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Publication Number: FHWA-HRT-05-079
Date: May 2006

Optimization of Traffic Data Collection for Specific Pavement Design Applications

Chapter 4. LTPP Data Analysis

The data necessary for filling the two knowledge gaps identified earlier were extracted from the LTPP database:

  • Extended-coverage WIM data to allow simulation of the selected traffic data collection scenarios identified in table 8.
  • Detailed structural data for the pavement sites selected to ensure realistic simulation of their performance under the selected traffic input scenarios.

The following subsections present:

  • Data extraction from extended-coverage WIM sites in the LTPP database.
  • Rationale for selecting several of these sites for the sensitivity analysis, while using the remainder to obtain the regional data sets necessary for factoring short-term, site-specific data in simulating the selected scenarios.
  • Analysis conducted for establishing the regional traffic data sets.
  • Methodology used for simulating each of the traffic data collection scenarios.

LTPP WIM Data Extracted

The main criterion for selecting data from the LTPP database was the extent of WIM data coverage in terms of the total number of data days per year. A search of the LTPP database(16) was performed based on this criterion. Initially, a filter of 359 days per year or greater was selected (i.e., 2 percent of days per year missing). This resulted in a total of 58 sites, some involving multiple data years. To increase the number of sites available for analysis, a lower threshold filter was used involving WIM coverage of 299 days per year or greater (i.e., 20 percent of days per year missing). This resulted in a total of 178 sites, some involving multiple data years. The number of LTPP sites meeting these two criteria versus the number of data years available are plotted in figures 2 and 3, respectively. Figure 2, for example, suggests that 46 sites have more than 359 days per year of WIM data for 1 year; 6 sites do so for 2 years, and so on. Multiple years of data for the same site are advantageous because they allow for the establishment of traffic growth patterns. The data quality for these sites was deemed to be level E (i.e., the data had passed the quality control conducted by the State DOTs and the LTPP regional support contractor offices). To further ensure data quality, the LTPP quality assurance reports pertaining to these 178 sites were examined. They revealed no particular problems with any of them. These quality assurance reports were not appended here, but they are available on request.

The highest resolution of traffic data necessary for simulating the scenarios in table 8 is daily summaries, which are not contained in Data Release; therefore, data had to be retrieved from the CTDB. It contains traffic data at five levels of resolution:

  • Level 1. Annual load/count summary records by axle (uploaded to the information management system database to become part of the periodic Data Release).
  • Level 2. Annual loads by vehicle class and annual load spectra by truck type.
  • Level 3. Daily summary traffic records.
  • Level 4. Submitted traffic loading records (i.e., raw individual card-4 and card-7 data).
  • Level 5. Additional traffic loading information.

Given the highest resolution of daily data desired for simulating the 17 traffic scenarios, level 3 WIM data were extracted from the CTDB for the 178 WIM sites for the data years identified. The data fields extracted are described in table 10. The data was in Microsoft® Access format. It contained the daily number of axle passes by truck class, axle type, and load bin as it combined axle weight and vehicle classification information.

Figure 2. LTPP sites with WIM data available for periods longer than 359 days per year.(16)

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Figure 3. LTPP sites with WIM data available for periods longer than 299 days per year.(16)

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Table 10. Definition of variables extracted from the CTDB.
Variable Name Definition
STATE_CODE State/province ID
SHRP_ID Test section LTPP identifier
LANE_TRF Lane identifier, where 1 is the lane nearest the right-hand shoulder
DIR_TRF Traffic direction (1, 2, 3, 4 indicate east, west, north, south, respectively)
VEH_CLASS FHWA vehicle classes 1 through 13; 14 indicates "other," 15 indicates "unknown"
AXLE_GROUP Axle configuration (1, 2, 3, 4 indicate single, tandem, tridem, quad axles, respectively)
YEAR Year the data were collected
MONTH Month the data were collected
DAY Day of week
RECORD_STATUS QC code from A through E
AX_CT_01 to AX_CT_40 Number of axle passes by load bin. Depending on axle type, these bins are:
  • Singles: AX_CT_01 is 0 to 4.44 kilonewtons (kN) (0 to 9,892 pounds force (lbf)
  • Tandems: AX_CT_01 is 0 to 8.89 kN (0 to 1999 lbf); subsequent bins are in increments of 907 kilograms (kg) (2,000 pounds (lb)).
  • Triples/Quads: AX_CT_01 is 0 to 13.34 kN (0 to 2999 lbf); subsequent bins are in increments of 13.35 kN (3001 lbf).

Rationale For Selecting Sites For The Detailed Sensitivity Analysis

A number of these extended WIM data coverage LTPP sites were selected for the detailed sensitivity analysis of the NCHRP 1-37A design guide with respect to the traffic input obtained from the simulated traffic data collection scenarios (table 8). The remaining sites were used for obtaining the regional traffic data sets (i.e., vehicle classification and axle-load distribution estimates for the detailed sensitivity analysis sites).

The following criteria were used for selecting sites for the detailed sensitivity analysis:

  • WIM data coverage of preferably 299 days per year or greater.
  • Availability of WIM data over several years to allow the study of the effect of traffic growth.
  • Distribution of sites over a wide range of truck traffic volumes (i.e., AADTT) and structural thicknesses.

The latter was indexed by the structural number (SN) and the concrete slab thickness for flexible and rigid pavement sites, respectively. Figures 4 and 5 show the distribution of AADTT versus structural thickness for all of the extended-coverage WIM sites identified from the LTPP database for flexible and rigid pavements, respectively. For each pavement type, two AADTT intervals were identified.

For flexible pavements, two truck traffic volumes were defined:

  • Fewer than or equal to 800 trucks/day/lane.
  • More than 800 trucks/day/lane.

Figure 4. Flexible pavement site selection by AADTT and structural number.

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Figure 5. Rigid pavement site selection by AADTT and slab thickness.

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For rigid pavements, two truck traffic volumes were defined:

  • Fewer than or equal to 1,200 trucks/day/lane.
  • More than 1,200 trucks/day/lane.

The final selection of sites for the detailed sensitivity analysis was carried out by identifying 5 to 10 sites in each AADTT interval. For each interval, the sites selected covered a range in AADTT, while satisfying the other two criteria listed earlier (i.e., highest possible WIM data coverage over multiple years). For rigid pavement selection, additional consideration was given to structural configuration (roughly half of the pavement sections selected was jointed and the other half was continuously reinforced). Background information on the sections selected for the detailed sensitivity analysis of the NCHRP 1-37A design guide is given in tables 11 and 12. These tables identify the years of extended WIM data coverage (i.e., 299 days per year or greater), the AADTT for the year selected for the detailed sensitivity analysis, and the structural thickness/configuration of the sites. The data for the remaining years were used to establish the truck traffic growth rate for these sites.

Identifying Groups Of Sites For Obtaining Regional Data

As shown in table 8, the numerous traffic data collection scenarios needed for simulation involve representative regional traffic data; therefore, it was necessary to establish a formal process for developing representative regional traffic data for the detailed sensitivity analysis sites identified above (tables 11 and 12). This grouping needs to be carried out separately for establishing vehicle classification information and axle-load information (the second and fifth input components of the NCHRP 1-37A design guide, respectively, as described in table 6). It could be done subjectively using roadway functional class criteria, such as the ones shown in table 13. It is clearly better to do so using objective criteria, such as clustering techniques. As described in the literature review, clustering was introduced in the 2001 TMG (Appendix 2-b as the preferred technique for identifying sites with similar seasonal traffic volume distribution patterns.)(3) Clustering is used in this study to identify sites with similar vehicle classification distributions and axle-load distributions. As mentioned earlier, the vehicle classification and axle-load distributions in the NCHRP 1-37A design guide are input in the form of frequency distributions (percentage). As a result, there is no need to establish regional sites in terms of similar pavement loading, as is done for conventional TWRGs, nor it is necessary to use the rather outmoded ESAL concept for doing so (this is likely to influence future editions of the TMG).

In terms of load distribution, regional clusters were identified with respect to tandem axles only because they are the most common in the traffic stream. It should be noted that a number of alternatives were considered, including the use of raw load distribution for all four-axle types and the load distribution of all four axle types weighed by their relative frequency in the traffic stream. Using the distribution of the tandem axles was only favored for its simplicity. Furthermore, in developing regional traffic data, clustering was done by State. Although there is no fundamental reason for partitioning the nationwide data, it better simulates the practice of individual DOTs that work primarily with their own data. A detailed description of the clustering technique can be found in statistical texts.(16) A brief overview of the method is given below and explained through an example involving the LTPP WIM sites in Washington State.

 

Table 11. Background information on the flexible LTPP sites selected.
Site State Structural Number in millimeters (mm) (inches) Data years1 Data Days2 AADTT2 AADTT Level
091803 CT 114 (4.5) 1994,95 359 165 AADTT = 800
261004 MI 43 (1.7) 1992,94,95,96,97,98 348 229
271019 MN 76 (3.0) 1992,94,95,96 313 268
282807 MS 140 (5.5) 1995,96 321 457
531007 WA 66 (2.6) 1993,94,95 365 177
182008 IN 158 (6.2) 1992,93,97,98 349 709
182009 IN 234 (9.2) 1998 356 655
261010 MI 122 (4.8) 1994,95,98 362 647
536048 WA 107 (4.2) 1994 365 783
261012 MI 135 (5.3) 1994,95,98 355 977 AADTT > 800
181028 IN 178 (7.0) 1997,98 319 1535
186012 IN 231 (9.1) 1992,97,98 324 1473
261013 MI 150 (5.9) 1994,98 334 1395
283081 MS 122 (4.8) 1993 356 1120
283093 MS 102 (4.0) 1995 341 1920
  1. Data year used in traffic data collection scenario simulation is bolded.
  2. AADTT volumes and data days are for the year bolded.

 

Table 12. Background information on the rigid LTPP sites selected.
Site State Slab in mm (inches)) Configuration Data Years1 Data Days2 AADTT2 AADTT Level
094020 CT 230 (9.0) JRCP 1994 308 546 AADTT = 1200
263069 MI 230 (9.0) JRCP 1994,95,97 319 577
284024 MS 200 (8.0) JRCP 1995 360 99
501682 VT 200 (8.0) JRCP 1992,94,95,97 363 419
533813 WA 198 (7.8) JRCP 1992,93,94 365 548
185022 IN 230 (9.0) CRCP 1997 313 1164
094008 CT 230 (9.0) JRCP 1994 364 1496 AADTT > 1200
265363 MI 230 (9.0) CRCP 1993,94,95,97 355 1247
274055 MN 225 (8.9) JRCP 1994,97 300 1381
275076 MN 230 (9.0) CRCP 1997 344 1438
095001 CT 200 (8.0) CRCP 1995 323 1590
185518 IN 230 (9.0) CRCP 1994,97,98 365 3746
264015 MI 230 (9.0) JRCP 1994,96,97,98 341 1807
285006 MS 200 (8.0) CRCP 1993,94,95,97 361 1559
285805 MS 200 (8.0) CRCP 1993,94,95 361 2024
  1. Data year used in traffic data collection scenario simulation is bolded.
  2. Volumes and data days are for the year bolded.

 

Table 13. Identification codes for roadway functional classes as defined by LTPP database field FUNCTIONAL_CLASS in table INV_ID.
ID Roadway Functional Class
1 Rural Principal Arterial: Interstate
2 Rural Principal Arterial: Other
6 Rural Minor Arterial
7 Rural Major Collector
8 Rural Minor Collector
9 Rural Local Collector
11 Urban Principal Arterial: Interstate
12 Urban Principal Arterial: Other Freeways or Expressways
14 Urban Other Principal Arterial
16 Urban Minor Arterial
17 Urban Collector
19 Urban Local

Clustering is a mathematical approach for establishing similarities between different objects. Objects are described by their attributes. For this particular example, the objects are the LTPP WIM sites identified in Washington State (the 17 that met the study criteria) and the attributes are the distribution of the load of tandem axles (40 load bins from 8.90 to 355.9 kN (2 to 80 thousand pounds force (kips)). In this particular example, the attributes need not be normalized because they are all frequencies adding up to 100 percent. The next step is to compute a dissimilarity coefficient matrix. For this purpose, the so-called Euclidean distance e is used, which is defined as the distance between attributes for each pair of objects. If there were only two attributes, i and j, and they were plotted in a Cartesian coordinate system, the Euclidean distance eij would be the linear distance between the two objects defined on this plot by their coordinates. For more than two attributes, a similar definition would apply, the difference being that this would be a multidimensional plot (40 dimensional in this example). The Euclidean matrix for the annual distribution of tandem-axle loads in Washington State LTPP sites is shown in table 14. A value of the coefficient eij close to 0.0 suggests a similarity between the pair of objects, while higher eij values suggest a significant difference between the pair of objects. The next step is to construct what is referred to as a clustering tree, where pairs of similar objects are successively grouped together and compared with the remaining objects in order of increasing eij. The clustering tree for this example is presented in table 15 and plotted graphically in figure 6. This clustering method is referred to as Ward's Minimum Variance Method. All of these calculations were carried out using an add-on function to Microsoft Excel found in the statistiXL® library.(17) Figure 6 allows identification of groups of WIM sites in Washington State with similar distributions of tandem-axle loads, given a selected value of the Euclidean distance, and therefore, a level of acceptable dissimilarity. Three clusters were identified, assuming an eij value of 0.07.

For the two WIM sites to be analyzed (6048 and 1007 as indicated by arrows), the selected groups for obtaining regional WIM data are identified by the two uppermost squares in figure 6. Figures 7 and 8 show the frequency distributions of tandem-axle loads for these two groups and illustrate the distinct difference in the patterns between the two groups of WIM sites identified. For the LTPP sites selected for traffic scenario simulation, tables representing clustering trees by State are presented in Appendix B. This includes clusters with respect to the annual average tandem-axle load distributions and clusters with respect to the annual average truck classification and distributions (i.e., FHWA classes 4 through 13). The actual LTPP sites finally selected for obtaining regional AVC and WIM data are summarized in tables 16 and 17, respectively. The highlighted sites in these two tables are the ones used in the detailed sensitivity analysis of the NCHRP 1-37A design guide, while data from the other sites are used to estimate regional vehicle classification and axle-load distributions. As an example, the regional vehicle classification data for site 182008 were estimated as the average of the vehicle classification distributions for sites 181037, 183031, and 184042. For sites that exhibit no similarities with others (e.g., site 091803), the statewide average was assumed to be representative of the regional data.

 

Table 14. Euclidean distance matrix: Annual distributions of tandem-axle loads, Washington State LTPP sites.
Washington State LTPP Sites Analyzed
  1002 1005 1006 1007 1008 1801 3011 3013 3014 3019 3812 3813 6020 6048 6056 7322
1002                                
1005 0.016                              
1006 0.003 0.018                            
1007 0.005 0.011 0.013                          
1008 0.009 0.007 0.016 0.002                        
1801 0.021 0.025 0.018 0.030 0.034                      
3011 0.010 0.012 0.014 0.009 0.011 0.012                    
3013 0.016 0.031 0.012 0.033 0.039 0.006 0.017                  
3014 0.017 0.029 0.026 0.012 0.016 0.028 0.008 0.033                
3019 0.024 0.008 0.033 0.015 0.011 0.034 0.015 0.044 0.030              
3812 0.011 0.006 0.010 0.017 0.016 0.016 0.013 0.015 0.034 0.016            
3813 0.004 0.011 0.005 0.010 0.013 0.009 0.004 0.007 0.016 0.020 0.006          
6020 0.011 0.019 0.006 0.020 0.023 0.006 0.010 0.005 0.024 0.034 0.011 0.004        
6048 0.003 0.010 0.004 0.006 0.008 0.011 0.004 0.012 0.013 0.019 0.008 0.002 0.005      
6056 0.018 0.036 0.013 0.037 0.044 0.010 0.022 0.001 0.040 0.052 0.017 0.010 0.007 0.016    
7322 0.025 0.039 0.017 0.044 0.050 0.006 0.024 0.002 0.043 0.055 0.020 0.013 0.008 0.019 0.002  
7409 0.012 0.009 0.014 0.015 0.016 0.007 0.005 0.012 0.022 0.012 0.005 0.004 0.010 0.006 0.016 0.017

 

Table 15. Summary of clustering strategies and associated Euclidean distance: Annual distributions of tandem-axle loads, Washington State LTPP sites.
Cluster First Item Second Item Euclidean Distance
1 6056 3013 0.001
2 1008 1007 0.002
3 6048 3813 0.002
4 Cluster 1 7322 0.003
5 1006 1002 0.005
6 Cluster 3 3011 0.007
7 7409 3812 0.005
8 6020 1801 0.006
9 3019 1005 0.008
10 Cluster 7 Cluster 6 0.022
11 Cluster 8 Cluster 4 0.028
12 Cluster 10 Cluster 5 0.036
13 Cluster 9 Cluster 2 0.045
14 Cluster 12 3014 0.059
15 Cluster 14 Cluster 13 0.078
16 Cluster 15 Cluster 11 0.132

Figure 6. Annual distributions of tandem-axle loads, Washington State LTPP sites.

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Figure 7. Tandem-axle load distributions for the cluster of Washington State LTPP site 6048.

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Figure 8. Tandem-axle load distributions for the cluster of Washington State LTPP site 1007.

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Table 16. LTPP sites used for obtaining regional vehicle classification data.
State Code Vehicle Classification Cluster Sites
9 4008 4020 5001 - - - - - - - - -
9 1803 - - - - - - - - - - -
18 1028 5518 6012 9020 - - - - - - - -
18 4042 3031 2008 1037 - - - - - - - -
18 5538 5528 2009 5022 5043 3030 - - - - - -
26 1010 1001 1004 - - - - - - - - -
26 9030 9029 5363 - - - - - - - - -
26 7072 4015 3069 1012 1013 - - - - - - -
27 4055 1023 1028 1085 3003 4033 4040 4054 5076 6251 7090 9075
27 1016 1019 1087 3013 4037 4050 - - - - - -
28 2807 1001 1016 3087 3089 4024 5025 - - - - -
28 5006 3081 9030 7012 3094 3093 3019 3018 3099 5805 3091 1802
28 3085 3083 3090 - - - - - - - - -
50 1682 1002 1681 1683 - - - - - - - -
50 1004 - - - - - - - - - - -
53 1007 1005 1801 3014 3019 7409 - - - - - -
53 7322 6056 3013 3813 3011 1002 - - - - - -
53 6048 1006 1008 3812 6020 - - - - - - -

Note: The 30 sites used for the detailed sensitivity analysis are shaded.

 

Table 17. LTPP sites used for obtaining regional axle-load data.
State Code Tandem-Axle Load Cluster Sites
9 4008 1803 4020 5001 - - - - - - - - - -
18 1028 2009 3030 3031 4042 5022 5043 5518 9020 - - - - -
18 6012 1037 2008 - - - - - - - - - - -
18 5538 5528 - - - - - - - - - - - -
26 1010 1001 1004 1004 1012 1013 3069 4015 5363 7072 9029 9030 - -
27 4055 1023 1028 4054 5076 - - - - - - - - -
27 6251 4050 4040 4037 4033 1019 1016 9075 1085 7090 1087 3003 - -
27 3013 - - - - - - - - - - - - -
28 2807 1001 1016 1802 3018 3019 3083 3085 3087 3089 3090 3091 4024 5025
28 9030 3099 3094 3093 5805 5006 3081 7012 - - - - - -
50 1682 1002 1004 1681 1683 - - - - - - - - -
53 1007 1005 1008 3019 - - - - - - - - - -
53 6020 1801 6056 3013 7322 - - - - - - - - -
53 6048 1002 1006 3011 3014 3812 3813 7409 - - - - - -

Note: The 30 sites used for the detailed sensitivity analysis are shaded.

Simulating Traffic Data Collection Scenarios

As described in the literature review, obtaining traffic input to the NCHRP 1-37A design guide from short-term traffic samples involves considerable calculations in factoring the site-specific data available using representative regional or national vehicle distribution and axle-load distributions. TrafLoad(13) could be used to carry out these calculations; however, it accepts as input raw data (e.g., card-4 and card-7) and therefore, was not directly applicable to the daily summary data format used in this study. More important, TrafLoad could not be used to analyze all of the possible combinations of data used in simulating short-term scenarios from extended-coverage WIM data (1 month/season of data involves 34 = 81 combinations of months, as described later). Therefore, it was decided to develop customized software for computing the traffic data input to the NCHRP 1-37A design guide. The software developed is written in Microsoft®Visual Basic®. It reads daily traffic data summaries from the Microsoft Access database extracted from the CTDB and computes the traffic input elements to the NCHRP 1-37A design guide following the procedures described in the 2001 TMG.(3) Furthermore, it adopts the traffic ratio approach in factoring short-term counts, as described by NCHRP 1-39.(13) Accordingly, equations 15, 16, and 17 for factor ratios are used. (The subscripts i for vehicle class and l for direction were dropped for brevity.)

(15)

Equation 15. Equation. The monthly day-of-week traffic ratios are computed as the ratio of the mean annual day-of-week divided by the average annual daily traffic.

(16)

Equation 16. Equation. The monthly traffic ratios are computed as the ratio of the monthly average daily traffic divided by the average annual daily traffic.

(17)

Equation 17. Equation. The daily traffic ratios are computed as the ratio of the annual average day-of-week divided by the average annual daily traffic.

Where:

  • MDWTR = Monthly DOW traffic ratios.
  • MADW = Monthly average day of week.
  • MTR = Monthly traffic ratios.
  • MADT = Monthly average daily traffic.
  • DTR = Daily traffic ratios.
  • AADW = Annual average day of week.

For each of the traffic data collection scenarios, the software computes the mean and the standard deviation (SD) for each of the NCHRP 1-37A design guide traffic data input elements outlined in table 6. The methodology used for doing so follows.

Scenario 1-0: Site-Specific Continuous WIM Data

This scenario represents the most complete traffic data set for generating input to the NCHRP 1-37A design guide, and for this reason, it is defined as the truth in traffic data. For the 30 sites analyzed, WIM data coverage ranged from more than 299 days per year to more than 359 days per year. Following is an explanation of how the five traffic data input components to the NCHRP 1-37A design guide (refer to table 6) were computed:

  • Component 5 of the NCHRP 1-37A Design Guide Input (Axle-Load Distributions):
    • Browse the daily summary data table to obtain the number of days per DOW (from Sunday through Saturday) for each month that has traffic records.
    • For each month and DOW, sum the axle passes per truck class for each axle type and each load bin.
    • Divide each sum by the number of days of data computed above to obtain the average number of daily axle passes per bin, per axle type, per truck class for each DOW and month.
    • Average the number of daily axle passes per bin for the seven DOWs to obtain the monthly average number of axle passes by axle type, load bin, and truck class for each month.
    • Translate the number of passes per bin into load distributions (percent) by axle type, truck class, and month.
  • Component 4 of the NCHRP 1-37A Design Guide Input (Number of Axles per Truck):
    • Compute the average daily number of axles by axle type and truck class, regardless of load bin, over the 12-month period.
    • Compute the average daily number of trucks by class.
    • Divide the two values computed above to obtain the average number of axles by truck class and axle type.
  • Components 1, 2, and 3 of the NCHRP 1-37A Design Guide Input (AADTT, Truck Class Distribution, and MAFs):
    • For each month and DOW, sum the number of trucks by class.
    • Divide each sum by the number of days of data computed above to obtain the average number of daily vehicle passes by truck class per DOW and month.
    • Average the number of trucks by class for the seven DOWs to obtain the monthly average number of trucks by class per month.
    • Average the number of trucks for the 12 months to obtain AADTT by truck class.
    • Translate these average values into frequencies (percentage).
    • Add the number of trucks for all classes to obtain AADTT.
    • Compute MAFs by truck class using the data above and equation 5.

The procedure described above accommodates WIM traffic data sets with missing data days. For some of the WIM sites that have the largest number of missing days (i.e., 299 days of WIM data per year or more), additional assumptions had to be made:

  • Where entire months of data are missing, data are assumed to have values equal to the average of the data for the months available.
  • Where entire DOWs are missing for a particular month, data are assumed to have values equal to the average of the data for the available DOWs for the same month.

Scenario 1-1: Site-Specific WIM Data for 1 Month/4 Seasons

This scenario involves WIM data that cover 1 month in each of 4 seasons. It is simulated from the continuous WIM data set of the 30 sites selected and is carried out by computing all of the necessary traffic input to the NCHRP 1-37A design guide from random combinations of sets of 4 months, each from a different season (a maximum of 81 combinations is possible). Only months with more than 25 days of data were considered for this analysis. The challenge in simulating this scenario is that the traffic volume by truck class is not known for all months of the year. All that is known for the site is the volume for 4 months of the year. The following paragraphs describe the methodology used in obtaining each of the five traffic data components input to the NCHRP 1-37A design guide (table 6).

Component 3 of the NCHRP 1-37A Design Guide Input (MAFs):

There are a number of alternative algorithms for computing traffic volumes and, as a result, MAFs by vehicle classification for the months considered missing. The one selected for this study uses the average regional MAF values for all truck classes to estimate truck volumes by class for the missing months. This algorithm is explained in the following example, and it is demonstrated in table 18.

 

Table 18. Example of computing MAFs from regional data.
Month R MAF(all truck classes) Measured VOL by Class Estimating VOL by Class VOL by Class Estimated SS MAF by Class
January 0.8 900 - 900 0.85
February 0.9 - 8,489x0.9/8.06 = 948 0.90
March 1.09 - 8,489x1.09/8.06 = 1,148 1.09
April 1.05 1,100 - 1,100 1.04
May 1.12 - 8,489x1.12/8.06 = 1,180 1.12
June 1.15 - 8,489x1.15/8.06 = 1,211 1.15
July 1.1 1,200 - 1,200 1.14
August 1 - 8,489x1/8.06 = 1,053 1.00
September 1 - 8,489x1/8.06 = 1,053 1.00
October 0.99 950 - 950 0.90
November 0.95 - 8,489x0.95/8.06 = 1,001 0.95
December 0.85 - 8,489x0.85/8.06 = 895 0.85
Sum of four MAFs 3.94 Sum = 4,150 - AADTT= 1,053 Sum =12.00
12 (sum of 4 MAFs) 8.06 8,489  

Consider that for a given truck class, daily traffic volumes (VOL) are available only for January, April, July, and October (they add up to a volume of 4,150 vehicles). Given the average regional MAF values above, compute the sum of them for the available months (i.e., 3.94). This suggests that the sum of the regional MAF values for the 8 missing months is 8.06 (=12-3.94), which gives a total volume of 8,489 (= 4,150x8.06/3.94) for these months. This, in turn, allows estimation of the traffic volume of the missing months (e.g., February volume is computed as 8,489x0.9/8.06 and so on). Note that this approach preserves the traffic volume for the available months.

The group of sites used for obtaining the regional MAF data was identified as the agency-specific cluster that exhibited a similar truck classification pattern as the site under consideration (truck classification clusters are presented in Appendix B and summarized in table 16). This was deemed to be reasonable compromise between using agencywide average MAF data for all truck classes and MAF cluster data for individual truck classes. Furthermore, it was practical to implement since the monthly vehicle classification distributions are relatively stable (table 19), and thus identifying a cluster from 4 months of traffic data is realistic.

 

Table 19. Monthly versus annual vehicle class distribution, AVC cluster, Washington State site 6048.
Month Vehicle Class
4 5 6 7 8 9 10 11 12 13
January 2.3 34.0 5.6 0.1 7.8 36.4 3.9 1.2 1.7 7.1
February 2.3 34.2 6.1 0.2 7.9 35.5 3.8 1.2 1.8 7.2
March 1.2 37.3 5.8 0.2 5.4 36.1 3.8 1.4 1.7 7.2
April 1.4 41.7 5.9 0.3 5.8 31.6 3.6 1.1 1.6 7.0
May 1.5 43.4 6.3 0.3 6.4 29.8 3.4 1.1 1.5 6.4
June 1.5 42.6 6.2 0.2 6.9 29.2 3.8 1.1 1.4 7.0
July 1.6 49.4 5.8 0.3 7.4 24.4 3.2 0.6 1.2 6.2
August 1.4 49.5 5.7 0.5 8.6 22.0 3.2 0.5 1.3 7.2
September 1.9 45.2 5.8 0.2 9.5 25.0 2.9 0.6 1.3 7.8
October 1.5 45.9 6.2 0.3 7.0 27.4 3.0 0.6 1.2 6.9
November 1.6 46.3 6.3 0.4 4.9 29.0 3.5 0.6 1.3 6.3
December 1.6 46.0 5.4 0.4 4.3 30.3 3.6 0.6 1.3 6.5
Mean 1.7 43.0 5.9 0.3 6.8 29.7 3.5 0.9 1.4 6.9

Components 1, 2, and 5 of the NCHRP 1-37A Design Guide Input (AADTT, Truck Class, and Axle-Load Distribution):

Having established the volumes by truck class for the missing months, the algorithm used for obtaining traffic data input components 1, 2, and 5 was identical to that for scenario 1-0.

Component 4 of the NCHRP 1-37A Design Guide Input (Number of Axles per Truck):

The number of axles by axle configuration and truck class was assumed to be constant and equal to each statewide average for the sites analyzed. This assumption is justified considering that the number of axles for the most common truck classes (classes 5 and 9) is relatively constant. Tables 20 and 21 show the number of single and tandem axles per vehicle for the Washington State sites analyzed. It can be seen that the number of single and tandem axles for vehicle classes 5 and 9 varies only slightly between sites. This is not the case for vehicle classes 7 and 11; however, they account for less than 4 percent of the total truck volumes. Another reason for this assumption was that the number of axles per vehicle type (i.e., 4 by 10 matrix) had to be input manually to the NCHRP 1-37A design guide software, and therefore, assuming it to be constant for each agency, significantly reduced the data input effort.

 

Table 20. Number of single axles per vehicle, annual Washington State data.
Vehicle Class
Site 4 5 6 7 8 9 10 11 12 13
1002 1.59 1.99 1.00 0.67 2.40 1.21 1.21 4.52 3.78 2.39
1005 1.21 2.00 1.00 0.64 2.26 1.15 1.07 4.53 3.83 2.14
1006 1.36 1.97 1.00 1.08 2.26 1.14 1.10 4.61 3.02 2.37
1007 1.49 1.99 1.07 1.15 2.26 1.20 1.06 4.73 3.75 2.22
1008 1.21 2.00 1.00 0.98 2.24 1.28 1.22 4.86 3.45 1.90
1801 1.59 1.98 1.00 0.98 2.34 1.22 1.03 3.61 3.62 2.53
3011 1.25 2.00 1.00 0.73 2.38 1.03 1.03 4.33 3.21 1.76
3013 1.29 2.00 1.00 1.03 2.31 1.22 1.15 4.49 3.47 2.27
3014 1.65 1.99 1.23 0.94 2.56 1.14 1.07 4.35 3.62 2.29
3019 1.52 1.99 1.00 0.67 2.38 1.09 1.15 4.34 3.57 2.13
3812 1.81 2.00 1.00 0.44 2.59 1.10 1.07 4.34 3.65 1.82
3813 1.82 2.00 1.00 1.00 2.53 1.15 1.01 3.75 3.70 1.91
6020 1.26 2.00 1.00 1.25 2.24 1.13 1.35 4.66 3.34 2.25
6048 1.43 2.00 1.00 0.98 2.34 1.14 1.04 4.07 2.80 1.46
6056 1.41 1.99 1.05 1.24 2.31 1.25 1.12 4.21 3.48 2.06
7322 1.50 2.00 1.12 0.78 2.32 1.28 1.12 4.70 3.73 2.04
7409 1.71 2.00 1.00 0.97 2.16 1.09 1.12 3.97 3.58 2.38
Mean 1.48 1.99 1.03 0.91 2.35 1.17 1.11 4.36 3.51 2.11

 

Table 21. Number of tandem axles per vehicle, annual Washington State data.
Vehicle Class
Site 4 5 6 7 8 9 10 11 12 13
1002 0.73 0.06 1.00 1.00 0.66 1.88 1.06 0.67 1.10 2.21
1005 0.79 0.02 1.00 1.26 0.77 1.92 1.02 0.27 1.05 2.48
1006 0.72 0.03 1.00 0.84 0.75 1.92 0.99 0.96 1.21 2.06
1007 0.92 0.05 0.97 0.84 0.81 1.90 0.92 0.25 1.06 2.13
1008 0.79 0.01 1.00 1.06 0.76 1.85 1.06 0.27 1.11 2.04
1801 0.72 0.07 1.00 0.65 0.72 1.89 0.93 0.86 1.09 2.17
3011 0.76 0.01 1.00 1.28 0.58 1.98 1.06 0.47 1.32 2.45
3013 0.73 0.00 1.00 1.67 0.69 1.88 1.14 0.47 1.20 2.33
3014 0.36 0.01 0.89 0.13 0.61 1.93 0.93 0.26 1.11 2.34
3019 0.54 0.03 1.00 0.83 0.64 1.95 0.96 0.34 1.17 2.01
3812 0.21 0.00 1.00 1.54 0.42 1.94 0.96 0.39 1.11 2.52
3813 0.38 0.01 1.00 0.77 0.48 1.92 0.98 0.80 1.06 2.57
6020 0.75 0.01 1.00 0.90 0.76 1.93 1.34 0.41 1.19 2.36
6048 0.59 0.00 1.00 0.29 0.66 1.91 1.00 0.42 1.07 1.18
6056 0.77 0.01 0.98 1.35 0.71 1.87 1.14 0.56 1.23 2.25
7322 0.72 0.00 0.94 1.68 0.73 1.85 1.17 0.34 1.07 2.26
7409 0.54 0.01 1.00 0.51 0.84 1.95 1.04 0.63 1.19 2.34
Mean 0.65 0.02 0.99 0.98 0.68 1.91 1.04 0.49 1.14 2.22

Scenario 1-2: Site-Specific WIM Data for 1 Week/Season

This scenario was simulated in a manner similar to the one described under scenario 1-1. The difference was that only 1 week per season of WIM data was considered available. For each season, a week was selected at random, after excluding the dates involving national holidays and those having incomplete data. This simply yielded a higher number of combinations to be simulated (i.e., depending on data coverage, up to 20,736 combinations). Each week was assumed to be representative of the entire month. The handling of the remaining elements of the NCHRP 1-37A design guide input was identical to that described under scenario 1-1.

Scenario 2-0: Continuous Site-Specific AVC Data and Regional WIM Data

This scenario used only the vehicle classification information that is available from the 30 WIM sites being analyzed. NCHRP 1-37A design guide inputs 1, 2, and 3 were obtained in an identical manner as done for scenario 1-0. For input 4, the number of axles by configuration and vehicle class, the agencywide average was used for reasons explained earlier. Input 5, which uses the load frequency distribution by axle configuration, had to be estimated from regional WIM data. In doing so, it was assumed that although there are no site-specific WIM data, there is sufficient qualitative information for truck weights for the site to allow classification of it into one of the axle-load clusters determined within a particular agency. As a result, input 5 was obtained from the average WIM data of the appropriate cluster, rather than from agencywide WIM data.

Scenario 2-1: Site-Specific AVC Data for 1 Month/Season and Regional WIM Data

This scenario was simulated in a manner similar to that for scenario 1-1. The difference was that traffic data input 5, the load distribution by axle configuration, was obtained from regional WIM data as described under scenario 2-0.

Scenario 2-2: Site-Specific AVC Data for 1 Week/Season and Regional WIM Data

This scenario was simulated in a manner similar to that for scenario 1-2. The difference was that traffic data input 5, the load distribution by axle configuration, was obtained from regional WIM data as described under scenario 2-0.

Scenario 2-3: Site-Specific AVC Data for 1 Week/Year and Regional WIM Data

This scenario was simulated by assuming that the week of data considered available is representative of the month to which it belongs. After excluding those involving national holidays and those having incomplete data, weeks were selected at random, and subsequently, in traffic data input 3, the MAFs were estimated from the regional vehicle classification cluster corresponding to the site in question. Traffic data inputs 1, 2, and 4 were also estimated as per scenario 1-1. Finally, traffic data element 5, the load distributions by axle type, were obtained from regional WIM data as described under scenario 2-0.

Scenario 3-0: Continuous Site-Specific ATR Data, Regional AVC Data, and Regional WIM Data

This scenario consists of continuous site-specific vehicle counts for an entire year combined with regional AVC and regional WIM data. These vehicle counts include vehicle classes 1 through 3: motorcycles, passenger cars, and light four-tire trucks. Although no site-specific vehicle classification or load information is available, it was assumed that there exists qualitative information to assign the site correctly to one of the AVC clusters and one of the WIM clusters developed for the agencies analyzed; therefore, the percentage of trucks at the site (classes 4 through 13) was assumed to be equal to the average of the percentage of trucks at the sites that belong to the actual AVC cluster for this site. This allowed calculation of AADTT according to the method described under scenario 1-0. Traffic data input 2 was obtained as the average of the vehicle classification distribution for the sites that belong to the actual AVC cluster for the site. Similarly, traffic data input 3 was obtained as the average of the MAFs for the sites that belong to the actual AVC cluster for the site. Traffic data input 4, the number of axles by type and vehicle class, was assumed to be equal to the statewide average for the reasons described under scenario 1-1. Traffic data input 5, the load distribution by axle configuration, was obtained as the average of the data for the actual WIM cluster to which the site belongs. It should be noted that this scenario results in a far lower variation in traffic data input than most of the scenarios described earlier because it relies on continuous regional data for the majority of the input.

Scenario 3-1: Site-Specific ATR Data for 1 Week/Season, Regional AVC Data, and Regional WIM Data

This scenario was simulated in a manner similar to scenario 3-0. The only difference is that vehicle volume data are considered known only for 1 month for each of 4 seasons. Traffic data input 2, 3, 4, and 5 were obtained in a similar manner to scenario 3-0. Traffic data input 1, the AADTT, was computed as described under scenario 1-1.

Scenario 4-0: Continuous Site-Specific ATR Data, Regional AVC Data, and National WIM Data

This scenario is similar to scenario 3-0. The only difference was that the axle-load information from the WIM cluster was replaced with information from national average WIM data. The latter was assumed to be equal to the default axle-load distributions embedded into the NCHRP 1-37A design guide software. This assumption affected only traffic data input 5, the load distribution by axle configuration.

Scenario 4-1: Site-Specific ATR Data for 1 Week/Season, Regional AVC Data, and National WIM Data

This scenario was simulated in a manner similar to scenario 3-1. The difference was that the axle-load information from the WIM cluster was replaced with information from national average WIM data. The latter was assumed to be equal to the default axle-load distributions embedded into the NCHRP 1-37A design guide software.

Scenario 4-2: Site-Specific ATR Data for 1 Week/Year, Regional AVC Data, and National WIM Data

This scenario is a variation of scenario 4-1, where only a single week of data is available per year. As in scenario 2-3, 1 week was selected at random after excluding those weeks that involved national holidays or incomplete traffic data. This week was assumed to be representative of the entire year. As in scenario 3-0, regional AVC cluster data were used to compute percentage of trucks and average MAF values were used to obtain the traffic volumes by month and truck class. National WIM data (the default values in the NCHRP 1-37A design guide software) were used for traffic data input 5.

Scenario 4-3: Site-Specific ATR Data for 1 Weekday Plus 1 Weekend/Year, Regional AVC Data, and National WIM Data

This scenario involves ATR counts from 1 weekday and 1 weekend day. Traffic volumes on these days were weighted by 5 and 2, respectively, to compute weekly traffic volumes. All weeks that did not involve holidays or missing data were considered at random under this scenario. Subsequently, all traffic data input elements were computed as described under scenario 4-2.

Scenarios 4-4 through 4-7: Various-Coverage, Site-Specific ATR Data, National AVC Data, and National WIM Data

These scenarios are essentially identical to scenarios 4-0, 4-1, 4-2, and 4-3, respectively. The only difference is that traffic data inputs 2 and 3 were not computed from the regional AVC data, but rather from national data. For the latter, the default vehicle classification values embedded into the NCHRP 1-37A design guide were used. In doing so, the default classification distribution for truck traffic class (TTC) type 1 was arbitrarily selected and described as a major single-trailer truck route (i.e., predominantly class 9 trucks). The default MAF values embedded into the NCHRP 1-37A design guide were 1.00 for all months and vehicle classes. For each time coverage in site-specific ATR data, the method used for computing each of the traffic data input elements to the NCHRP 1-37A design guide was described earlier.

Estimating Traffic Input

The preceding discussion documents in detail the methodology and assumptions used in obtaining each of the five traffic data input elements to the NCHRP 1-37A design guide (table 6) for each of the 17 traffic data collection scenarios considered (table 8). A summary of the source of data used in computing each traffic data input element to the NCHRP 1-37A design guide is given in table 22.

 

Table 22. Summary of the source of traffic data input to the NCHRP 1-37A design guide for the selected scenarios.
NCHRP 1-37A Design Guide Input
Scenario AADTT Percent Trucks by Class Vehicle Classification Distribution MAFs No. of Axles per Truck Load Frequency Distribution
1-0 SS SS SS SS SS SS
1-1 SS SS SS From VC cluster State average SS
1-2 SS SS SS From VC cluster State average SS
2-0 SS SS SS SS State average From WIM cluster
2-1 SS SS SS From VC cluster State average From WIM cluster
2-2 SS SS SS From VC cluster State average From WIM cluster
2-3 SS SS SS From VC cluster State average From WIM cluster
3-0 SS From VC cluster From VC cluster From VC cluster State average From WIM cluster
3-1 SS From VC cluster From VC cluster From VC cluster State average From WIM cluster
4-0 SS From VC cluster From VC cluster From VC cluster State average National average
4-1 SS From VC cluster From VC cluster From VC cluster State average National average
4-2 SS From VC cluster From VC cluster From VC cluster State average National average
4-3 SS From VC cluster From VC cluster From VC cluster State average National average
4-4 SS National average National average National average National average National average
4-5 SS National average National average National average National average National average
4-6 SS National average National average National average National average National average
4-7 SS National average National average National average National average National average

Table 23 shows the number of possible time-coverage combinations analyzed for each scenario. Obviously, the continuous data coverage scenarios (i.e., scenarios 1-0, 2-0, 3-0, 4-0, and 4-4) involve only a single time-coverage combination and, as a result, yield singular estimates of the traffic data input elements of the NCHRP 1-37A design guide (table 6). On the other hand, the discontinuous scenarios yield one set of traffic data input elements per data coverage combination. Statistics for this traffic data input were computed and their range was established as a function of the desired level of confidence.

 

Table 23. Number of possible traffic sampling combinations by scenario.
Scenario Time-Coverage Combinations
1-0 1
1-1 81
1-2 20,736
2-0 1
2-1 81
2-2 20,736
2-3 48
3-0 1
3-1 81
4-0 1
4-1 20,736
4-2 48
4-3 480
4-4 1
4-5 20,736
4-6 48
4-7 480

For each confidence level, NCHRP 1-37A design guide simulations for the discontinuous time-coverage scenarios were conducted by considering the lowest percentile for all traffic input elements simultaneously (i.e., 1, 2, 3, and 5 as identified in table 6). The reason for considering traffic underprediction as critical is because it results in pavement designs that are thinner than required, which, in turn, would fail prematurely. The reason for specifying the lowest percentile of all traffic input simultaneously is because it allows computation of the statistical maximum error in pavement life predictions given a confidence level. As a result, it reflects the confidence that this level of error will not be exceeded, which, in turn, is the reliability in the pavement design process. In performing these NCHRP 1-37A design guide simulations, it was decided to keep the traffic growth rate constant for all vehicle classes (4 percent annually) to ensure comparable results between sites. The effect of the actual traffic growth rate on pavement-performance predictions for each site was studied separately and is detailed in Chapter 5 of this report.

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