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Publication Number: FHWA-RD-03-094
Date: March 2005

Estimating Cumulative Traffic Loads, Volume II:
Traffic Data Assessment and Axle Load Projection for The Sites With Acceptable Axle Weight Data, Final Report for Phase 2

Alternative Text

Figure 1. Text boxes. Overview of main traffic data assessment and projection activities. The figure is a list of assessments of traffic data and the development of traffic projections carried out in two phases over the course of 30 months and consisting of 8 main activities. Each of the three phases is separated into its own box. Phase 1 has three activities numbered 1 through 3; Preliminary assessment of Long-Term Pavement Performance traffic data, Development of Long-Term Pavement Performance traffic projection procedure, and Validation of Long-Term Pavement Performance traffic projection procedure using case studies. Phase 2 has five activities numbered 4 through 8, which are Development of Long-Term Pavement Performance traffic feedback and resolution package, Preparation of Long-Term Pavement Performance traffic feedback and resolution packages for all participating agencies, Review of Long-Term Pavement Performance traffic feedback and resolution packages by regional coordination offices, Review of Long-Term Pavement Performance traffic feedback and resolution packages by participating agencies, and Implementation of review comments received from participating agencies. Phase 3 is the recommended phase, and the recommendations are provided in chapter 6.

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Figure 2. Form. Initial site-specific report for Mississippi. The figure is a 4-page feedback and resolution report prepared for the Mississippi Department of Transportation. The sheet includes procedures for the reviewer and refers to figures 3 through 10 to help assist the reader. The reference to figures 3 through 10 briefly describes the purpose of each sheet in the report. The purpose of this figure is to give an example of an initial feedback and resolution report.

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Figure 3. Questionnaire. Feedback and data resolution sheet for site 285805. The figure is a questionnaire, filled out for site 285805, Mississippi in March 2001. This questionnaire is the first sheet in the site-specific report. The purpose of this sheet is to summarize all major site-specific features that may influence traffic projection.

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Figure 4. Map. Site map for site 285805. The figure is a map of the southern region of the United States. Baton Rouge and New Orleans, Interstates 59 and 10, and test sites 5805, 3093, and 3094 are labeled on the map. This figure is the second sheet in the site-specific report. The purpose of the map is to clearly identify the location of the site tested, as well the location of other sites nearby.

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Figure 5. Graphs. Annual traffic projection sheet for site 285805. The figure shows graphs and a table comparing annual average daily volumes, annual average daily truck volumes, average equivalent single axle loads and truck factors. This figure is the third sheet in the site-specific resolution and feedback report. The purpose of the projection sheet is to summarize the trends in historical and monitoring traffic data. It is expected that traffic volumes and equivalent single axle loads will exhibit an increasing trend. Truck factors (equivalent single axle loads per truck) should be at a level or perhaps increasing to reflect the increased cost-efficiency of the trucking industry. The total annual average daily traffic volume increased only slightly from 11,000 in 1991 to 12,800 in 1996. The truck volume increased from 11,000 in 1991 to 15,000 in 1996. Both the average equivalent single axle load per day and per truck increased their average from 1992 to 1996. The trend increased as expected.

The figure also includes a table showing the availability of monitoring data by data type (automatic vehicle classifiers and weigh-in-motion) and for years 1990 through 1998.

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Figure 6. Graph. Projected annual average daily traffic volumes for site 285805. The figure shows a graph and a table comparing annual average daily traffic volumes and projected growth, which is usually a smooth line or a curve. The purpose of this sheet is to show historical and monitoring truck volumes and the suggested projection model used to estimate truck volumes for all in-service years. Historical, monitoring, and projected data increases steady, as expected.

Historically the volume of trucks rises from 970 average annual daily traffic in 1975 to a peak of 2190 in 1989. In 1990 there is a sudden drop down to 1544. The line ends in 1991 with an annual daily traffic volume number of 1524. The projected line climbs steadily from 885 in 1975 to 2429 in 1998. Monitoring from 1992 through 1996 matches the projected line.

This figure also includes a table showing the numbers of the average annual daily traffic truck volume (historical, monitoring, and projected) from 1975 through 1998. Projected growth as a percentage and factor are depicted for the same time period.

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Figure 7. Graphs. Annual vehicle class distribution for site 285805. The figure shows two graphs. The first graph shows the actual axle counts for different truck classes. The second graph shows the distribution of trucks as a percentage of the total truck count. The years tested include 1992 to 1996. Vehicle class 9 and class 5 are significant for sit 285805 in Mississippi. Class 9 has 70 percent of vehicle counts and class 5 has 10 percent of the vehicle counts.

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Figure 8. Graphs. Annual load spectra for the site 28-5085. The figure shows three graphs comparing annual load spectra for all available years with single, tandem, and triple axle loads. The first graph labeled single axle is used to represent traffic during the years before the installation of a weigh-in-motion scale, and during the initial operation of the scale. The spectrum of the second graph labeled tandem axle is used to represent traffic levels for the most recent years, with and without the weigh-in-motion scale data. The last graph is labeled triple axle. Both the first and the last graphs' spectra can be the averages of several annual spectra. Tandem axle distribution usually has two peaks. The first peak corresponds to unloaded tandems, and the second peak to the fully loaded tandems.

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Figure 9. Graphs. Average annual load spectrum for site 285805. The figure shows three different graphs comparing the axle load and axle counts for single, tandem, and triple axles. The average annual load spectrum is obtained by calculating the average of the annual spectra, presented in figure 8. The single axle spectra peaks at 9,000 pounds and 17,800 axles. The tandem axle spectrum has two peaks, the firs t 9,000 pounds and the second at 27,000 pounds. The triple axle spectra decreases but peaks at 32,000 pounds.

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Figure 10. Graphs. Projected annual equivalent single axle loads for site 285805. The figure shows a graph and a table comparing the average equivalent single axle loads for all in-service years. This figure is the last page of the initial site-specific report. The purpose of traffic collection and analysis is to obtain axle load spectra and not equivalent single axle loads. Equivalent single axle loads are used mainly for comparison and quality assurance purposes. The historical, monitoring, and projected data are increasing on the graph. The historical line for equivalent single axle loads starts at 330,000 and rises to 1,063,000 in 1989, after which it drops to 837,000 and 827,000 for 1990 and 1991 respectively. The line stops there. The line for the projected ESAL starts in 1975 at 359,237 and rises steadily through 1998 to 986,994. The monitor line covers the period from 1992 through 1996 and increases rapidly from 618,395 to 1,256,965.

A table is included in this figure showing numbers of the annual equivalent single axle loads from 1975 through 1998 in the three categories of historical, monitoring, and projected.

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Figure 11. Graph. Projection of truck volumes using historical and monitoring data. The figure is a line graph of the historical and monitoring truck volume data being backcasted, interpolated, and forecasted. The year is graphed on the horizontal axis from 1980 to 2000. The annual average daily traffic truck volume is graphed on the vertical axis. The line increases from 1980 to 2000. Backcasting begins before 1980 and increases gradually to 1988. Interpolation is increases from 1990 to 1994. Forecasting increases from1995 to 2000. The purpose of this graph is to estimate traffic loads for all in-service years.

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Figure 12. Graphs. Comparison of annual average daily traffic volumes for class 14 vehicles with annual average daily traffic volumes for all trucks. The figure shows three graphs comparing class 14 vehicles to trucks for three different Minnesota sites. The year is graphed on the horizontal axis on all three graphs. The annual average daily volume is graphed on the vertical axis on all three graphs. The trucks had higher annual average daily traffic volumes throughout the year compared to class 14 vehicles. Class 14 vehicles and trucks followed the same trend, the data increased and decreased on the same years. Class 14 vehicles, or passenger cars had higher volumes than trucks.

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Figure 13. Graphs. Use of the mean of all annual axle load spectra to obtain base annual spectrum The figure shows three graphs comparing the mean of all annual load spectra for all available years with single, tandem, and triple axle loads from site 063042. The tests include years from 1990 to 1998. The axle load is graphed on the horizontal axis and the axle count is graphed on the vertical axis. All the tests increase and decrease in the same trend for each axle load. The single axle has 3 peaks at 400, 1000, and 1700 pounds. Tandem axle load has 2 peaks at 1000 and 32,000 pounds. Triple axle load has its peak between 2,500 to 35,000 pounds. The base annual spectrum was obtained as a mean value of the annual spectra for 1990 to 1998.

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Figure 14. Graphs. Use of the mean of 1991, 1992, and 1995 annual axle load spectra to obtain base annual spectrum. The figure shows three graphs comparing the axle load spectra historically from site 185518. The axle load is graphed on the horizontal axis and the axle count is graphed on the vertical axis. The first graph measures single axles and has all the data increasing and decreasing in the same pattern. The highest year is 1995 with 43,000 axle counts at 11,000 pounds before decreasing gradually to zero. The second graph measures tandem axles and all the years increase and decrease in a wavy pattern, or in an M pattern before returning to zero. The third graph measures triple axles and most of the data has a minor wave-like pattern. Only the 1997 data zigzags. Most of the data follow the same trends.

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Figure 15. Graphs. Use of the mean of 1991 and 1992 annual axle load spectra to obtain base annual spectrum. The figure shows three graphs comparing the annual load spectra from site 124057. The axle load is graphed on the horizontal axis and the axle count is graphed on the vertical axis of all three graphs. The first two graphs increase slightly, and then gradually decrease to zero. Two different years, 1994 and 1991, zigzag up and down a couple of times before descending to zero. The last graph, most of the years remain at zero. The years 1993 and 1991 increases slightly at 5,000 axles and 53,000 pounds. Most of the data follow a similar trend in the graphs

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Figure 16. Graphs. Rejection of all available annual axle load spectra. The figure shows three graphs comparing annual load spectra from site 473104. The axle loads are graphed on the horizontal axis and the axle counts are graphed on the vertical axis. The years graphed include the time between 1992 and 1996. The first graph compares annual single axles. All the years tested increase in axle counts at 5000 pounds, which decreases to zero. The second graph compares tandem axles. The years 1993through1996 have a random zigzag pattern; increasing and decreasing axle counts as the load increases. Only 1992 remain at zero. The third graph compares triple axle counts. Only 1994 and 1996 have a random zigzag pattern. Both years increase and decrease in number of axles as the load increases. The rest of the years do not have any axles.

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Figure 17. Graph. Projected annual average daily traffic truck volumes for site 124057. The figure shows a graph and a table comparing the annual average truck volume per year. The graph has three tests: historic, monitoring, and projected. The years are graphed on the horizontal axis from 1985 to 1998. The annual average daily truck volume is graphed on the vertical axis up to 4500. The monitored data starts with an outlier point in 1991 at a volume of 4000. The monitored data decreases to a volume of 1000 the next year. The purpose of this graph is to show an example of an outlier, or error.

This figure also includes a table showing the numbers of the annual average daily truck volumes truck volume (historical, monitoring, and projected) from 1986 through 1998. Projected growth as a percentage and factor are depicted for the same time period.

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Figure 18. Graphs. Annual traffic projection sheet for site 063042. The figure has two graphs that follow the historical and monitoring truck factors. The first graph has the year graphed on the horizontal axis from 1978 to 1998 and the annual average daily traffic truck volume and total volume graphed on the vertical axis. Both historical data increases in volume from 1980 to 1989. The average equivalent single axle loads per day increase in a zigzag pattern from 11,000 to 16,000. The average equivalent single axle loads per truck increase from 1990 to 1991, then remains steady at a volume of 10,000. The second graph has the year graphed on the horizontal axis from 1978 to 1998 and the average equivalent single axle load per day and per truck graphed on the vertical axis. The historical data increases in loads per day. The monitored date decreases in average loads per truck.

The figure also includes a table showing the availability of monitoring data by data type (AVC and WIM) and for years 1990 through 1998.

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Figure 19. Graph. Projected annual average daily traffic truck volumes (initial) for site 473104. The figure shows a graph and a table of annual average daily truck volumes. The year is graphed on the horizontal axis from 1985 to 1998. The truck volume is graphed on the vertical axis up to 14. Three lines are graphed, which are historical, monitoring, and projected. Historical zigzags from 1986 at a volume of 5 to 1992 at a volume of 9. Historical begins with a volume of 5 in 1986, which increases to 10 in 1987, then decreases to 6 in 1988, and ends at 9 in 1992. Monitoring also increases and decreases in a zigzag patter between the years 1992 to 1996. Monitoring begins with a volume of 2 in 1992, increases to 9 in 1993, then decrease to 6 in 1995, and ends at 12 in 1996. Projected increases in a straight line with a volume of 6 in 1986 to a volume of 12 in 1997. The outlier is monitoring data in 1992 with a volume of 2.

This figure also includes a table showing the numbers of the annual average daily truck volumes truck volume (historical, monitoring, and projected) from 1986 through 1997. Projected growth as a percentage and factor are depicted for the same time period.

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Figure 20. Pie Charts. Summary of Long-Term Pavement Performance sites sorted by data presence and data usability for projection. The figure shows nine different pie graphs separated into three sections: data presence, data usability for projection, and data quality for projection. Under the two sections, data presence and data usability for projection, the charts depict four categories: axle weight, truck class, only historical truck, only truck class, and no traffic data or no usable data. Each section has three pie charts. The first is built on the total 890 sites, then two pie charts with a breakdown by the 791 general pavement study sites and the 99 specific pavement sites.

  1. Data presence total sites. The pie chart shows the total sites of 890 depicts 695 at axle weight, truck class; 87 in only historical truck; 59 in no traffic data; and 49 in only truck class.
  2. Data presence GPS sites. The pie chart is for general pavement studies with a total of 791, with 650 being axle weight, 83 only historical truck, 48 only truck class, and 10 with no traffic data.
  3. Data presence SPS sites. The chart for specific pavement study sites represents the remaining 99 sites with 49 having no traffic data, 45 for axle weight, truck class, 4 for only historical truck, and 1 for only truck class.
  4. Data usability total sites. The pie chart under the section titled data usability for projects shows the 890 total sites having 543 in axle weight, truck class; 199 in only truck class; 85 in only historical truck; and 63 in no traffic data.
  5. Data usability GPS sites. The pie chart for general pavement studies shows a total of 791, has 511 being axle weight; 199 in only truck class; 85 only historical truck; and 10 with no traffic data.
  6. Data usability SPS sites, Last, the chart for specific pavement study sites represents the remaining 99 sites with 53 having no traffic data; 32 for axle weight; truck class; 12 for only historical truck; and 2 for only truck class.

The third set of pie charts measure data quality for projection. The data is divided into three categories: axle weight, truck class and volume yielding acceptable; axle weight, truck class and volume yielding questionable; and all other.

  1. Data quality total sites. The total 890 sites has 364 sites measuring axle weight of truck class and volume yielding acceptable projections, 332 sites measuring all other sites, and 194 sites measuring axle weight of truck class and volume yielding questionable projections.
  2. Data quality GPS sites. The 791 general pavement studies has 342 axle weight, truck class and volume yielding questionable; 265 other, and 184 axle weight, truck class and volume yielding acceptable.
  3. Data quality SPS sites The last pie chart for specific pavement study 67 other; 22 axle weight, truck class and volume yielding questionable; and 10 axle weight, truck class and volume yielding acceptable.

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Figure 21. Map. Geographical distribution of Long-Term Pavement Performance sites with acceptable projection confidence codes. The figure is a map of the United States with the Long-term Pavement Performance sites pinpointed. Multiple pinpoints are identified as either rural or urban depending on their highway functional class.

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Figure 22. Graph. Mean vehicle class distribution for Long-Term Pavement Performance sites with acceptable and questionable projection confidence codes located on rural highways. The figure is a graph showing the average rural performance site for each vehicle class. The Federal Highway Administration vehicle class is graphed on the horizontal axis from 4 to 13. The percentage of vehicles is graphed on the vertical axis. There are four sites tested: rural principal arterial for interstate, rural principal arterial for other, rural minor arterial, and rural major collector. All four sites peak at in percentage for vehicle class 5 and 9. The rural principal arterial for interstates has the highest percentage, 60 percent at class 9.

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Figure 23. Graph. Mean vehicle class distribution for Long-Term Pavement Performance sites with acceptable and questionable projection confidence codes located on rural highways. The figure is a graph showing the mean or average percentage of urban performance sites for each vehicle class. The Federal Highway Administration vehicle class is graphed on the horizontal axis from 4 to 13. The percentage of vehicles is graphed on the vertical axis. The sites tested are urban principal arterial for interstates, urban principal arterial for freeways or expressways, and urban other principal arterial. All three sites climax on vehicle class 5 and 9. Vehicle class 5 has an average between 20 to 27 percent. Vehicle class 9 has a mean between 35 to 41 percent.

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Figure 24. Graph. Trends in the number of monitoring axle load spectra. The figure is a graph illustrating the overall historical trend in the available axle load spectra. The year is graphed on the horizontal axis from 1998 to 1999. The number of sites with axle load data is graphed on the vertical axis up to 500. The annual axle load spectra compared are available in information management system and used for projection. The available in information management system increases from 1990 to 1993, from 58 sites to 446 sites. The trend decreases gradually and ends in 1998 with 287 sites. Used for projection increases from 1990 to 1993, from 26 sites to 200 sites. Used for projection increases slightly and remains constant until 1995, then decreases. In 1998, used for projection ends with 150 sites. Available in information management system had more sites overall.

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Figure 25. Flowchart. Overview of the information management system traffic module showing the proposed addition of projected traffic data. The figure is a flowchart of information management traffic. Information management traffic module includes historical, monitoring, and projected traffic data. The major elements in historical data are total volume and equivalent single axle load. The major elements for monitoring data are axle load spectra, volume by class, total volume, and equivalent single axle load. The major elements for projected data are projected annual axle load spectra and intermediate data elements.

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Figure 26. Flowchart. Flowchart used for calculating computed parameter tables. The figure is a flowchart with two main categories, which are historical and monitoring data, and projected data. Historical and monitoring data has two sections. The first section is estimated annual total general pavement study lanes and monitored vehicle distribution. The second section in historical and monitoring data includes monitored basic information.

The first section in historical and monitoring data leads to Annual Projection factors in the Projected data category. The projection factors leads to projection summary and computation of projected annual axle load spectra, then ends at cumulative axle loads by year. The second section in historical and monitoring data category leads to the projected data category, which is computed normalized base annual axle load and computed bases annual axle load. The base annual axle load leads to reporting projection summary and computation of projected annual axle load spectra, and ends with cumulative axle loads by year.

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Figure 27. Flowchart. Flowchart for computation of the normalized base annual load spectra. The figure is a flowchart for finding the normalized base annual axle load spectrum. Information management system splits into two directions. The first is to find monitored basic information and retrieval of supporting traffic data and other information. The other direction is to find the axle distribution and extract available annual load spectra for all monitoring year. The two separate direction meet again to select the annual axle load spectra and calculate the base annual spectrum by averaging the selected annual axle load spectra. Next normalize axle counts in base spectrum with respect to the total number of axles for each axle type to end with the computed normalized base annual axle load spectra.

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Figure 28. Flowchart. Flowchart for computation of the base annual axle load summary. The figure is a flowchart to find base annual axle load. The information management module can either start with monitor basic information of monitor axle distribution. Both data leads to select annual axle load spectra and calculate the base annual spectrum by averaging the selected annual axle load spectra. The next box is to obtain base total annual number of axles for each axle type, which leads to the end to the traffic projected base annual axle summary.

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Figure 29.
Flowchart. Flowchart for computation of the annual projection factors. Information management system traffic module. The chart begins with one of three tables; either estimated annual total general pavement study lanes, basic information, or monitor vehicle distribution and monitor axle distribution. The estimated annual general pavement study lanes leads to extract site-specific historical annual average daily traffic volume and equivalent single axle load information. The monitor basic information can either be empty or not empty. If it is empty, then extract site-specific monitoring annual average daily traffic volume and equivalent single axle load information. If the information is not empty, then estimate the annual average daily traffic volume and equivalent single axle load based on site-specific monitoring vehicle class and axle count data. These three tables all lead to analyze monitoring and historical traffic data and develop traffic projection model. Next is to compute the annual traffic projection factors for each in-service year. The computed annual projection factors is the result.

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Figure 30. Flowchart. Flowchart for computation of projected annual axle load spectra. There are two different openings to the flowchart. The first is to multiply normalized number of axle and total number of axles to get the base number of axles. The other start is the annual projection factors for each year y since site opening to traffic to 1998 or "out of study" date. Both data leads to obtaining annual axle load spectrum for each year by multiplying base number of axles in each weight category by annual projection factors, or multiply the base number of axles with annual projection factor for year y to get the projected number of axles for year y. The final computation results in projected annual axle load spectra.

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Figure 31. Text boxes. Overview of pavement loading guide functions. The pavement loading guide functions include database management, data comparison and assessment, development of pavement loading estimates, and development of axle load spectra and cumulative traffic estimates. The operations for database management includes data storage; selection, sorting, and retrieval; and importing and exporting data. The operations for data comparison and assessment include selection of sites using different filters; display of selected data for truck distribution, axle load spectra, and site location; and data comparison for Long-Term Pavement Performance and other data, and generic data. The operations for development of pavement loading estimates include selection of truck class and axle load distribution for individual vehicle classes using direct input, data from other sites, and generic data. The operations for development of axle load spectra and cumulative traffic estimates include base annual axle load spectra, projected axle load spectra for all years, cumulative axle load spectra, and annual and cumulative equivalent single axle loads.

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Figure 32. Graph. Comparison of truck class distributions for sites 062040, 066044, and 068150. The figure shows a graph inside a screen from pavement loading guide software. The screen is on substep 3B; Select sites and compute vehicle distribution. The Federal Highway Administration vehicle class is graphed on the horizontal axis from 4 to 13. The percentage of total truck counts is graphed on the vertical axis. There are three California sites graphed on the screen, which are 62040, 66044, and 68150. Sites 62040 and 68150 are general pavement study 2. Site 66044 is general pavement study 6A. All three sites have the greatest percentage of trucks for class 5.

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Figure 33. Computer screen. Comparison of single axle load distributions for vehicle class 9 for sites 062040 and 066044 with computed mean distribution. The figure shows a screen from the pavement loading guide software. Inside the screen is a graph of normalized axle load distribution. The axle weight is graphed on the horizontal axis up to 30,000 pounds. The percentage of single axle counts is graphed on the vertical axis up to 30 percent. There are three sites tested on the graph: site 6-2040, 6-6044, and computed. All three increase to 20 percent at 10,000 pounds and then decrease again. All three sites have similar trends.

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Figure 34. Computer screen. Comparison of tandem axle load distributions for vehicle class 9 for sites 06040 and 066044 with computed mean distribution. The figure is a screen from the pavement loading guide software. Inside the screen is a graph of normalized axle load distribution. The axle weight is graphed on the horizontal axis up to 60,000 pounds. The percent of tandem axle counts is graphed on the vertical axis up to 20 percent. All three have the same pattern, increasing and decreasing a couple times in an M pattern.

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Figure 35. Graphs. Comparison of site-specific and surrogate base annual spectra for site 068150. The figure shows three graphs mapping the trend of axle loads and axle counts of site-specific and surrogate data. Axle loads are graphed on the horizontal axis. Axle counts are graphed on the vertical axis. Site-specific and surrogate tests increase and decrease together in a wave-like patter for both single and tandem axle graphs. There is no pattern in data for the triple axle graph. Site specific remains below 100 axles throughout the axle loads. Surrogate and site-specific data had similar results in single axle loads.

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Figure 36. Graph. Comparison of projected, historical, and monitoring annual equivalent single axle loads for site 068150. The figure shows a graph and a table on the projected, historical and monitoring annual equivalent single axle loads. The year is graphed on the horizontal axis from 1983 to 1998. The average equivalent single axle load per year is graphed on the vertical axis up to 500,000. Site-specific category 2 and surrogate category 3 increase in a straight line from 1983 to 1998 at 100,000 to 200,000 loads per year. The monitoring load only shows for 1995 to 1997 at 160,000 loads per year. From 1984 to 1991, Historical load has remained at a steady pace of 60,000 to 80,000 loads per year. Historical load increases dramatically in 1993 to 450,000 loads per year. The cumulative number of equivalent single axle loads using surrogate spectra was 2.06 million, while the corresponding number of equivalent single axle loads for site-specific spectra was 2.18 million.

A table is included in this figure showing numbers of the annual equivalent single axle loads from 1984 through 1998 in the three categories of historical, monitoring, and projected. Projected is divided into two subcategories: site-specific spectra category 2 and surrogate spectra category 3.

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Figure 37. Graph. Comparison of truck class distributions for sites 041007 and 041017. The figure is a graph inside the pavement guide software screen. The Federal Highway Administration vehicle class is graphed on the horizontal axis from 4 to 13. The percentage of total truck counts is graphed on the vertical axis up to 80. Two sites are graphed on the figure, which are 041007 and 041017. Both sites increase to 76 truck counts at class 9.

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Figure 38. Graphs. Comparison of site-specific and surrogate base annual spectra for 041017. The figure shows three different graphs measuring site-specific and surrogate axle loads per counts. The first graph measures single axle counts. Both loads increase and decrease in the same pattern. Surrogate increases at 10,000 pounds to 50,000 axles, then decrease to 9,000 axles at 1,400 pounds. Surrogate increases to 45,000 axles at 10,000 pounds, and then decreases to 7,000 axles at 14,000 pounds. The second graph measures tandem axle counts. Both bases increase and decrease in the same pattern also. The third graph measures triple axle loads. Site-specific decreases drastically in axle counts between 12,000 to 18,000 pounds at 220 to 40 axle counts. Site-specific has below 50 axles from 18,000 to 66,000 pounds. Surrogate increases and decreases in a zigzag pattern. There is a good agreement between traffic loads using surrogate and site-specific data.

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Figure 39. Graph. Comparison of projected, historical, and monitoring annual equivalent single axle loads for site 041017. The figure is a graph and a table of projected, historical, and monitoring equivalent single axle loads per year. The year is graphed on the horizontal axis from 1974 to 1998. The average equivalent single axle load per year is graphed on the vertical axis up to 1,800,000. The four sites examined are historical, monitored, site-specific category 2, and surrogate category 4. Both site-specific and surrogate increase in a straight line from 1976 to 1998 at an average of 58,000 to 200,000 loads per year. Monitoring has only two plots in 1993 and 1997 at about 102,000 and 132,000 loads per year. From 1978 to 1983, historical remains approximately close to 800,000 loads per year, and then increases to 1,556,000 in 1988. Historical drops off in 1991 at 150,000 loads per year. This figure shows a very good agreement between traffic loads estimated using surrogate data and site-specific data.

A table is included in this figure showing numbers of the annual equivalent single axle loads from 1976 through 1998 in the three categories of historical, monitoring, and projected. Projected is divided into two subcategories: site-specific spectra category 2 and surrogate spectra category 4.

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Table of Contents

 


The Federal Highway Administration (FHWA) is a part of the U.S. Department of Transportation and is headquartered in Washington, D.C., with field offices across the United States. is a major agency of the U.S. Department of Transportation (DOT).
The Federal Highway Administration (FHWA) is a part of the U.S. Department of Transportation and is headquartered in Washington, D.C., with field offices across the United States. is a major agency of the U.S. Department of Transportation (DOT). Provide leadership and technology for the delivery of long life pavements that meet our customers needs and are safe, cost effective, and can be effectively maintained. Federal Highway Administration's (FHWA) R&T Web site portal, which provides access to or information about the Agency’s R&T program, projects, partnerships, publications, and results.
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