07 1981-07 1981-07 1981-07 R - Squared (adjusted)
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TABLE D-10 Rail Revenue Ton–Miles (Annual) -- Log Actual Diagnostics Rail Revenue Ton Miles Log - Actual*
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TABLE D-11 Truck Ton–Miles -- Log Actual Regressions Candidate Demand Factor Weights Model 1 2 3 4 Period of Estimation 1981-07 1981-07 1981-07 1981-07 R - Squared (adjusted)
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From page 134... ...
TABLE D-12 Truck Ton–Miles -- Log Actual Diagnostics Truck Ton Miles Log - Actual* Diagnostics Model 1 2 3 4 Number of Observations 28 28 28 28 AIC (Information Criteria)
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From page 135... ...
TABLE D-13 Truck Vehicle Miles -- Log Actual Regressions Candidate Demand Factor Weights Model 1 2 3 4 Period of Estimation 1981-07 1981-07 1981-07 1981-07 R - Squared (adjusted)
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TABLE D-14 Truck Vehicle Miles -- Log Actual Diagnostics Truck Vehicle Miles Travelled Log - Actual* Diagnostics Model 1 2 3 4 Number of Observations 28 28 28 28 AIC (Information Criteria)
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From page 137... ...
TABLE D-15 Water Tons -- Log Actual Regressions Candidate Demand Factors Model 1 2 3 Period of Estimation 19812007 19812007 19812007 R-Squared Adjusted 0.76 0.75 0.66 S.E. of Regression 0.02 0.02 0.02 Durbin Watson Statistic 1.75 1.91 1.72 Model Coefficients Constant 15.53 17.61 16.39 t-stat 23.24 26.89 21.42 Candidate Demand Factors Weights Total Capacity Utilization 0.86 0.82 0.88 t-stat 6.63 5.73 5.07 Grain and Coal Tons 0.09 t-stat 2.68 Grain Tons 0.03 t-stat 3.00 Real BLS Gas -0.10 -0.08 t-stat -3.01 -3.14 Lagged Inland Waterway Trust Fund Tax/Gallon -0.03 t-stat -1.03 Exogenous Impact Controls Rail Deregulation -0.01 t-stat -0.70 Statistical Error Corrections AR(1)
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TABLE D-16 Water Tons -- Log Actual Diagnostics Model Coefficients Number of Observations 28 28 28 AIC (Information Criteria) -148.56 -145.51 -139.02 BIC (Information Criteria)
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From page 139... ...
TABLE D-17 Water Ton Miles -- Log Actual Regressions Candidate Demand Factors Model 1 2 3 Period of Estimation 19812007 19812007 19812007 R-Squared Adjusted 0.95 0.87 0.96 S.E. of Regression 0.04 0.03 0.03 Durbin Watson Statistic 1.82 1.95 1.62 Model Coefficients Constant 23.23 26.13 24.65 t-stat 11.01 22.93 26.83 Candidate Demand Factors Weights Rail Ton-Miles -0.44 -0.65 -0.68 t-stat -4.13 -11.95 -26.29 Real BLS Gas -0.15 t-stat -2.79 Total Capacity Utilization 0.69 t-stat 3.88 Lagged Inland Waterway Trust Fund Tax/Gallon -0.13 t-stat -2.30 Purchasing Managers Index 0.25 t-stat 2.70 Exogenous Impact Controls Lagged NAFTA Impact 0.14 0.11 0.09 t-stat 4.54 8.25 3.59 Statistical Error Corrections AR(1)
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From page 140... ...
D‐18 TABLE D-18 Water Ton Miles -- Log Actual Diagnostics Model Coefficients Number of Observations 27.00 27.00 28.00 AIC (Information Criteria) -95.91 -103.48 -107.08 BIC (Information Criteria)
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From page 141... ...
TABLE E-1 Principal Component Analysis Groups Demand Factor Commodity Consumption Foreign Exchange Production Purchasing Manager Index & Capacity Utilization Group Employment 1 Urban Gas Price in Real $ Real Personal Consumption 2005 Chained $ Trade Weighted Foreign Exchange Index (Broad Trading Partners) Real GDP 2005 Chained $ Purchasing Managers Index Total Employment 2 Increase in Coal Producer Price Index Total Housing Starts Trade Weighted Foreign Exchange Index (Major Trading Partners)
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TABLE E-2 Principal Component Results for the Commodity Group Commodity Group Results Principal Components in Actual Number Value Difference Proportion Cumulative Value Cumulative Proportion 1 2.3521 1.8060 0.7840 2.3521 0.7840 2 0.5461 0.4444 0.1820 2.8983 0.9661 3 0.1017 -- - 0.0339 3.0000 1.0000 SOURCE: Developed by the Research Team TABLE E-3 Principal Component Results for the Consumption Group Consumption Group Results Principal Components in Actual Number Value Difference Proportion Cumulative Value Cumulative Proportion 1 9.1971 8.0693 0.8361 9.1971 0.8361 2 1.1279 0.7319 0.1025 10.3250 0.9386 3 0.3960 0.2716 0.0360 10.7210 0.9746 4 0.1244 0.0527 0.0113 10.8453 0.9859 5 0.0716 0.0076 0.0065 10.9170 0.9925 6 0.0641 0.0550 0.0058 10.9810 0.9983 7 0.0091 0.0036 0.0008 10.9901 0.9991 8 0.0055 0.0020 0.0005 10.9956 0.9996 9 0.0035 0.0026 0.0003 10.9991 0.9999 10 0.0008 0.0007 0.0001 10.9999 1.0000 11 0.0001 -- - 0.0000 11.0000 1.0000 SOURCE: Developed by the Research Team TABLE E-4 Principal Component Results for the Foreign Exchange Group Foreign Exchange Results Principal Components in Actual Number Value Difference Proportion Cumulative Value Cumulative Proportion 1 2.7825 1.7142 0.6956 2.7825 0.6956 2 1.0683 0.9215 0.2671 3.8508 0.9627 3 0.1468 0.1444 0.0367 3.9976 0.9994 4 0.0024 -- - 0.0006 4.0000 1.0000 SOURCE: Developed by the Research Team E‐2
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TABLE E-5 Principal Component Results for the Production Group Production Group Results Principal Components in Actual Number Value Difference Proportion Cumulative Value Cumulative Proportion 1 6.8376 6.7103 0.9768 6.8376 0.9768 2 0.1273 0.0969 0.0182 6.9648 0.9950 3 0.0303 0.0274 0.0043 6.9951 0.9993 4 0.0029 0.0019 0.0004 6.9981 0.9997 5 0.0010 0.0002 0.0001 6.9991 0.9999 6 0.0008 0.0008 0.0001 7.0000 1.0000 7 0.0000 -- - 0.0000 7.0000 1.0000 SOURCE: Developed by the Research Team TABLE E-6 Principal Component Results for Manager Index/Capacity Utilization Group Purchasing Manager Index & Capacity Utilization Group Results Principal Components in Actual Number Value Difference Proportion Cumulative Value Cumulative Proportion 1 1.9898 1.0921 0.6633 1.9898 0.6633 2 0.8977 0.7852 0.2992 2.8875 0.9625 3 0.1125 -- - 0.0375 3.0000 1.0000 SOURCE: Developed by the Research Team TABLE E-7 Principal Component Results for Employment Group Employment Group Results Principal Components in Actual Number Value Difference Proportion Cumulative Value Cumulative Proportion 1 2.9597 2.9224 0.9866 2.9597 0.9866 2 0.0373 0.0342 0.0124 2.9969 0.9990 3 0.0031 -- - 0.0010 3.0000 1.0000 SOURCE: Developed by the Research Team E‐3
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From page 144... ...
TABLE E-8 Principal Component Candidate Demand Factor Weights: Commodity Group Principal Component Candidate Demand Factor Weights: Commodity Group Actual Candidate Demand Factors 1 2 3 Urban Gas Price in Real $ 0.62 -0.26 -0.74 Increase in Coal Producer Price Index 0.51 0.85 0.12 Urban Gas Price in Real $ 1 yr Lag 0.60 -0.45 0.66 SOURCE: Developed by the Research Team TABLE 1 Principal Component Candidate Demand Factor Weights: Consumption Group Principal Component Candidate Demand Factor Weights: Consumption Group Actual Candidate Demand Factors 1 2 3 4 5 Real Personal Consumption 2005 Chained $ 0.32 -0.16 0.14 0.28 0.03 Total Housing Starts 0.19 0.72 0.45 -0.01 0.45 Inv.-Sales Ratio (Census) -0.32 0.07 -0.15 0.53 0.41 Chained Inv.-Sales Ratio (BEA)
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TABLE E-11 Principal Component Candidate Demand Factor Weights: Production Group Principal Component Candidate Demand Factor Weights: Production Group Actual Candidate Demand Factors 1 2 3 4 5 Real GDP 2005 Chained $ 0.38 -0.13 0.04 0.14 -0.87 Real Income/Capita, Chained 2005 $ 0.38 -0.31 0.12 0.76 0.35 Industrial Production Index 0.38 0.03 -0.58 -0.16 0.27 Industrial Manufacturing Index 0.38 0.00 -0.56 -0.07 -0.07 Real Exports in Goods (in $) 0.36 0.88 0.26 0.13 0.06 Real GDP 2005 Chained $, 1 Yr.
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From page 146... ...
TABLE E-14 Rail Tons PCA -- Log Actual Regressions Rail Tons Log – Actual* Model 1 2 2*
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From page 147... ...
TABLE 2 Rail Tons PCA -- Log Actual Diagnostics Rail Tons Log - Actual* Model 1 2 2*
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From page 148... ...
TABLE E-16 Rail Ton Miles PCA -- Log Actual Regressions Rail Ton Miles Log - Actual* Model 1 2 3 Period of Estimation 1981-2007 1981-2007 1982-2007 R - Squared (adjusted)
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From page 149... ...
TABLE E-17 Rail Tons Miles PCA -- Log Actual Diagnostics Rail Ton Miles Log - Actual* Model 1 2 3 Number of Observations 27 25 26 AIC (Information Criteria)
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From page 150... ...
TABLE E-18 Rail Revenue Ton Miles PCA -- Log Actual Regressions Rail Revenue Ton Miles Log - Actual* Model 1 2 3 Period of Estimation 1981-2007 1982-2007 1982-2007 R - Squared (adjusted)
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From page 151... ...
TABLE E-19 Rail Revenue Ton Miles PCA -- Log Actual Diagnostics Rail Revenue Ton Miles Log - Actual* Model 1 2 3 Number of Observations 27 26 26 AIC (Information Criteria)
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From page 152... ...
TABLE E-20 Rail Train Miles PCA -- Log Actual Regressions Rail Train Miles Log - Actual* Model 1 2 3 Period of Estimation 1990-2007 1990-2007 1990-2007 R - Squared (adjusted)
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From page 153... ...
TABLE E-21 Rail Train Miles PCA -- Log Actual Diagnostics Rail Train Miles Log - Actual* Model 1 2 3 Number of Observations 18 18 18 AIC (Information Criteria)
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From page 154... ...
TABLE E-22 Rail Car Miles PCA -- Log Actual Regressions Rail Car Miles Log - Actual* Model 1 2 3 Period of Estimation 1990-2007 1991-2007 1990 2007 R - Squared (adjusted)
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From page 155... ...
TABLE E-23 Rail Car Miles PCA -- Log Actual Diagnostics Rail Car Miles Log - Actual* Model 1 2 3 Number of Observations 18 17 18 AIC (Information Criteria)
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From page 156... ...
TABLE E-24 Truck Ton Miles PCA -- Log Actual Regressions Truck Ton Miles Log - Actual* Model 1 2 3 Period of Estimation 1982-2007 1983-2007 1984-2007 R - Squared (adjusted)
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From page 157... ...
TABLE E-25 Truck Ton Miles PCA -- Log Actual Diagnostics Model 1 2 3 Number of Observations 26 25 24 AIC (Information Criteria) -153.31 -156.20 -127.40 BIC (Information Criteria)
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From page 158... ...
TABLE E-26 Truck VMT PCA -- Log Actual Regressions Truck VMT Log - Actual* Model 1 2 3 Period of Estimation 1982-2007 1983-2007 1984-2007 R - Squared (adjusted)
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From page 159... ...
E‐19 TABLE E-27 Truck VMT PCA -- Log Actual Diagnostics Truck VMT Log - Actual* Model 1 2 3 Number of Observations 26 25 24 AIC (Information Criteria)
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