National Academies Press: OpenBook

Performance Measures for Freight Transportation (2011)

Chapter: Appendix E - Modal Freight Performance Measures: State of Practice

« Previous: Appendix D - National-Level Performance Measures: State of Practice
Page 120
Suggested Citation:"Appendix E - Modal Freight Performance Measures: State of Practice." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
×
Page 120
Page 121
Suggested Citation:"Appendix E - Modal Freight Performance Measures: State of Practice." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
×
Page 121
Page 122
Suggested Citation:"Appendix E - Modal Freight Performance Measures: State of Practice." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
×
Page 122
Page 123
Suggested Citation:"Appendix E - Modal Freight Performance Measures: State of Practice." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
×
Page 123
Page 124
Suggested Citation:"Appendix E - Modal Freight Performance Measures: State of Practice." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
×
Page 124
Page 125
Suggested Citation:"Appendix E - Modal Freight Performance Measures: State of Practice." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
×
Page 125
Page 126
Suggested Citation:"Appendix E - Modal Freight Performance Measures: State of Practice." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
×
Page 126
Page 127
Suggested Citation:"Appendix E - Modal Freight Performance Measures: State of Practice." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
×
Page 127
Page 128
Suggested Citation:"Appendix E - Modal Freight Performance Measures: State of Practice." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
×
Page 128
Page 129
Suggested Citation:"Appendix E - Modal Freight Performance Measures: State of Practice." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
×
Page 129
Page 130
Suggested Citation:"Appendix E - Modal Freight Performance Measures: State of Practice." National Academies of Sciences, Engineering, and Medicine. 2011. Performance Measures for Freight Transportation. Washington, DC: The National Academies Press. doi: 10.17226/14520.
×
Page 130

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

121 a p p e n D i X e Modal Freight Performance Measures: state of Practice

122 Mode-Specific Performance Measures At the national level, significant volumes of data are col- lected to measure many aspects of freight system perfor- mance. Collecting freight-related data are all of the modal agencies of the U.S. Department of Transportation, including the FHWA, the Federal Railroad Administration (FRA), the Federal Motor Carrier Safety Administration (FMCSA), the Maritime Administration (MARAD), the FAA, the National Highway Transportation Safety Administration (NHTSA), and the Surface Transportation Board (STB). The U.S. Army Corps of Engineers (USACE) collects performance data on the national marine transportation system. The U.S. Envi- ronmental Protection Agency (EPA) tracks and regulates mobile emissions, including those of trucks, ships, railroads, and aircraft. The U.S. Department of Commerce monitors imports, exports, and many categories of commercial output. The complexities of using data from these different sources are discussed below in the data quality of this appendix. highway infrastructure condition Measures The Highway Performance Monitoring System provides data that reflect the extent, condition, performance, use, and operating characteristics of the nation’s highways. It was developed in 1978 as a national highway transportation sys- tem database and includes limited data on all public roads, more detailed data for a sample of the arterial and collector functional systems, and certain statewide summary informa- tion. The data are sample based and therefore do not provide data regarding every highway link. Also, speed and reliability data are estimated, making the data unsuitable for examina- tion of individual links. The National Bridge Inventory compiles bridge inspection data on the nation’s bridges as reported by the state and local governments. It reports conditions in terms of Functional Obsolescence and Structural Deficiencies. The National Bridge Inventory is bridge specific. The Fatality Analysis Reporting System includes state- by-state data on crashes by type, including those involving trucks. Trucking performance Measures Trucking-specific performance data in the public domain remain insufficient for many policy, investment, and opera- tional decisions, according to some.1 State transportation agencies usually generate actual or estimated average daily traffic volumes for trucks, but other important information, such as truck origins and destinations, remains expensive and intrusive for them to collect. Traditional means for gathering such information have involved stopping trucks and inter- viewing drivers or giving them a questionnaire. Often such surveys were conducted only once a decade, or less frequently. A need for improved truck freight performance data has led to efforts to use existing commodity-flow data and to exploit emerging technologies. The Freight Analysis Frame- work (FAF), a FHWA-led initiative, analyzes commodity-flow information to produce estimates of overall freight volumes, as well as estimated origins and destinations. An FAF guide- book2 describes several means by which capacity-related measures could be estimated, including: • Traffic volume, • Capacity, • Volume-to-capacity ratios, • Average speed, • Travel time, and • Link delay. Efforts to use GPS data to improve truck-movement infor- mation began in 1999 with an investigation of the use of on- board devices to monitor the trucking industry’s use of road- ways.3 This research was limited, however, to the number of participating drivers and companies, and by equipment costs. To further research the idea of using wireless truck position data to determine metrics related to demand for roadways, Short and Jones4 analyzed several million truck movements across the U.S. interstate system. It was shown that a ranking of demand for groupings of 3-mile segments (i.e., hundreds of segments falling across entire interstate corridors) could be determined, thus identifying a potential complement to the FAF information described earlier. The research has pro- duced robust travel-time and travel-reliability information on the Interstate Highway System (IHS) and has identified major truck-freight bottlenecks.5 Using this source, the following methods for producing freight performance measures have been developed: Use of Multiple Unique Truck Positions to Measure Speed: Using this method, truck position pairs for individual/unique vehicles are matched, and a time/distance calculation is made to deter- mine average travel rates. The end results are calculations along broad corridors (e.g., Interstate 10 from Jacksonville, FL, to Santa Monica, CA). A database of such calculations is updated monthly and measures 27 U.S. interstate corridors. Measurements can focus on specific regions, times of day, and days of the week. Measuring Border Crossing and Bottleneck Delay: Delay is mea- sured at border crossings and other points, such as highly con- gested bottlenecks, by measuring travel time across such points. Spot Speed Measures: Speeds can also be measured for specific urban areas and highway intersections that are highly congested. The end result includes measures such as average speed in a geo- graphic location by hour of day, which identifies peak times of freight congestion/delay.

123 The rate at which trucks move and thus the time it takes to travel given distances is a common indicator of issues such as congestion and delay. Measuring such issues through the use of travel-time and travel-rate information can produce the following types of metrics:6 • Travel time or difference in travel times (minutes or seconds) • Travel rate (travel time divided by travel distances) • Delay rate (minutes per mile) • Total delay (person hours, vehicle hours) • Relative delay rate (delay rate divided by acceptable travel rate • Delay ratio (delay rate divided by actual travel rate) • Miles of congested roadway • Miles of congested travel reliability Reliability of truck movement, as the term implies, is an indicator of how likely a roadway will perform in a certain way during a given period of time. As would be expected, trucking companies often prefer roadways that perform in a reliable manner so that they can plan routes/deliveries and accurately estimate costs. Such factors can play a role in meet- ing delivery windows and scheduling required hours of ser- vice and rest periods. Three methods are commonly used for determining reli- ability of travel: statistical range, buffer time measures, and tardy trip indicators:7 Statistical Range: This can be described as a Travel Time Window, Percent Variation, or Variability Index, all of which can be applied to freight movement. Buffer Measures: These can be considered as “time allowance,” and measures include Buffer Time, Buffer Time Index, and Plan- ning Time Index. Tardy Trip Indicators: These indicators measure “the unreli- ability impacts using the amount of late trips.” Included in this category are the Florida Reliability Method, which measures travel time during the peak, On-Time Arrival measures, and the Misery Index, which measures the most-congested 20 percent of travel periods. FHWA8 offers two methods for measuring reliability. The first, shown in Figure E.1, is named the Travel Time Index, which compares peak period and free-flow travel conditions. The second method is the Buffer Time Index, which “expresses the amount of extra ‘buffer’ time needed to be on- time 95 percent of the time (late one day per month).”9 FHWA defines travel time reliability “as how much travel times vary over the course of time.” Thus, when measuring the reliability of truck movements, truck-specific informa- tion can be analyzed to determine similar travel-time vari- ability (over a specific time period) for all or part of the trucking industry. The calculation of reliability measures specifically for trucks is demonstrated as shown in Figure E.2.10 The buffer time was “calculated for travel across entire corridors [e.g., Interstate 10], for each of the 100-mile segments of the cor- ridor, and travel across every combination of each of the 100- mile segments of a corridor.” operational costs Beyond speed, delay, and reliability, several performance measures look at the cost of production. A first measure is cost per mile. The American Trucking Associations (ATA) 2003 Motor Carrier Annual Report11 lists the key elements of a cost-per-mile calculation for trucking. This includes the fol- lowing in approximate order of magnitude: • Other wages and benefits • Equipment rents and purchased transportation • Driver wages • Miscellaneous • Fuel and fuel taxes 3 • Miles of congested roadway • Miles of congested travel Reliability Reliability of truck movement, as the term implies, is an indicator of how likely a roadway will perform in a ce tain during a given period of time. As would be expected, trucking companies often prefer roadways that perform in a reliable manner so that they can plan routes/deliveries and accurately estimate c sts. Such actors can play a role in meeting delivery windows a d scheduling required hours of service and rest periods. Three methods are commonly used for determining reliability of travel: statistical range, buffer time measures, and tardy trip indicators:7 Statistical Range: This can be described as a Tr vel Time Window, Percent Variation, or Variability Index, all of which can be applied to freight movement. Buffer Measures: These can be considered as “time allowance,” and measures include Buffer Time, Buffer Time Index, and Planning Time Index. Tardy Trip Indicators: These indicators measure “the unreliability impacts using the amount of late trips.” Included in this category are the Florida Reliability Method, which measures travel time during the peak, On-Time Arrival measures, and the Misery Index, which measures the most-congested 20 percent of travel periods. FHWA8 offers two methods for measuring reliability. The first, shown in Figure E.1, is named the Travel Time Index, which compares peak period and free-flow travel conditions. Figure E.1. Travel Time Index The second method is the Buffer Time Index, which “expresses the amount of extra ‘buffer’ time needed to be on-time 95 percent of the time (late one day per month).” FHWA defines travel time reliability “as how much travel times vary over the course of time.” Thus, when measuring the reliability of truck movements, truck-specific information can be analyzed to Comment [JP1]: Author: If this is the Cambridge Systematics doc you should have a citation with page. Figure E.1. Travel Time Index.

124 4 determine similar travel-time variability (over a specific time period) for all or part of the trucking industry. The calculation of reliability measures specifically for trucks is demonstrated as shown in Figure E.2.9 The buffer time was “calculated for travel across entire corridors [e.g., Interstate 10], for each of the 100- mile segments of the corridor, and travel across every combination of each of the 100-mile segments of a corridor.” Figure E.2. Buffer Index from Freight Performance Measures Initiative. Operational Costs Beyond speed, delay, and reliability, several performance measures look at the cost of production. A first measure is cost per mile. The American Trucking Associations (ATA) 2003 Motor Carrier Annual Report10 lists the key elements of a cost-per-mile calculation for trucking. This includes the following in approximate order of magnitude: • Other wages & benefits • Equipment rents & purchased transportation • Driver wages • Miscellaneous • Fuel & fuel taxes • Depreciation • Insurance • Outside maintenance • Tax and license • Tires Measurement of Safety Performance for Trucks Truck safety measures can be calculated in several ways. ATA11 identifies the number of fatal crashes annually as a safety measurement for the entire industry and places the measure into two categories: • Total Annual Large-Truck Fatal Crashes • Large-Truck Fatal Crash Rate Per 100 Million VMT Comment [JP2]: Author: Please check the endnote. It names another document as the source for this document. Figure E.2. Buffer Index from Freight Performance Measures Initiative. • Depreciation • Insurance • Outside maintenance • Tax and license • Tires Measurement of safety performance for Trucks Truck safety measures can be calculated in several ways. ATA12 identifies the number of fatal crashes annually as a safety measurement for the entire industry and places the measure into two categories: • Total Annual Large-Truck Fatal Crashes • Large-Truck Fatal Crash Rate Per 100 Million VMT Such statistics are typically sourced from reports such as the FMCSA Large Truck Crash Facts 2005, which develops measures from data sources such as FARS, NHTSA’s General Estimates System (GES), and FMCSA’s Motor Carrier Manage- ment Information System (MCMIS).13 FMCSA organizes crash statistics into four sections, which are described as follows: • Number of crashes; • Number of vehicles involved in crashes; • Number of people involved and resulting fatalities and in- juries; and • Number of drivers involved. FMCSA addresses the cost of highway crashes that involve medium and heavy trucks with estimates for the following measures: • Cost of crashes involving longer combinations; • Cost of straight truck crashes; • Cost of “property damage only” crashes; • Cost per crash involving a nonfatal injury; and • Cost per crash involving a fatality. economic Measures, Forecasting, and other private-sector Trucking performance Measures Although much of the truck-specific economic forecast- ing that is produced is related to growth in truck tonnage and other freight sectors, the trucking industry does follow the economic forecasts for a variety of non-freight industries, especially manufacturing. ATA’s U.S. Freight Transportation Forecast tracks trends and forecasts in manufacturing, con- struction, agricultural commodities, mining, and non-oil merchandise imports. private-sector summary Key private-sector performance measures are produced by ATA and listed in Trucking Trends. These measurements include the following: Commodity/ Commodity Flow Information: The statistics followed by the industry in this category focus mainly on how freight is moved (i.e., percentage by truck, rail, air), as well as the value of and type of goods shipped. Trucking Company Failures: The number of trucking com- pany failures that occur in a given time period is an indicator of industry performance. Trends in the number of failures can help measure the impact of other forces on the trucking industry, such as high fuel prices or an economic slowdown. Tonnage Growth: ATA has a For-Hire Truck Tonnage Index that measures the decline or growth in freight hauled by the in- dustry on an annual basis, as well as percent changes in the ton- nage index itself. Revenue Growth: For-Hire Trucking Revenue is also measured as an index, as well as the percentage change in the index itself. Revenue per Mile and Revenue per Ton: Both revenue per mile and per ton of freight shipped are indexed on an annual basis.

125 Trucking Producer Price Indices: The Producer Price Index for segments of trucking are used to track the change in prices for trucking services in general, and specifically for truckload carriers, less-than-truckload carriers, local delivery, and long- distance trucking (as well as other segments). Other Financial and Operating Statistics: USDOT typi- cally releases financial and operating data collected through Form M, which is a required reporting document for carriers with $3 million or more in annual revenue. These data and the performance measures derived from the data have not been re- leased by USDOT since 2003. Rail Performance Measures The American Association of Railroads (AAR) has since 1999 published performance measures for the Class I rail- roads. On its website (http://www.railroadpm.org/) it reports weekly updates on train travel speeds, cars on line, and dwell times of Norfolk Southern, CSX, Union Pacific, BNSF, Kansas City Southern, and Canadian Pacific Railway. It notes that, despite using common methodology, one railroad’s perfor- mance metrics should not be compared to another’s. It notes that performance can be affected by differences in network terrain, railroad design, the mix of traffic, the effect of pas- senger operations, and external factors such as weather and port operations. It also notes that each railroad’s calculation methodology of each measure also can vary. The performance measures allow train speeds to be mea- sured by train type, such as intermodal, grain, coal, or double stack. It allows dwell times to be observed at major yards. It also tracks cars on the system by the various types of cars such as box, intermodal, or hopper cars. Historical performance data are available for the past 53 weeks. AAR reports that the railroads agreed over a series of years to consolidate their performance reporting for public convenience. AAR states that it is unaware of the cost to the railroads of generating the measures because each railroad contributes its data from its internal reporting mechanisms. surface Transportation Board data STB requires voluminous reporting data from the U.S. rail- roads, much of which could be used to develop performance measures at the national level. The data generally are aggre- gated from proprietary sources and are therefore not avail- able at a local or regional level. Some of the data sources are described below. Waybill data STB requires U.S. railroads to report sample waybill data, which is reported in a public form that has been purged of proprietary information. It contains information regard- ing origin and destination of cargo, types of commodities shipped, numbers of cars, tons and revenue, and length of haul. These data could be translated into various perfor- mance measures of rail volume, commodity shipment types, or other measures.14 railroad earnings The economic health of railroads is measurable from the earnings reports that the publicly traded and publicly regu- lated railroads must report. These reports track gross rev- enues, net operating revenues, revenue ton-miles, and net income. In addition, the corporate annual reports required by the Securities and Exchange Commission provide detailed information on the economic performance of the railroads.15 railroad statistics More than 1,500 categories of statistics are reported for each of the Class I railroads in the Statistics of Class I Freight Railroads report. These data, required by STB, include uni- form reporting of income, expenses, investments in track, equipment investments, and depreciation by various catego- ries. These data were last published in 2004.16 cost of capital STB17 makes an annual calculation of whether the Class I railroads have earned income that exceeds their cost of capi- tal, which for 2007 was determined to be 11.3 percent. For 2008, it determined that the NS and Soo Line, or Canadian Pacific, railroads earned more than their cost of capital. All other Class I railroads were found to be either revenue “ade- quate” or “inadequate.” rail safety data The Federal Railroad Administration Office of Safety Anal- ysis18 website (http://safetydata.fra.dot.gov/ officeofsafety/) provides search and query tools to conduct analyses of rail- road crashes. The query tools link to federal crash databases that allow for analysis of crashes by railroads, state, crash types, and localities. Links to individual crash reports are provided. Aviation Performance Measures The air transportation industry has been measure- intensive for decades, with both private carriers and the FAA carefully evaluating key measures of reliability, safety, and service. Annually, beginning in FY 2004, FAA developed an Figure E.2. Buffer Index from Freight Performance Measures Initiative.

126 aggressive strategic plan to help manage and measure per- formance. Its Flight Plan provides the framework to match resources with initiatives for long-term change. This report sets forth goals and the performance measures to assess prog- ress in meeting them and is tightly aligned with the mission, vision, goals, and performance measures outlined in the DOT Strategic Plan. The Flight Plan highlights performance measures, and conducts analysis on each measure to determine whether the data were complete and reliable enough to measure appropri- ately. Within this report performance measures are grouped in the broad categories of Safety, Capacity, International Leadership, and Organizational Excellence. Table E.1 provides an overview of measures used by FAA and highlights whether or not the performance measures were met. Waterborne Freight Performance Measures MARAD produces an annual Statistical Snapshot19 that provides nearly 20 categories of water-freight-related statis- tics. The statistics address freight volumes, ports of entry and export, commodity trends, numbers of ships and containers involved, measures of trade, measures of employment in the industry, and measures of the economics of waterborne ship- ping. It is useful to assess general trends in port volumes and activity (Table E.2). Analogous to the highway mode, in which data exist for traffic volumes but not for highway performance to the same extent, there are little available data on port performance. Port volumes are measured, but information is not available as to how ports have accommodated the growth in container volume in past decades. In its Report to Congress on the Performance of Ports and the Intermodal System20 MARAD noted that a lack of common performance measures and the lack of a reporting process have stymied its attempts to measure the efficiency of major U.S. ports. It informed Congress that it was unable to assess congestion levels at ports or to assess the performance of the nation’s intermodal system overall: MARAD was unable to provide the requested comparison of the most congested ports in terms of operational efficiency due to a lack of consistent national port efficiency data. Given the diverse characteristics of U.S. ports, comparing port efficiency 8 Aviation Performance Measures The air transportation industry has been measure-intensive for decades, with both private carriers and the FAA carefully evaluating key measures of reliability, safety, and service. Annually, beginning in FY 2004, FAA developed an aggressive strategic plan to help manage and measure performance. Its Flight Plan provides the framework to match resources with i itiative for long-term change. This report sets forth goals and the performance measures to assess progress in meeting them and is tightly aligned with the mission, vision, goals, and performance measures outlined in the DOT Strategic Plan. The Flight Plan highlights performance measures, and conducts analysis on each measure to determine whether the data were complete and reliable enough to measure appropriately. Within this report performance measures are grouped in the broad categories of Safety, Capacity, International Leadership, and Organizational Excellence. Table E.1 provides an overview of measures used by FAA and highlights whether or not the performance measures were met. Table E.1. FAA statistics. Table 10 Measure Actual Target Data Data Index Range 10S1 Commercial Air Carrier Fatality Rate (FAA) - - Green 10S2 General Aviation Fatal Accident Rate (FAA) 0.0 8.1 Green 10S2 General Aviation Fatal Accident Rate (FAA) 1.09 1.02 Red 10S3 Alaska Accident Rate (FAA) 2.55 1.70 Red 10S4 Runway Incursions (Category A and B) (FAA) 0.12 0.45 Green 10S6 Commercial Space Launch Accidents (FAA) 0 0 Green 10S7 Operational Errors (FAA) 3.24 2.05 Red 10S59 Safety Management System (FAA) 3 3 Green 10S105 Total Runway Incursions (FAA) 409 446 Green GREATER CAPACITY 10 (FAA) - - Green 10C1 Average Daily Airport Capacity (35 OEP Airports) (FAA) 101.354 102,648 Yellow 10C2 Airport Average Daily Capacity (7 Metro Areas) (FAA) 42.494 Green 10C3 Annual Service Volume (FAA) 3 3 Green 39,484 table e.1. statistics.

127 9 Table 10 Measure Actual Target 10C4 Adjusted Operational Availability (FAA) 398.78 99.7 Green 10C5 NAS On-Time Arrivals (FAA) -99.78 88.00 Yellow 10C6 Noise Exposure (FAA) 089.69 3 Green 10C7 Aviation Fuel Efficiency (FAA) 3 3 Green INTERNATIONAL LEADERSHIP 10 (FAA) 3 - Green 10I2 CAST Safety Enhancements (FAA) 1- 4 Red 10I7 International Aviation Development Projects (FAA) 3 3 Green 10I23 NextGen Technology (FAA) 2 1 Green 10I40 Aviation Leaders (FAA) 1 1 Green ORGANIZATIONAL EXCELLENCE 10 (FAA) - - Green 10E2 Cost Control (FAA) 3 3 Green 10E3 Critical Acquisitions on Budget (FAA) 100 90 Green 10E4 Critical Acquisitions on Schedule (FAA) 96 90 Yellow 10E5 Information Security Program (FAA) 3 3 Green 10E6 Customer Satisfaction (FAA) 3 3 Green 10E61 OPM Hiring Standard (FAA) 3 3 Green 10E102 Reduce Workplace Injuries (FAA) 3 3 Green 10E104 Unqualified Audit Opinion (FAA) 2 3 Yellow 10E107 Grievance Processing Time (FAA) 3 3 Green 10E108 ATC Positions Workforce Plan (FAA) 15,812 15,639 Green 10E226 Continuity of Operations (FAA) 0 0 Green 10E231 Aviation Safety Positions Workforce Plan (FAA) 7 7,171 7,195 Green *STRATEGIC OBJECTIVES (FAA) 0 0 Green table e.1. FAA statistics. table e.1. Continued. Source: FAA 2010 Performance Targets, http://www.faa.gov/about/plans_reports/performance/quarter_scorecard/media/ FY10%202nd%20Quarter%20Scorecard.pdf. would require the creation of new methodologies and the collec- tion of data that were not available for this report. To generate its report for Congress on port performance, MARAD formed four teams of researchers who interviewed officials and representatives at 23 major U.S. ports. It stressed that, to assess port operations, it needed to interview port officials, port labor representatives, shippers, ship operators, and truckers, and it had to assess the infrastructure related to highways, rail, water, and the intermodal transfer points between the modes. The MARAD report noted a wide variety of issues—both operational and infrastructure related—that can influence efficient port operations:

128 table e.2. port volumes. Source: US Bureau of Census, Foreign Trade Division, www.census.gov/foreign-trade. 10 Source: FAA 2010 Performance Targets http://www.faa.gov/about/plans_reports/performance/quarter_scorecard/media/FY10%202nd%20Quarter %20Scorecard.pdf Waterborne Freight Performance Measures MARAD produces an annual Statistical Snapshot18 that provides nearly 20 categories of water-freight- related statistics. The statistics address freight volumes, ports of entry and export, commodity trends, numbers of ships and containers involved, measures of trade, measures of employment in the industry, and measures of the economics of waterborne shipping. It is useful to assess general trends in port volumes and activity (Table E.2). Table E.2. Port volumes. Port 2003 2004 2005 2006 2007 2008 % Change 2003-08 LA/LB 47.8 53.6 57.1 66.5 69.7 69.8 46 New York 22.1 23.6 26.8 27.8 29.9 31.9 44.3 Savannah 10.5 11.6 13.6 14.5 17.1 18.7 78.1 Houston 15.9 14.6 15.3 16.3 17.6 18.4 15.7 Seattle/Tacoma 12.6 14.5 18.3 17.6 18.9 17.9 42.1 Norfolk 10.2 10.1 10.9 11.9 12.3 12.9 26.5 S. Francisco 8.4 9.6 10.9 11.4 11.7 11.8 40.5 Charleston 9.9 10.8 12.1 11.2 11.3 10.9 10.1 Miami 7.7 8.5 9.7 9.3 8.8 8.3 7.8 N. Orleans 4.1 5.0 4.6 5.5 6.0 5.7 39.0 Top 5 109.0 117.8 131.0 142.7 153.2 156.7 43.8 Top 10 149.2 161.8 179.1 192.2 203.2 206.2 38.2 Total 174.0 187.6 205.8 220.6 231.6 235.1 35.1 Source: US Bureau of Census, Foreign Trade Division www.census.gov/foreign-trade Analogous to the highway mode, i which data exist for traffic volumes but not for highway performance to the same extent, there are little available data on port performance. Port volumes are measured, but information is not available as to how ports have accommodated the growth in container volume in past decades. The greatest concerns for both commercial operations and military deployments were the surge in cargo flows into the ports, the adequacy of cargo staging areas in the ports, port rail infrastructure, and communications. Additional issues that dominated commercial operations were landside access to ports, highway signage, channel and port dredging, increasing cargo volumes, financing, and intermodal connectivity. Two additional major concerns specific to military deployments were training and coordination among ports and shippers. While there were a wide variety of themes in response to MARAD’s questions, there was much agreement on the most ur- gent congestion and infrastructure issues facing the MTS [Mari- time Transportation System]. About half the ports cited specific reasons for congestion that cause infrastructure overload. One fourth of the ports described their infrastructure impediments as “severe.” The responses mirror the concerns raised in recent DOT, Government Accountability Office (GAO), and non-gov- ernment studies on MTS issues. MARAD advised Congress that, although a variety of potential port efficiency performance measures could be adopted, few of the potential measures had universal accep- tance because of the large diversity in port operations: In preparing this report, MARAD reviewed articles and stud- ies from the academic and scientific communities that set forth methodologies for measuring port efficiency. The literature re- viewed supported MARAD’s finding that there is no widespread agreement on an approach to measuring the efficiency of a port as a link in the logistics chain. A 2004 article in Maritime Policy & Management states: “Measures of port efficiency or performance indicators use a diverse range of techniques for assessment and analysis, but although many analytical tools and instruments exist, a problem arises when one tries to apply them to a range of ports and terminals. Ports are very dissimilar and even within a single port the current or potential activities can be broad in scope and nature, so that the choice of an appropriate tool of analysis is difficult. Organizational dissimilarity constitutes a se- rious limitation to enquiry, not only concerning what to measure but also how to measure. Furthermore, the concept of efficiency is vague and proves difficult to apply in a typical port organization extending across production, trading and service industries.21 MARAD listed the following considerations that influence a port’s efficiency and could skew an attempt to make com- parisons between ports: • Type of cargoes handled by the port (specialization); • Location of port relative to shippers’ markets (regional demand); • Price of port services relative to shippers’ alternative ports;

129 • Waterside access limitations; • Carrier investment in port infrastructure; • Quality of port services; • Business realignment to increase purchasing power; and • Availability of national government subsidies. MARAD noted “Factors That Affect Port Efficiency”: • Labor efficiency (cargo moved per unit of labor); • Land use efficiency (cargo storage per unit of land); • Waterside access limitations; • Capacity of port road and rail connections; • Inland transportation availability; and • Cargo handling capability. It went on to say that the diversity of factors prevents the general measurement of port efficiency. It quoted Cullinane22 as saying that there is even a lack of standard terminology between ports as to how define measures, with different ports using different terminology to describe similar functions. It quoted Robinson23 as saying that port efficiency measures “will always have a national tendency to be terminal specific.” It quoted De Monie24 as saying that the following factors impede measurement of port efficiency: • The sheer number of parameters involved; • The lack of up-to-date, factual, and reliable data, collected in an accepted manner and available for dissemination; • The absence of generally agreed and acceptable definitions; • The profound influence of local factors on the data ob- tained; and • The divergent interpretation given by various interests to identical results. Marad strategic plan and performance Measures MARAD has a strategic plan with embedded performance measures for the years 2008–2015.25 Its measures support its five basic strategic goals, which are: • Improve maritime policies and programs to enrich and se- cure the nation. • Expand reliable private and public investment funding mechanisms to support the growth of the Marine Trans- portation System. • Revitalize the partnerships between the Maritime Admin- istration and the Marine Transportation System’s private and public stakeholders. • Enhance the U.S. intermodal transportation system. • Maximize the potential of each employee to achieve the agency’s mission. Its Strategic Plan includes a cascading series of outcomes, strategies, key performance indicators, and performance measures. The performance measures are included in the agency budget documents and link expenditures with effec- tiveness. Two examples are the number of out-of-service ships that are dismantled in an environmentally sustainable way and the number of communities MARAD engages to enlist their help in improving the Maritime System. The MARAD measures evaluate internal agency performance and not the performance of ports, intermodal links, or actual shipments. Its more extensive lists of key performance indicators do relate to many aspects of national concern regarding ship- ping performance and security. However, it notes they are not quantitative, nor do they have a measurement system related to them. They are of a more qualitative nature. They include issues such as increased outreach to public and private sec- tors, increased private investment in the Maritime System and adoption of best practices in managing port facilities to maximize throughput. inland Waterways USACE’s Navigation Economics Technology Program26 produces a suite of analytic tools for the Corps to evaluate possible investments in the inland waterways system. It has produced a report, An Overview of the U.S. Inland Waterway System, that provides baseline information on the domestic inland system. It includes statistics on the size and charac- teristics of the waterways, locks, ports, and commodity flows on the system. The data are extensive but static and are not subject to regular updates. The Corps also produces a web- site with significant amounts of performance data regard- ing waterborne commerce and the conditions of locks and dams27 (Figure E.3). RITA produces in its Key Transportation Indicators monthly report a moving average of delay on the inland waterway system.28 Time series analysis of u.s. inland Waterways Trade BTS29 publishes monthly trend data on the shipment of commodities on U.S. inland waterways. Aggregate data are normalized to adjust for seasonal variations but do not pro- vide granularity as to types of commodities shipped, or by origins and destinations. european union Transport policy for 2010 The European Union has not adopted freight performance measures per se but it has adopted firm goals that arise from table e.2. port volumes. Source: US Bureau of Census, Foreign Trade Division, www.census.gov/foreign-trade.

130 its freight-transport policies. The EU example represents a clear case of performance goals selected specifically to achieve a formal, official transportation policy.30 Its 2001 White Paper proposed approximately 60 measures to develop a transport system capable of shifting the balance between modes by reducing the growth in truck freight, revitalizing rail trans- port, encouraging in-land and short sea shipping, and con- trolling the rate of growth in air travel. In a 2006 assessment of the 2010 White Paper goals, the EU noted mixed progress. Highway freight still carried 44 percent of freight tonnage, compared to 8 percent for rail and 4 per- cent for inland waterways. For passengers, 79 percent of travel was on roads, compared to 6 percent for rail and 5 percent for air. The number of cars trebled between 1970 and 2000 and continues to grow, particularly in the former Eastern Bloc members, which are rapidly developing. Between 1995 and 2004, highway freight grew 35 percent compared to 6 percent for rail freight.31 A recent European research program conducted for the Dutch Ministry of Transport, Public Works and Water Man- agement has shown that the EU so far has not succeeded in achieving its passenger mode-shift goals and has had only partial success on its freight goals. The Dutch study proposed a renewed emphasis on pricing to discourage highway travel and increase travel on rail and water modes. It also proposed the acceleration of biofuels and hydrogen to achieve air qual- ity and greenhouse gas emissions goals that have not been achieved so far by the mode-shift strategy.32 13 MARAD’s commercial performance measures include the number of short sea demonstration projects; the number of innovations in ship building technologies; the number of innovations in environmental impact prevention; the number of innovations in marine/land-based linkages; and the amount of private investment in ship building. Its performance measures for commercial mobility focus specifically on its internal accomplishment of activities to support commerce, as opposed to focusing on the actual operations of the maritime system. Its national security measures include the readiness of ships for military transport, the amount of training necessary to support military surge capabilities at ports, and the amount of port-security training. Its environmental measure was the number of obsolete vessels removed from the fleet. Inland Waterways USACE’s Navigation Economics Technology Program24 produces a suite of analytic tools for the Corps to evaluate possible investments in the inland waterways system. It has produced a report, An Overview of the U.S. Inland Waterway System, that provides baseline information on the domestic inland system. It includes statistic on the size and characteristics of the waterways, locks, ports, and commodity flows on the system. The data are extensive but static and are not subject to regular updates. The Corps also produces a website with significant amounts of performance data regarding waterborne commerce and the conditions of locks and dams25 (Figure E.3). RITA produces in its Key Transportation Indicators monthly report a moving average of delay on the inland waterway system.26 Figure E.3. Inland waterway volumes. Time Series Analysis of U.S. Inland Wa erways Trade Deleted: Its commercial performance measures include the number of short sea demonstration projects; the number of innovations in ship building technologies; the number of innovations in environmental impact prevention; the number of innovations in marine/land- based linkages; and the amount of private investment in ship building. Its environmental measure was the number of obsolete vessels removed from the fleet. Comment [JP8]: Author: Reorganized to correspond to the list above. They’re all about commercial performance measures. Author, please review rewrite and let us know if this works. Figure E.3. Inland waterway volumes. Endnotes 1 Beagan, Daniel, M. Fischer, and A. Kappam, Quick Response Freight Manual II, prepared for USDOT, FHWA, Office of Freight Management and Opera- tions, Washington, D.C., 2007. 2 Fekpe, Edward, M. Alam, T. Foody, and D. Gopalakrishna. Freight Analysis Framework Highway Capacity Analysis: Methodology Report, prepared for USDOT Office of Freight Management and Operations, Washington, D.C., 2002. 3 Battelle. Heavy-Duty Truck Activity Data Collection and Analysis Using Global Positioning Systems, prepared for USDOT, FHWA, Office of Highway Infor- mation Management, Washington, D.C., 1999. 4 Short, Jeffrey, and C. Jones, Utilization of Wireless Truck Position Data to De- termine Demand for Highways. 10th International Conference on the Appli- cation of Advanced Technologies in Transportation, Athens, Greece, 2008. 5 Mallet, William, C. Jones, J. Sedor, and J. Short. Freight Performance Measu- rement: Travel Time in Freight Significant Corridors (FHWA-HOP-07-071), Office of Freight Management and Operations, FHWA, U.S. DOT, Decem- ber 2006. 6 Turner, Shawn, T. Lomax, and H. Levinson. “Measuring and Estimating Congestion Using Travel Time-Based Procedures,” Transportation Research Record 1564, Transportation Research Board, National Research Council, Washington, D.C., 1996, pp. 11–19. 7 Lomax, Tim, David Schrank, Shawn Turner, and Richard Margiotta. Selecting Travel Reliability Measures, FHWA, http://tti.tamu.edu/docu- ments/474360-1.pdf, 2003. 8 FHWA. Traffic Congestion and Reliability: Linking Solutions to Problems. Pre- pared by Cambridge Systematics with the Texas Transportation Institute for FHWA Office of Operations, 2004. 9 FHWA, Traffic Congestion and Reliability: Linking Solutions to Problems, Cambridge Systematics and Texas Transportation Institute for FHWA Office of Operations, 2004. 10 Jones, Crystal, Daniel Murray, and Jeffrey Short. Measurement of Travel Time in Freight-Significant Corridors: Phase Two, presented at 12th Annual World Congress on ITS, San Francisco, CA, November 6–10, 2005. 11 ATA. Motor Carrier Annual Report, Arlington, VA, 2003. 12 ATA. American Trucking Trends 2007-2008, Arlington, VA, 2008. 13 FMCSA. Large Truck Crash Facts 2005. Washington, D.C., 2007. 14 STB. Waybill data, http://www.stb.dot.gov/stb/industry/econ_waybill.html (accessed Sept. 30, 2008). 15 STB Decision Ex Parte No. 552 (Sub-No.12) Railroad Revenue Ade- quacy—2007 Determination, Decided: September 24, 2008. http://www. stb.dot.gov/decisions/ReadingRoom.nsf/UNID/07DBA8356B2F41F685257 4D00047B59E/$file/39363.pdf.

131 16 STB, Statistics of Class I Freight Railroads, http://www.stb.dot.gov/econdata. nsf/66a333195e0491c885256e82005ad319?OpenView (accessed Sept. 30, 2008). 17 STB Decision Ex Parte No. 552 (Sub-No.12) Railroad Revenue Ade- quacy—2007 Determination, Decided: March 24, 2010. http://www.stb.dot. gov/decisions/ReadingRoom.nsf/UNID/07DBA8356B2F41F6852574D0004 7B59E/$file/39363.pdf. 18 FRA Office of Safety Analysis, http://safetydata.fra.dot.gov/officeofsafety/ (accessed March 24, 2010). 19 MARAD. U.S. Water Transportation Statistical Snapshot, 2008. 20 MARAD, Report to Congress on the Performance of Ports and the Intermodal System, June 2005. 21 MARAD, Report to Congress on the Performance of Ports and the Intermo- dal System, June 2005, p. 7. 22 Wang, T.-.F., D.-W. Song, and K. P. B. Cullinane. The Applicability of Data En- velopment Analysis to Efficiency Measurement of Container Ports, presented at the International Association of Maritime Economists, Panama Conference, November 2002, p. 6. 23 Robinson, D. Measurements of Port Productivity and Container Terminal De- sign: A Cargo Systems Report, IIR Publications, London, 1999. 24 De Monie, G. Measuring and Evaluating Port Performance and Productivity, Monograph No. 6, UNCTAD Monographs on Port Management, UN Con- ference on Trade and Development, Geneva, Switzerland, 1987. 25 MARAD. Strategic Plan for Fiscal Years 2003–2008, 2008. 26 USACE, Navigation Economic Technologies Program. An Overview of the U.S. Inland Waterway System, IWR Report 05-NETS-R-12, 2005. 27 U.S. Army Corps of Engineers, “Publications from the Navigation Data Center,” http://www.ndc.iwr.usace.army.mil/publications.htm (accessed May 24, 2010). 28 RITA. Key Transportation Indicators, April 2010. https://www.bts.gov/pu- blications/key_transportation_indicators/april_2010/index.html (accessed May 24, 2010). 29 BTS. A Time Series Analysis of U.S. Inland Waterways Trade, 2008, https:// www.bts.gov/publications/transportation_indicators/december_2002/Spe- cial/html/A_Time_Series_Analysis_of_US_Inland_Waterways_Trade.html (accessed Sept. 29, 2008). 30 European Union. “White Paper: European Transport Policy for 2010: A Time to Decide,” 2001. 31 European Union, “Keep Europe Moving Sustainable Mobility for Our Conti- nent (Mid-term review of the European Commission’s 2001 transport White Paper),” 2006, p. 10. 32 J. Annema, “Effectiveness of the EU White paper: ‘European Transport Policy for 2010’ 2005 for the Dutch Ministry of Transport, Public Works and Water Management. Figure E.3. Inland waterway volumes.

Next: Appendix F - Environmental Freight Performance Measures: State of Practice »
Performance Measures for Freight Transportation Get This Book
×
 Performance Measures for Freight Transportation
Buy Paperback | $63.00
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

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.

READ FREE ONLINE

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!