Comprehensive Truck Size and Weight Limits Study - Modal Shift Comparative Analysis Technical Report
Appendix H: Rail Contribution and Revenue Analysis
Truck Size and Weight Short Line Analysis Methods
This document outlines the technical approach to the analyses of the effects on short line railroads brought about by changes in truck sizes and weights. Short lines are Class II and Class III railroads as defined by the Surface Transportation Board (STB). There are around 560 short line railroads operating in the U.S. Of these, 10 are Class II's with the remaining Class III's. Together these railroads originate or terminate about 18 percent of Class I carload freight or around 6.5 million carloads, annually and generate around $4 billion in revenues. While commodity makeup on these carriers is diverse, they principally serve rural communities and provide these areas the rail link to the Class I railroad network. Short line railroads provide two primary high level service: 1) extension of Class I railroads with the interlining and 2) regional/intrastate rail service.
Similar to the Class I railroad analysis, the short line analysis examined the impacts on operating revenues resulting from both rate reductions or discounting on the part of the railroad to hold on to existing rail traffic and lost revenue due to diversion of traffic from rail to trucks when the railroad has to give up the traffic because it will not move the goods below cost. The short line analysis uses the ITIC model and the 2011 STB Carload Waybill Sample in the same way as was done for the analysis of potential impacts on the Class I railroads.
To consider the effects on short line railroads, those records on the waybill sample were analyzed, where a short line railroad was identified as an originating, intermediate, or terminating carrier. This is the "documented" set of short line moves. This data set includes any waybill that reports a short line railroad. Overall, the waybill sample documents moves by around 140 short line railroads, far fewer than the total number of short line railroads operating in any year. Industry experience tells us that sometimes short line railroads are not included on the waybill sample because the Class I railroad handles the billing/settlement for these carriers. To handle the unreported short line railroads, an additional dataset was developed that identified waybill records where the origin or destination was on a Class I railroad and there was access to a short line railroad within a reasonable range of their origin or destination. This dataset was referred to as the "potential" short line waybills. This data set included any waybill record that could potentially have involved a short line railroad but did not identify that short line on the waybill.
The short line analysis employed two data sources to develop revenue impacts for the illustrative truck size and weight scenarios. First, the analysis used the 2011 STB Carload Waybill Sample. The Waybill Sample was used in conjunction with the ITIC model to estimate rail shipments potentially affected by the scenario truck size and weight limits and short line revenues affected by the scenarios. Finally, the 2011 Centralized Station Master (CSM) was used to determine which waybills on the Waybill Sample would be geographically relevant to short line railroads.
Short line rail impacts were analyzed after total rail impacts were estimated. Initially all rail shipments potentially affected by the scenario truck size and weight limits were identified through the application of ITIC. Subsequently those shipments were further analyzed to assess which might have included movements by short lines for part of the trip. As explained below, some waybill records explicitly included the short line portion of the move while short line operations for other moves had to be inferred based on the proximity of short line railroads to origins and destinations of waybill records.
Documented Short Line Data Set
The documented short line data set includes any waybill record where a short line was specifically identified as being involved along some portion of the route. To construct the documented short line data set, we identified each waybill sample where a short line was an originating, intermediate, or terminating carrier. Next, those waybills were crossed referenced with the set of all rail waybills that were identified as being affected by the scenario truck size and weight policy changes. The dataset of documented waybills involving short line moves that would be affected by scenario changes was broken down into two sets: those for which rail traffic would be diverted to trucks and those for which short lines could be expected to discount their prices to keep the traffic from diverting to truck. As expected, diversions or rate reductions occurred across multiple scenarios for the same waybill. For example, if rail traffic reflected by a waybill is diverted in Scenario 1, diversion would also occur in Scenarios 2 and 3 because the size of the truck increases with each scenario.
To estimate the revenue impacts from diverted traffic, the analysis used the waybill sample revenue estimates. The waybill includes revenue for each railroad on each part of the journey. This estimate of revenue differs slightly from that of the analysis for all railroads which used average revenue for particular origin and destination pairs. For the revenue impacts due to diverted traffic, the results aggregated the revenue across the waybills by including only the revenue received by the short line segment of the trip. The analysis assumes that all revenue on a diverted waybill is lost. Revenue losses were estimated only for Scenarios 1-3. Potential revenue losses associated with Scenarios 4-6 could not be estimated due to data constraints.
To estimate the revenue lost due to discounting rates to keep traffic on the rail, the analysis used the revenue reduction totals as estimated in the original analysis. These totals include revenue for the entire haul and, therefore, could not explicitly be broken down by revenue lost on Class I railroads versus short line railroads. To estimate the revenue lost on short line railroads, the analysis first estimated the percent of total revenue on each waybill for a given short line. Next, the analysis applied this percent to the total revenue lost due to discounting to estimate the loss to short line railroads only. This step assumes that the lost revenue due to discounting is lost in the same proportion as the revenue received. In theory, a Class I could absorb a larger percentage of these losses or vice versa, but no information was available to estimate differential rate reductions.
Potential Short Line Data Set
As noted above, not all short line operations are directly reflected on waybill records. The CSM data provides geographical information on railroad junctions and allows waybills that potentially included short line operations to be identified when the waybill record does not include information on short line involvement. A potential short line data set (waybills that potentially involved short line moves that were not specifically noted in the waybill itself) was developed to attempt to estimate the full range of potential short line impacts associated with scenario truck size and weight limit changes. This potential short line data set consists of all waybills with origins or destinations at junctions with a short line railroad, but which do not indicate that a short line was involved in the move. There was no way to determine which of these records actually involved a short line, so this data set includes all potential waybill records that could have included short line operations that were not reported. As with the documented short line data set, waybills in the potential short line data set were matched with waybills from the overall analysis of potential rail impacts associated with each scenario to identify the potential short line moves that could be affected by truck size and weight changes.
Because none of these records actually have the short line documented at the origin or destination, these waybill records could not provide revenue explicitly for the short line portion of the trip. To assign the revenue to short line railroads, the analysis first examined the entirety of the waybill sample to identify waybill records with similar trip characteristics. The waybill was used to identify any trip where a short line railroad provided the origin or destination segment of the trip and connected to a Class I railroad (these records are already included in the documented dataset). From this sample, the analysis estimated the average percent of revenue a short line received when providing service and connecting to a Class I railroad. Next, this percent was applied to the total revenue on a waybill to estimate the hypothetical short line revenue.
Using this methodology, the analysis needed to assume that there is no systematic bias in the way waybills are and are not reported for short line railroads. This revenue would not be a good proxy for an unreported short line trip if the true population of unreported short line trips were inherently different from those that are reported. For example, if unreported short line trips were overall shorter than the ones reported or if particular routes were systematically not reported on the waybill, the revenue estimates would not be a good proxy for the unreported short line trip. This data set should be thought of as an upper bound to the potential of unreported short line trips. Obviously, not all short line trips are unreported. This dataset provides an illustrative example of the worst case impacts on the unreported short lines but makes no claims as to which, where, or how often short line railroads go unreported.
End Notes:
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[1] The Federal government began regulating truck size and weight in 1956 when the National Interstate and Defense Highways Act (Public Law 84-627), establishing the Interstate Highway System, was enacted. A state wishing to allow trucks with sizes and weights greater than the Federal limits was permitted to establish "grandfather" rights by submitting requests for exemption to the FHWA. During the 1960s and 1970s, most grandfather issues related to interpreting State laws in effect in 1956 were addressed, and so most grandfather rights have been in place for many decades. See USDOT Comprehensive Truck Size and Weight Study, Volume 2, "Chapter 2: Truck Size and Weight Limits - Evolution and Context," FHWA-PL-00-029 (Washington, DC: FHWA, 2000), p. II-9.
[2] A county centroid is the latitudinal and longitudinal (i.e., geographic) center of a county. See http://opengeocode.org/tutorials/USCensus.php for more information.
[3] The Federal government began regulating truck size and weight in 1956 when the National Interstate and Defense Highways Act (Public Law 84-627), establishing the Interstate Highway System, was enacted. A state wishing to allow trucks with sizes and weights greater than the Federal limits was permitted to establish "grandfather" rights by submitting requests for exemption to the FHWA. Claims that were not legally defensible were rejected. During the 1960s and 1970s, most grandfather issues related to interpreting State laws in effect in 1956 were addressed, and so most grandfather rights have been in place for many decades. See USDOT Comprehensive Truck Size and Weight Study, Volume 2, "Chapter 2: Truck Size and Weight Limits - Evolution and Context," FHWA-PL-00-029 (Washington, DC: FHWA, 2000), p. II-9.
[4] United States Government Accountability Office, Report to the Subcommittee on Select Revenues Measures, Committee on Ways and Means, House of Representative, GAO-11-134, January 2011.
[5] See Federal Register, Volume 79, No. 111, June 10, 2014, p. 33257. The Surface Transportation Board defines class of railroad based on revenue thresholds adjusted for inflation. For 2013, the most recent available, Class I carriers had revenues of $467.0 million or more. Class II carriers have revenues ranging from $37.4 million to under $467.0 million. Class III carriers have revenues under $37.4 million. All switching and terminal carriers regardless of revenues are Class III carriers. (See 49 CFR 1201.1-1)
[6] For the potential data set, this will always be 21 percent.
[7] Coast-down testing is a technique for establishing the dynamometer load which simulates the vehicle road load during EPA dynamometer fuel economy and emission testing.
[8] SmartWay tires are certain low-resistance tire models that the EPA has determined can reduce NOx emissions and fuel use by 3 percent or more, relative to the best selling new tires for line haul class 8 tractor trailers. See Source: http://epa.gov/smartway/forpartners/technology.htm for more information.
[9] FHWA, Work Zone Safety and Mobility Rules, https://ops.fhwa.dot.gov/wz/resources/final_rule.htm. Last modified September 19, 2013.
[10] Elefteriadou, Lily and Nathan Webster, "Quantifying Traffic Operational Impacts of New Truck Configurations in the U.S. Highway Network," 1997, http://road-transport-technology.org/Proceedings/5%20-%20ISHVWD/Part%201/QUANTIFYING%20TRAFFIC%20OPERATIONAL%20IMPACTS%20OF%20NEW%20TRUCK%20CONFIGURATIONS%20IN%20THE%20U.S.%20HIGHWAY%20NETWORK%20-%20Elefteriadou%20.pdf
[11] Roberts, Paul O., and J.R. Ginn, Stockout Costs in Inventory Management, Harvard Business School Working Paper, 71-9, April, 1971.
[12] Roberts, Paul O., The Translog Shipper Cost Model, MIT Center for Transportation Studies Report No. 81-1, U.S. Department of Transportation University Research Program, Cambridge Massachusetts, June, 1981.
[13] Friedlaender, A.F. and R.H. Spady, Hedonic Rates and the Derived Demand for Freight Transportation, Center for Transportation Studies, Massachusetts Institute of Technology, Cambridge, MA, 1977.
[14] Jack Fawcett, Associates, Transportation Demand Forecasting, Transportation Research Board Special Report, 1988.
[15] Y.S. Chiang, A Policy Sensitive Model of Freight Demand, PhD Dissertation, Department of Civil Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 1979.
[16] Examples include, Morton (1969), Tihansky (1972), Wang and Epstein (1975) and Sloss (1971).
[17] For examples see articles in Mathematica by, Miller (1972), and in (1969), and Watson et al. (1974).
[18] Paul O. Roberts, Moshe Ben Akiva, M. Terziev, and Y.S. Chiang, Development of A Policy Sensitive Model For Forecasting Freight Demand, M.I.T. Center for Transportation Studies, CTS Report 77-11, Cambridge, MA, April 1977.
[19] Winston, Clifford, Mode Choice in Freight Transportation, Department of Economics, University of California, Berkeley, CA 1978.
[20] Roberts, Paul O., with Mark Terziev, James Kneafsey, Lawrence Wilson, Ralph Samuelson, Yu Sheng Chiang, and Christopher Deephouse, Analysis of the Incremental Cost and Trade-Offs Between Energy Efficiency and Physical Distribution Effectiveness in Intercity Freight Markets, MIT Center for Transportation Studies, Report CTS 76-14, Cambridge, MA, November, 1976.
[21] Roberts, P. O. with Tom Brigham, and Carol Miller, An Equilibrium Analysis of Selected Intercity Freight Markets: Truck with Double Trailers vs. TOFC Shuttle Trains, MIT Center for Transportation Studies Report CTS 77-25, Cambridge, MA, December, 1977.
[22] The Intermodal Competition Model was programmed for the AAR by an outside contractor from a model design developed by Dr. Paul O. Roberts and described in The Translog Shipper Cost Model Op. Cit., 1981.
[23] FHWA, Western Uniformity Scenario Analysis, Washington, DC, 2003.
[24] Federal Railroad Administration, Study of the Benefits of Positive Train Control, 2004.
[25] Roberts, Paul O., Factors Influencing the Demand for Freight Transport, CTS Discussion Paper 8-75, MIT Center for Transportation Studies, Cambridge, Massachusetts, August 1975.
[26] Roberts, P.O. and A.S. Lang, The Tradeoffs Between Railroad Rates and Service Quality, Report 78-12, MIT Center for Transportation Studies, Cambridge, Massachusetts, May 1978.
[27] County Business Patterns is issued annually by the Department of Commerce, Bureau of the Census, Washington, DC.
[28] Chiang, Y.S. and P.O. Roberts, Representing Industry and Population Structure for Estimating Freight Flows, MIT Center for Transportation Studies CTS Report 76-8, Cambridge, Massachusetts, August 1976.
[29] It is very unlikely that a location with shipments less than two truckloads per year would obtain large enough benefits from mode diversion to overcome the initial cost and inertia of the change.
[30] Freight Station Accounting Code Directory, Association of American Railroads, Accounting Division, American Railroads Building, Washington, DC, 20036.
[31] The Federal Bridge Formula is a formula used by highway engineers to define limits on the weight and spacing of roadway wheel loadings of highway vehicles for use in bridge design.
[32] Wolfe, K. Eric and W.P. Linde, "The Carload Waybill Statistics: Usefulness for Economic Analysis," Journal of the Transportation Research Forum, Volume 36, No. 2, 1997, pp. 26 - 41.
[33] The model is an "all or nothing" choice based on a comparison of total logistics costs for the alternatives modeled.
[34] Generally, captive shippers are those which do not enjoy viable competitive alternatives to the serving rail carrier by virtue of the product shipped or their location or both.
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