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Publication Number:  FHWA-HRT-06-005    Date:  July/August 2006
Publication Number: FHWA-HRT-06-005
Issue No: Vol. 70 No. 1
Date: July/August 2006


Improving The Reliability of Freight Travel

by Crystal Jones and Joanne Sedor

A new FHWA initiative applies vehicle-monitoring technologies to develop performance measures for the movement of goods around the Nation.

Speedy but safe movement of freight on the highway system has grown in importance to the Nation's economy. To ensure reliable freight movement, trucks like this one sometimes need to continue traveling despite poor weather conditions. Photo: AAA Foundation for Traffic Safety.
Speedy but safe movement of freight on the highway system has grown in importance to the Nation's economy. To ensure reliable freight movement, trucks like this one sometimes need to continue traveling despite poor weather conditions. Photo: AAA Foundation for Traffic Safety.

American businesses and house-holds -- indeed the entire economy -- depend on the reliable movement of goods. U.S. freight carriers moved more than 19 billion tons (17 billion metric tons) of products in 2002, worth some $13 trillion. Trucks carried about 60 percent of the tonnage and nearly 70 percent of the value. As the demand for goods and services increases, so will the amount of truck traffic on the Nation's highways.

Manufacturers, distributors, retailers, and fleet operators consider reliability and other characteristics of the transportation network when choosing the best method to deliver raw materials to the factory and finished goods to customers. Reliability also influences the answers to logistics questions such as: How many distribution centers do I need? Where do I locate them? What inventory should they contain?

Research on the trucking industry shows that shippers and carriers cost out transit time at $25 to $200 per hour, depending on the product being moved. Unexpected delays can increase that cost by 50 to 250 percent. With the recent adoption of just-in-time management approaches, manufacturers and distributors are even more dependent on efficient shipment of goods, making a reliable freight transportation system that much more indispensable.

The U.S. Department of Transportation (USDOT) recognizes that timely and reliable movement of freight is critical to the Nation's economy. In its March 2005 report, Research Activities of the Department of Transportation: A Report to Congress, USDOT named global connectivity as one of the department's top priorities for research, development, and technology to "facilitate a more efficient domestic and global transportation system that enables economic growth and development."

Freight Stakeholders and Their Primary Interests
Role or Perspective Objectives Primary Areas Of Interests Measurements
Shipper Deliver products to customers on time, in the right quantity, and undamaged Speed, reliability, security, visibility Fill rate: the percentage of orders delivered "on time," that is, no later than the delivery day requested by the customer
Carrier Achieve on-time delivery Reduce total annual operating costs Enhance driver productivity Business profitability, return on investment Delay: actual delivery day minus confirmed delivery day
Transportation System Operators Maintain a safe and reliable transportation system Provide affordable, accessible, and dynamic transportation systems responsive to current and future customers Enhance system efficiency and intermodal connectivity Congestion, mobility, safety, security, economic development Travel time: the time it takes for vehicles to travel between two points Delay: delay per person and delay per vehicle
Source: FHWA

Within the Federal Highway Administration (FHWA), the Office of Freight Management and Operations is charged with advancing programs, policies, and initiatives that improve the Nation's highway freight system. Recently the FHWA Freight Office launched the Freight Performance Measurement (FPM) initiative to monitor and measure the performance of the freight system. The goal of the project is to determine how effectively the surface transportation network is accommodating the increasing demand for reliable freight movement and meeting the demands and expectations of its users.

Federal, State, and local agencies have long struggled with the difficulties of measuring performance. Among the many factors that limit the ability of public transportation agencies to implement successful systems for measuring performance are the following:

  • Diverse and contradictory objectives, such as the social, economic, mobility, and environmental objectives expressed by transportation decisionmakers and those noted by transportation system users and the community
  • Intangible products and services, such as providing an efficient and seamless transportation system or increasing the accessibility and mobility of people and freight
  • Unreliable measurement tools, such as speed sensors or wire loops buried in the pavement, which may not be available in all areas or may be broken or inoperable
  • Lack of resources and data, such as staff, expertise, and funding to collect new data and successfully apply available data

One potential remedy for the lack of data available for measuring the performance of the freight transportation network is to leverage information produced by automatic vehicle location (AVL) technology installed onboard commercial vehicles, specifically data on location and time. The freight industry uses AVL technology to enhance the "visibility," or tracking, of cargo as it moves from supplier to customer and to improve the availability of information on shipment status. Vehicle-monitoring technologies, such as AVL, increase shippers' confidence that goods will be delivered on time and in the right quantity.

One of the greatest advantages of using AVL data for performance measurement is that it enables the collection of feedback from system users (freight carriers) in real time. Collecting data in real time is likely to produce a more accurate performance evaluation than collecting data on service performance after the fact, such as asking drivers to recall their experiences in a survey, or estimating performance using a transportation model.

Determining Performance Measures

According to Lance A. Neumann, president of Cambridge Systematics, Inc., and chair of the Transportation Research Board's Performance Measurement Committee, one of the most difficult challenges faced by transportation agencies is identifying freight performance measures that (1) are in areas the public sector can influence and (2) are meaningful to freight stakeholders in the private sector. Neumann spent the past decade working with Federal, State, and regional agencies on measuring performance and improving the ability of these organizations to define goals and establish greater accountability in their programs and services.

"Perspectives on system performance and the performance measures that may be of most interest and relevance vary depending on your role," Neumann told attendees during a recent presentation on measuring freight performance. This is particularly true for users, shippers, and carriers of freight versus owners and operators of the system, such as State departments of transportation (DOTs), metropolitan planning organizations (MPOs), and others involved in operating public infrastructure.

"Similarly, depending on the geographic scale of the issue examined, the kinds of measures you may want to look at will vary," Neumann said. Deriving a list of measures that would be interesting, relevant, and useful may be relatively easy, he added, but "the measures that we can actually use will always be affected by the data that are available and the tools that are available to use those data." In other words, a measure without supporting data is just a measure.

After examining potential measures that public sector operators can influence and that are meaningful to freight users in the private sector, FHWA found speed and buffer time index (travel time reliability) to be among the best. Shippers (manufacturers, distributors, and retailers who have to move goods) cited velocity and reliability among their primary measures of interest. Carriers (who do the actual moving for shippers) are most interested in profitability and return on investment, both of which heavily depend on their ability to provide reliable service to their customers. (To date, the most prevalent use of the buffer time index and other reliability indices has been in the area of monitoring urban mobility. For more information on buffer time index and travel time reliability, visit www.ops.fhwa.dot.gov/perf_measurement/reliability.htm.)

FPM Study Corridors
Interstate Route State Total Miles Selected Cities Served (Population larger than 5,000)
I-5 California 796.53 San Diego, Los Angeles, Stockton, Sacramento, Red Bluff, Anderson, Redding, Eureka
Oregon 308.14 Ashland, Medford, Grants Pass, Roseburg, Eugene, Salem, Portland
Washington 276.62 Vancouver, Kelso, Chehalis, Centralia, Olympia, Tacoma, Seattle, Everett, Mt. Vernon, Bellingham
TOTAL 1,381.29  
I-10 California 242.54 Santa Monica, Los Angeles, Pomona, Ontario, San Bernardino, Beaumont, Banning Indio, Blythe
Arizona 392.33 Phoenix, Casa Grande, Tucson
New Mexico 164.27 Lordsburg, Deming, Las Cruces
Texas 881 El Paso, Ft. Stockton, Kerrville, San Antonio, Houston, Beaumont, Orange
Louisiana 274.42 Lake Charles, Lafayette, Baton Rouge, Kenner, New Orleans
Mississippi 77.19 Gulfport, Biloxi, Pascagoula
Alabama 66.31 Mobile
Florida 362.28 Pensacola, Tallahassee, Jacksonville
TOTAL 2,460.34  
I-45 Texas 284.91 Galveston, Texas City, Houston, Huntsville, Corsicana, Ennis, Dallas
I-65 Alabama 367.00 Mobile, Greenville, Montgomery, Clanton, Birmingham, Cullman, Decatur, Athens
Tennessee 121.71 Nashville
Kentucky 137.32 Bowling Green, Elizabethtown, Louisville
Indiana 261.27 New Albany, Seymour, Columbus, Franklin, Indianapolis, Lebanon, La Fayette, Gary
TOTAL 887.30  
I-70 Utah 232.15 Cove Fort, Richfield, Green River
Colorado 451.04 Grand Junction, Denver
Kansas 424.15 Goodland, Hays, Russell, Salina, Abilene, Junction City, Topeka, Lawrence, Kansas City
Missouri 251.66 Kansas City, Boonville, Columbia, St. Louis
Illinois 135.94 East St. Louis, Vandalia, Effingham
Indiana 156.6 Terre Haute, Indianapolis, Richmond
Ohio 225.6 Springfield, Columbus, Zanesville, Cambridge
W. Virginia 14.45 Wheeling
Pennsylvania 167.92 Washington, Monessen-Charleroi, Breezewood
Maryland 93.62 Hancock, Hagerstown, Frederick, Baltimore
TOTAL 2,153.13  
Source: FHWA.

Meeting the Data Challenge

Having chosen speed and travel time reliability as appropriate measures of global connectivity, FHWA's next step was to identify a dependable source of measurement data. The FHWA researchers selected AVL, which also is known as fleet tracking, satellite tracking, vehicle tracking, asset tracking, cargo tracking, or global positioning system (GPS) tracking. Trucking fleet operators commonly use the AVL technology to track their vehicles throughout the course of delivering products. Other operators use it for more sophisticated purposes, including granting customers access to information on shipment locations, monitoring the mechanical performance and security of the fleet vehicles during transit, and conducting dynamic vehicle routing and scheduling.

Public sector agencies such as the U.S. Department of Homeland Security also are exploring the use of AVL technology to meet their missions and needs. For example, in 2005, the Transportation Security Administration announced a Hazmat Truck Security Pilot to test near real-time tracking and identification systems, theft detection and alert systems, motor vehicle disabling systems, and systems to prevent the unauthorized operation of trucks and unauthorized access to their cargos.

In 2002, FHWA and the American Transportation Research Institute (ATRI), with help from technology vendors and commercial carriers, successfully demonstrated that location and date-time data from AVL technologies could be used to derive measures of speed and travel time reliability. To date, the FHWA research effort under the FPM program has focused on measuring speed and travel time reliability on five freight-heavy corridors, namely I-5, from California to Washington; I-10, from Florida to California; I-45, in Texas; I-65, from Alabama to Indiana; and I-70, from Maryland to Utah.

Speed and BTI (Reliability) by Corridor (January-September 2005)
Corridor Average Speed (mi/h) Buffer Time Index (BTI)
I-5 49.70 19.38
I-10 55.86 20.63
I-45 54.05 29.95
I-65 57.66 6.92
I-70 54.30 10.78
The table shows average speeds and buffer time indices (BTI) for five interstate corridors based on preliminary results. For the given data, I-65 would be considered the most reliable with a BTI of 6.92. To better understand reliability, however, researchers would need to consider sources of variation in the system, including incidents, weather, and work zones. Source: FHWA.

The first step in setting up the FPM effort was to obtain permission from motor carriers to use location reports from their trucks' AVL equipment. Confidentiality was crucial to the subsequent agreement FHWA developed with the carriers and the technology vendor to allow access to their data. Using the FPM's satellite-based method for collecting data, the specific location of vehicles is determined at regular, predetermined time intervals using latitude and longitude positioning. The locations are stamped with a time, date, and vehicle identification number. To ensure confidentiality, vehicles are randomly assigned identification numbers so specific carriers cannot be identified. Using location and time data, the FPM project team could derive the speed of a vehicle traveling between two or more points.

Results and Findings

The FHWA-ATRI research team used the measured vehicle speeds for the five corridors to calculate a buffer time index (BTI), which is a measure of reliability that indicates how much extra time should be allowed for a trip due to variations in the system caused by weather, incidents, and work zones.

Although FHWA's primary focus for the FPM initiative is to derive speed and travel time reliability measures for the purpose of monitoring the performance of the national freight system, these data also could be segregated in a variety of other formats that are useful to a wider audience of stakeholders. For instance, State DOTs could use FPM data and results to analyze a corridor or stretch of road between two cities.

Further, State or Federal researchers could look at data collected along a travel corridor during a specific period of time. For example, the FPM project team created a map depicting the transportation situation that resulted from Hurricane Katrina after it struck the gulf coast on August 29, 2005. Based on data collected September 1-7, the map shows that the freight system along I-10 was not completely shut down the week after Katrina. "This can be attributed to the fact that much of the I-10 corridor was restored in a short time period," says Jeffrey Short, a senior ATRI researcher on the FPM project, "and that alternative routes exist in areas that were severely damaged."

Short adds that other factors influenced mobility on I-10, including a lack of other vehicles on the roadway (the area was designated a disaster zone and thus a place to avoid, and only authorized vehicles carrying aid were likely to be in the area in the wake of the storm), gas shortages, and prior evacuations of people from the area. Moreover, a driver taking a shipment from Jacksonville, FL, to Phoenix, AZ, during that time period would likely have chosen routes far north of I-10. "Therefore," Short says, "even greater capacity would in theory be freed up for those trucks [on I-10] measured by the FPM project team."

Generated from FPM data, this map shows truck travel times along I-10 for September 1-7, 2005, just after Hurricane Katrina hit the gulf coast. The color-coded map indicates I-10 was far from shut down to freight traffic. Indeed, the average speed between Mobile, AL, and New Orleans, LA, was 54-60 miles (87-97 kilometers) per hour. Speeds were slower from New Orleans to Baton Rouge, LA, about 18-35 miles (29-56 kilometers) per hour. The map suggests that a fairly quick recovery of I-10 in the aftermath of the hurricane and a lack of other vehicles on the roadway were the leading reasons freight moved more quickly than might be expected. Credit: Source: FHWA/ATRI.
Generated from FPM data, this map shows truck travel times along I-10 shortly after Hurricane Katrina hit the gulf coast on August 29, 2005. The map illustrates how researchers can segregate and present data for a corridor for a specific period of time. Source: FHWA/ATRI.

According to Short, the mapping capability, in general, is a value-added product that FHWA can use to illustrate how the freight transportation network is performing based on user-defined parameters. "The detailed maps that can be produced by the FPM system offer transportation system planners a range of support from granular data to macro-level corridor observations," he says.

Research Benefits And Applications

According to Director Tony Furst of FHWA's Office of Freight Management and Operations, the future of the FPM initiative looks promising. FHWA researchers are able to obtain data in a cost-effective way to support the need for performance measurement.

This bar graph illustrates, month-by-month for the first 8 months of 2005, the average speed of truck travel on the Missouri portion of I-70 versus average speeds on the rest of I-70 in its 10 other States. The x-axis (horizontal) indicates the months January through August, and the y-axis (vertical, on the left) indicates travel times in miles per minute. Thus, the graph shows that in January trucks on I-70 in Missouri traveled on average 1.094 miles (1.761 kilometers) per minute, slower than the 1.113 miles (1.791 kilometers) per minute in the other 10 States. In February, Missouri I-70 trucks traveled on average 1.095 miles (1.762 kilometers) per minute, slower than the 1.102 miles (1.773 kilometers) per minute for trucks on the remainder of I-70. Little changed in March, when Missouri I-70 trucks still moved at 1.095 miles (1.762 kilometers) per minute, and the average elsewhere was 1.103 miles (1.775 kilometers) per minute. Missouri I-70 trucks slowed down a bit in April and May, to 1.088 miles (1.751 kilometers) and 1.085 miles (1.746 kilometers) per minute, respectively. Speeds dropped off in the other 10 States as well, to 1.101 miles (1.772 kilometers) per minute in April and 1.100 miles (1.770 kilometers) per minute in May. In the summer, however, traffic speeded up, with trucks in Missouri moving on I-70 at 1.115 miles (1.794 kilometers) per minute in June, 1.118 miles (1.799 kilometers) per minute in July, and 1.104 miles (1.777 kilometers) per minute in August. Outside Missouri, trucks moved more slowly, at 1.113 miles (1.791 kilometers) per minute in June and July and 1.103 miles (1.775 kilometers) per minute in August. Credit: Source: MoDOT.
This bar graph shows the performance-in terms of average travel time for trucks-of the Missouri portion of I-70 relative to the average of all 10 States through which I-70 runs. Source: MoDOT.

In the next phase of work, now underway, FHWA is partnering with eight States to conduct case studies assessing how FPM data could be used to support freight planning, performance management, and other functions within State and local transportation agencies. The case studies will focus on answering the following questions:

  • How could FPM data be used to assist States in developing strategies to manage surface transportation congestion?
  • Would a long-term FPM program be a useful tool to inform decisionmaking with regard to both public and private freight investments?
  • Do FPM data enable States to analyze the performance of the transportation network?
  • What are current freight performance measures and practices at the State and local levels?
  • Does a relationship exist between FPM and existing State-level performance measurement?
  • Can FPM data contribute to States' efforts to inform their constituents and stakeholders of the importance of freight movement to their economy?
Speed and BTI (Reliability) Between Pairs of Cities on I-65
Origin Destination July 2005 August 2005
Average Speed (mi/h) Buffer Time Index Average Speed (mi/h) Buffer Time Index
Mobile Birmingham 57.96 10.33 58.38 8.41
Birmingham Nashville 55.37 3.21 56.17 2.35
Nashville Louisville 57.30 12.12 55.87 13.60
Louisville Gary 56.83 9.74 57.05 7.23
Segregating the data by city pair is useful in understanding the performance of segments of a particular corridor, as shown in this table. States and MPOs could use data on city pairs when analyzing freight movement at a local or regional level. In addition, if FPM evolves such that researchers can process the data in near real time, the city-pair data could be used to support freight-specific traveler information services. Source: FHWA.

Feedback from the Missouri Department of Transportation (MoDOT) indicates that the data are valuable to State transportation agencies seeking to measure freight performance. MoDOT is using preliminary FPM research data in its "Tracker" tool (available online at www.modot.org/about/general_info/Tracker.htm), which assesses how well MoDOT delivers services and products to its customers.

"We have built 18 tangible results around our customers' expectations," says MoDOT Director Pete Rahn. The agency counts the efficient movement of goods as one of the tangible results. The average travel time for trucks on selected roadways is a measure of this result, and MoDOT uses the preliminary FPM data to populate this performance measure.

"These results guide us every day as we go about the business of delighting our customers," Rahn says. "In the Tracker we have established measures to gauge our progress, and we are comparing ourselves to the best organizations in the country."

Other real-time traffic data networks, such as those enabled by the use of cell phone probe technology, are limited in that they do not differentiate among vehicle classifications; normally the data collected are a compilation of all vehicles in the traffic stream. "The data from the [FPM] initiative are a wonderful supplement to other real-time traffic data we will use for performance and system management," says MoDOT Traffic Engineer Michelle Teel. "We use the freight data to evaluate and improve our performance in delivering efficient movement of goods. These data allow us to analyze the performance of our roads and how we are serving the freight community."

Another important component of FPM is to access and articulate how the data and related products benefit commercial users of the highway system. Daniel Murray, ATRI's vice president of research, noted the trucking industry's inherent interest as it "is heavily dependent on a fast, efficient, and safe roadway system." Qualitative studies, such as the American Trucking Associations and ATRI's survey report Top Industry Issues 2005, have shown that congestion is ranked as one of the industry's top concerns. FPM data could help by enabling traffic managers to identify bottlenecks and congested areas along freight-significant corridors.

With these benefits in mind, Murray adds that the trucking industry will continue to support the FPM research because the industry has long been "a vocal supporter of funding and programs that reduce transportation inefficiencies." The industry has generally been agreeable to participating in research and program initiatives that increase the likelihood of capacity improvements, he says.

Although FPM itself is not a capacity improvement, the data derived from FPM provide a quantitative measure for Federal, State, and local transportation agencies to use in determining how well the highway system is meeting freight demands. With these data, the agencies can set reasonable goals for desired performance levels and begin to develop tactics and strategies and make investments that will improve the freight system.

In April 2006, the FPM research team began collecting and analyzing data on an additional 20 freight-significant corridors. The 25 interstates now included in this effort are among the Nation's most significant freight corridors in terms of average daily truck traffic, covering more than 32,000 miles (51,500 kilometers) of highways. In 2007, FHWA officials expect to expand data collection and analysis to reach 10,000 miles (16,000 kilometers) of major arterials with significant freight movement.

Initial FPM data and analysis results clearly demonstrate that variations in the system due to weather, work zones, and incidents (including crashes and lane closures due to wildfires or broken-down vehicles) affect speed and travel time reliability. Using data collected during calendar year 2005, for example, researchers created maps illustrating the eastward progression of a November 2005 blizzard along the I-70 corridor in Colorado. One map, reflecting data from November 27, showed a slowdown of truck traffic on I-70 as the storm began. Another map, generated using data from the next day, November 28, revealed that little to no truck traffic was moving along the 250-mile (400-kilometer) corridor between the Colorado line and the Salina, KS, area. Although the FPM mapping capability enables users to see problem areas in the system at a glance, a greater benefit is realized from the data behind the maps, which enable quantitative measurement of exactly how much system change is produced by varying conditions.

In 2006, the FPM project team is obtaining more specific information by conducting two studies on the effects of weather and work zones on speed and travel time reliability in the five freight-heavy corridors studied earlier. To complete the case studies, the team plans to coordinate and collaborate with States along the corridors on the selection of past, planned, or existing work zones. Various weather conditions will be examined, including severe events such as snowstorms, hurricanes, and flooding, as well as more routine weather events such as fog, wind, and rain.

Using data collection techniques similar to those used for measuring speed and travel time reliability in freight-significant corridors, the FPM team began collecting U.S.-Canada border crossing data in July 2005. Based on 2003 data from the Bureau of Transportation Statistics, the chosen five crossings represent more than 55 percent of all inbound truck traffic from Canada to the United States. The intent is to use truck location data to derive measures of border delay. FHWA researchers believe that border delay is an important indicator for monitoring performance of the freight system, as delay results in economic costs to border communities, carriers, imports and exporters, shippers, and public agencies.

The Road Ahead

The ability of transportation agencies to measure the performance of the freight system is complicated by the fact that much of the needed data resides with system users in the private sector. According to Rolf Schmitt, freight policy team leader in FHWA's Freight Office, the use of AVL technology has proven to be a viable solution for obtaining data to help measure system performance. Beyond meeting FHWA's need for performance data, "the FPM initiative helps fill freight transportation data gaps at State and local levels," Schmitt says. "This information is critical to improving the effectiveness of the freight transportation system."

Truck Traffic at Five Major United States- Canada Border Crossings, 2003
Port Name Location Trucks
Ambassador Bridge Detroit, MI/Windsor, ON 1,634,319
Pembina Pembina, ND/Emerson, MB 201,761
Peace Bridge Buffalo, NY/Ft. Erie, ON 1,162,961
Champlain-Rouses Pt. Champlain, NY/Lacolle, QC 387,962
Blaine Pacific Highway Blaine, WA/Surrey, BC 365,089
In 2003, nearly 3.8 million trucks entered the United States from Canada at these border crossings. The border component of the FPM initiative employs data collection and processing techniques similar to those used to measure speed and travel time reliability in freight-significant corridors. Researchers use the data to derive measures of delay at borders. Source: USDOT, Research and Innovative Technology Administration, Bureau of Transportation Statistics.

FHWA plans to continue its partnership with the trucking industry to obtain the data necessary for monitoring progress toward the agency's goal of enhancing global connectivity. FHWA also will work with States and local transportation agencies to set goals for system performance and identify strategies and tactics for achieving them.

Director Tony Furst of the FHWA Freight Office sums up the significance of the initiative with respect to the transportation community: "The data give us a look into network performance we've never had before. Further, we are exploring ways of putting the data in the hands of State and local transportation agencies who make freight investment decisions and who are on the front line of providing a highway transportation system that meets the growing demand for freight movement."

FPM researchers created this map of I-70 between the Colorado State line and Salina, KS, to demonstrate how mapping can relate travel time data to weather events. The map shows the eastward progression of a snowstorm that slowed down truck speeds on November 27, 2005, as indicated by the red and yellow highlighting on I-70. The legend indicates speeds in miles per hour (mi/h), where red corresponds with speeds of 5 to 35 mi/h, yellow corresponds with 35 to 50 mi/h, and green corresponds with 50 mi/h or greater. Photo Credit: Source: FHWA/ATRI.
FPM researchers created this map of I-70 between the Colorado State line and Salina, KS, to demonstrate how mapping can relate travel time data to weather events. The map, reflecting roadway conditions in the midst of a blizzard on November 28, 2005, shows little to no truck movement in the encircled area. The legend indicates speeds in miles per hour (mi/h), where red corresponds with speeds of 5 to 35 mi/h, yellow corresponds with 35 to 50 mi/h, and green corresponds with 50 mi/h or greater. Most of I-70 within the encircled area shows no truck movement, except for a short stretch where trucks were moving at speeds between 5 and 35 mi/h. Photo Credit: Source: FHWA/ATRI.
FPM researchers created these maps of I-70 between the Colorado State line and Salina, KS, to demonstrate how mapping can relate travel time data to weather events. The maps show the eastward progression of a snowstorm that slowed down truck speeds on November 27 and eventually resulted in blizzard conditions that shut down the highway on November 28. The map from November 27 (above) shows a slowing of truck speeds, as indicated by the red and yellow highlighting on I-70. The map from November 28 (below), reflecting roadway conditions in the midst of the blizzard, shows little to no truck movement in the encircled area. Source: FHWA/ATRI.

Crystal Jones is the project manager of the FPM initiative in the FHWA Office of Freight Management and Operations. Jones also administers programs and initiatives related to freight movement at U.S. international land border crossings. Prior to joining the Freight Office in 2003, Jones was a traffic management specialist with the United States Army, specializing in supply chain management and automation.

Joanne Sedor is a transportation specialist in FHWA's Office of Freight Management and Operations. As a member of the FHWA Freight Policy and Communications teams, she analyzes and writes about freight issues. Prior to joining the Freight Office in 2001, Sedor was a senior analyst and project manager at the Congressional Office of Technology Assessment.

For more information, visit www.ops.fhwa.dot.gov/freight/freight_analysis/perform_meas.htm or contact Crystal Jones at 202-366-2976, crystal.jones@dot.gov.

Authors' note: The FPM results are preliminary and therefore should not be used for decisionmaking.



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