Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 53
53 Case Studies gram managers believe that MPOs, the state DOTs, and private- sector users have regularly consulted the data. They The complexities of data integration and of addressing believe that private-sector firms such as GE, UPS, FedEx, data deficiencies were clearly evident in the development of and Wal-Mart have used it to help determine the location two representative freight information systems, the FAF and of warehouses and assembly sites and to choose shipping the TSI. Although neither are performance measurement routes. systems, both provide comprehensive data regarding freight Although the FAF data provide unprecedented new insight volumes, origins, destinations, and other trend information. into the national freight network, FAF is not now scalable The level of effort that was necessary for these two systems down to the local level. FAF is focused on the national and provides an order-of-magnitude example of the complexities regional aspects of freight movement. It does not capture facing the development of a comprehensive freight perfor- movement less than 50 miles and was not designed to pro- mance measurement system. vide a local perspective. The managers of the FAF program said that augmenting the FAF data for local granularity would be very data intensive and probably expensive. The FAF pro- Freight Analysis Framework Case Study gram managers say they do not anticipate scaling the data The Freight Analysis Framework integrates data from a down to the local level. variety of sources to estimate commodity flows and related The FAF program incorporates data from the following freight transportation activity among states, regions, and data systems: major international gateways. The first version of FAF pro- vides estimates for 1998 and forecasts for 2010 and 2020. Commodity Flow Survey: This is a domestic shipper survey The second version provides estimates for 2002 and the most conducted by the U.S. Census Bureau. It has origin/destina- recent year plus forecasts through 2035. tion data for manufacturing, mining, and agriculture sectors. The FAF Commodity Origin-Destination Database esti- It is conducted every five years. The last one was conducted in mates tonnage and value of goods shipped by type of com- 2007. The survey seeks sample data from shippers randomly modity and mode of transportation among and within 114 identified from federal tax files. areas, as well as to and from seven international trading regions Vehicle Inventory and Use Survey (VIUS): This survey was throughout the 114 areas plus 17 additional international gate- conducted by the U.S. Census Bureau. Last done in 2002, it ways. The 2002 estimate is based primarily on the Commodity collected information about trucks to be used to compute Flow Survey and other components of the Economic Census. and calibrate tonnage for various products. The data will be Forecasts are included for 2010 to 2035 in five-year increments. analyzed and incorporated into FAF. Officials of FAF report that at present the effort requires Highway Performance Management System: These data one full-time U.S. DOT staff person and two full-time con- are obtained from state DOTs that collect data from samples sultants. Both Battelle Memorial Institute and the Oak Ridge of roadways statistically selected annually. The data address National Lab support the ongoing FAF efforts. information about the performance, use, and operating char- The initial FAF setup cost was about $1 million and was acteristics of U.S. highways. spent on acquiring private data. Because there were privacy Vehicle Travel Information System (VTRIS): This annual issues with the data, the detailed analysis and input/output update provides data about the number of trucks weighed, data could not be shared with users. The next phase cost weight by vehicle type, and the classification of vehicles mov- $600,000 and was a two-year effort focused on construct- ing on the U.S. highway system. This information is used for ing models. This allowed the agency to share the commodity calibration of tonnage of freight moved. data with users. The system captures data from the Bureau Transborder Surface Freight Data: This information gives of Transportation Statistics (BTS), the Federal Aviation North American trade data by commodity and mode. The Administration, the U.S. Army Corps of Engineers, and the data include imports and exports to and from Canada and Energy Information Association, as well as trans-border U.S. Mexico. This is updated monthly and annually. Customs Service data, census data, and foreign trade data. Waterborne Domestic and Foreign Commerce: This is Private-sector data come from ATA and AAR. FAF captures domestic information updated annually and foreign trade only "for hire" shipping and does not capture shippers who information updated monthly from USACE. use internal fleets, such as Wal-Mart and others who trans- Oil Pipeline: Oil movement data by multistate region are port their own goods. obtained from the Energy Information Administration. There are no precise data available about who uses the Air Traffic Statistics: Air traffic, tonnage, and revenue ton- FAF data and how frequently. From experience, the pro- mile data are obtained from carriers quarterly from the FAA.
OCR for page 54
54 The managers of the FAF program say their experience (VIUS). FAF1 used private data that could not be shared with holds significant lessons for development of a freight per- users looking for input and output data. This lack of publicly formance measurement process. They acknowledge current available data led to FAF2. uncertainties about roles and responsibilities and a lack of The measures were derived from FAF1 modeling that could clarity about the role of federal, state, and local agencies in be accomplished by using data that could be made public. providing data. In several states the relationship between The FAF data can play a significant role in monitoring and the state and the local agencies is contentious. The authority evaluating the nation's freight system. FAF provides informa- and responsibility are tied to the availability of funds and the tion about the volume and value of freight flow in the United agency controlling the funds. A lack of clarity on roles, cou- States, and it provides information about the network over pled with shortage of funds and lack of publicly available data which the freight moves, as shown in Figures 6.1 and 6.2. The Waterborne at various points of the network, makes Domestic it and difficult Foreign to have Commerce Waterborne Domestic and Foreign Commerce : : This snapshotThis is ofdomestic is information information domestic updated it provides information annually can updated and be compared annually and across foreign an integrated approach to national trade freight information performance updated mea-monthly from years USACE. and foreign trade information updated monthly from USACE. across the network to provide information about sures, they indicated. Fund shortages Oil have led to the cancel- the performance of freight movement, quantities moved, and Oil Pipeline Pipeline:: Oil Oil movement movement data data by by multistate multistate region region are are obtained obtained from from the the Energy Energy Information Information lation of funding for the Vehicle Inventory and Use Program Administration. Administration. revenue generated. It also provides information about speed, Air Air Traffic Traffic Statistics Statistics:: Air Air traffic, traffic, tonnage, tonnage, and and revenue revenue ton-mile ton-mile data data are are obtained obtained from from carriers carriers quarterly quarterly from from the the FAA. FAA. The The managers managers of of the the FAF FAF program program say their experience say their experience holds holds significant significant lessons lessons for for development development of a freight of a freight performance performance measurement measurement process. process. They They acknowledge acknowledge current current uncertainties uncertainties about about roles roles and and responsibilities responsibilities andand aa lack lack of clarity about the role of of clarity about the role of federal, federal, state, state, and and local local agencies agencies inin providing providing data. data. In In several several states states the the relationship relationship between between the the state state and and the the local local agencies agencies isis contentious. contentious. TheThe authority authority and and responsibility responsibility Figure 6.1. FAF can be used to understand some aspects of freight Figure 6.1. FAF can be used to understand some aspects of freight are are tied tied to to the the availability availability ofof Figure 6.1. Example of FAFanddata useful of for assessing funds movement movement such such asas relative relative volumes volumes and destinations destinations of freight freight flows flows from from funds and and thethe agency agency freight locations,movement. locations, in in this this case case Missouri. Missouri. controlling controlling thethe funds. funds. AA lack lack of of clarity clarity onon roles, roles, coupled coupled with with shortage shortage of of funds funds and and lack lack of of publicly publicly available available data data at at various various points points of of the the network, network, makes makes it it difficult difficult to to have have an integrated approach to an integrated approach to national national freight freight performance performance measures, measures, theythey indicated. indicated. Fund Fund shortages shortages have led to have led to the the cancellation cancellation of of funding funding for for the the Vehicle Inventory and Vehicle Inventory and Use Use Program Program (VIUS). (VIUS). FAF1 FAF1 used used private private data that could not data that could not be be shared shared with with users users looking looking for for input input and and output output data. data. This This lack lack of publicly available data of publicly available data led led to to FAF2. FAF2. Figure Figure 6.2. 6.2. FAF FAF illustrates illustrates California's California's import import volumes. volumes. The measures were derived The measures were derived Figure 6.2. FAF illustration of California's import volumes. 7 7
OCR for page 55
55 reliability, and congestion of movement of freight through the process was streamlined and staff resources were reduced the nation. It does not provide geographic or temporal gran- to five federal employees and two contractors. ularity. In other words, it is annualized data available at the The products delivered by TSI are: state and national level, not the local level. · Freight Index · Passenger Index Transportation Services Index · Combined (Total) Index The Transportation Services Index (TSI) was created by the USDOT Bureau of Transportation Statistics (BTS), and it The process of refining the data and integrating it to pro- measures the movement of freight and passengers nationally. vide the three different indexes involves many detailed steps. The index, which is seasonally adjusted, combines available Those include: data on freight traffic, as well as passenger travel, that have been weighted to yield a monthly measure of transportation Data Gathering services output (Figure 6.3). The BTS staff gather monthly data for each mode of trans- The TSI is a monthly measure of the volume of services portation from a range of government and private sources performed by the for-hire transportation sector. The index (Table 6.1). covers the activities of for-hire freight carriers, for-hire pas- senger carriers, and a combination of the two. The TSI has Forecasting been active since 2002 but is still under development and is Some data series were not complete through December therefore experimental. It is being examined for refinements 2003, the ending date through which the original TSI was in data sources, methodologies, and interpretations. published. Therefore, staff needed to forecast the one or The TSI provides insight into how the output of trans- two missing months, using a statistical technique known as portation services has increased or decreased from month an autoregressed moving average. As production of the TSI to month. The index can be examined together with other continues, the need to forecast missing data will be reduced. economic indicators to produce a better understanding of the However, it is not uncommon in indexes of this type for current and future course of the economy. The movement of monthly data to be delayed because of reporting or other the index over time can be compared with other economic problems and for preliminary data to be substituted. measures to understand the relationship of transportation to long-term economic changes. Deseasonalizing The managers of the TSI note that it is the broadest mea- Because the principal purpose of the index is to reflect sure of U.S. domestic transportation output. The project monthly shifts in transportation services output and to started with a grant from BTS in 2002 and was brought in- analyze short-term trends, it is essential that it be adjusted house that same year. The first official release of TSI occurred for the normal seasonal changes that affect the transporta- in March 2004. Initially the project had 22 staff and several tion sector. Transportation is highly seasonal, and without consultants working on the project. Over the course of time adjustment the index would not give an accurate picture T ra n s p o rta tio n S e rv ic e s In d e x F reight Index P as s enger Index 130 120 110 100 90 80 70 60 S e -0 0 S e -0 5 S e -9 0 S e -9 5 No 0 5 No 0 0 No 9 0 No 9 5 20 07 M 02 M l -9 2 M l -9 7 J a 04 J a 94 J a - 99 J 6 Ju 1 Ju 6 Ju 1 M -0 3 M -9 3 M -9 8 0 9 9 0 P a y u l- - n- n- n- n- - - p- p- p- p- v v v v l 08 ar ar ar ay ay ay Ja M Figure 6.3. TSI trends. Figure 6.3. TSI trends. Data Gathering The BTS staff gather monthly data for each mode of transportation from a range of government and private sources (Table 6.1).
OCR for page 56
Value added is used for consistency with other indicators that are used in relation to GDP, for example, 56 industrial production. By using value added, rather than gross revenues, for each sector, they seek to avoid double counting inputs (i.e., diesel fuel) to the transportation sector. Table Table6.1.TSI source 6.1. TSI source data. data. MEASURE MODE SOURCE Freight TSI Trucking American Trucking Association Air BTS and Carrier Websites Rail Association of American Railroads Water US Army Corps of Engineers Pipeline Energy Information Administration Passenger TSI Air Bureau of Transportation Statistics and carrier websites Rail Federal Railroad Administration Transit American Public Transportation Association Source: Source: US Department U.S. Department of Transportation, of Transportation, Research Research and Innovative and Innovative Technology Technology Administration, Bureau of Administration, Transportation Bureau of Transportation Statistics Statistics. Because value-added data is available from the Bureau of Economic Analysis on an annual basis only, weights of underlying changes in are determined annually transportation andBTS output. applied hasthroughout the year. duction. Valued By using added value reflects added, the volume rather than of gross revenues, for physical transportation as well as the value of that volume. Because they have already measured monthly therefore deseasonalized the data using standard statistical each sector, they seek to avoid double counting inputs (i.e., changes in that volume, it is necessary to ensure that changes in volume are not double-counted in the methodologies. diesel fuel) to the transportation sector. process of adjusting the weights for the index. This is accomplished through a mathematical process Because value-added data is available from the Bureau of called chaining, which follows standard methodologies established by the U.S. Census Bureau for similar Indexing indexes. Economic Analysis on an annual basis only, weights are deter- While physical measures are gathered for each mode, ulti- mined annually and applied throughout the year. Valued The "For-Hire Only" freight data are mately for combination and analysis, the data from the dif- collected for all five modes: trucking, air, rail, water, and pipeline. added reflects the volume of physical transportation as well as Passenger data include air, rail, and transit. ferent modes must be converted into an index. BTS uses 1996 the value of that volume. Because they have already measured as the base year andAs with the indexes byFAF data, the dividing theproducers of the index do not current monthly have statistics monthly changes on inwho that uses the data volume, ornecessary it is for what to ensure that value by the averagepurposes. value forAnecdotally, they know the 12 months the TSI is used bychanges of 1996. Wall Street in as a general volume areindicator of the economy. not double-counted in Itthe process of adjusting the weights for the index. This is accomplished Weighting and Chaining through a mathematical process called chaining, 10 which fol- The final step in creation of the index is combining the lows standard methodologies established by the U.S. Census individual mode indexes into the three summary indexes: Bureau for similar indexes. the freight index, the passenger index, and the overall, or com- The "For-Hire Only" freight data are collected for all five bined, TSI. The weighting is based on the relative economic modes: trucking, air, rail, water, and pipeline. Passenger data value added of each mode. Not all ton-miles are equivalent include air, rail, and transit. in their economic importance, nor are all passenger-miles. As with the FAF data, the producers of the index do not For example, the average price paid per ton-mile for freight have statistics on who uses the data or for what purposes. moved by rail is less than the average price paid per ton-mile Anecdotally, they know the TSI is used by Wall Street as a for freight shipped by truck because of differences in factors general indicator of the economy. It is used to evaluate the such as haul length, shipment volumes, and resultant econo- performance of the transportation sector by stock analysts. mies of scale. By using an economic measure for weighting, It is used as a forecaster of the economy. Companies such as the TSI staff recognizes these differences and makes the index Global Insight use this information as a factor in their analysis more valuable as a transportation measure that can be used to provide economic projection and forecasting information together with other economic measures, such as GDP. to clients such as GE and Wal-Mart. It is used by companies such as AllianceBernstein to provide research and information Value added is used for consistency with other indicators on investment related to services globally. This information is that are used in relation to GDP, for example, industrial pro- also published on the White House website.