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4.0 M.O.E. USER GUIDE This M.O.E. User Guide provides practitioners with techniques to evaluate truck weight enforcement activity and applies validated M.O.E.s that were developed and tested in the current research project. The user guide consists of two parts: sampling guidelines and a software data analysis tool. Sampling (Data Colleciion) Guidelines are applied to estimate the number of WIM data collection sites and required sample sizes required to measure an en- forcement effect. This guideline provides users with estimates for specified roadway classification and truck percentage conditions. Software (Data Analysts) Too! calculates and statistically compares M.O.E. val- ues between two observed enforcement conditions. This procedure also allows users to conduct an automated pavement design life analysis, estimating the theo- retical pavement-life effect resulting from differences produced by the two ob- served enforcement activities. It is Important to distinguish between procedural guidelines and a methodological tool. A guideline (i.e., a method by which to undertake a course of action, which may be modified at the discretion of the user) provides the user in this case with the starting point for determining site number and data-collection sample sizes. However, final sampling requirements in the applied evaluation watt depend upon observed data characteristics, due to statistical requirements for data stability (i.e., degree of measured variance). On the other hand, a too! (i.e., an instrument to perform an operation in a speci- fied manner) is to be strictly applied throughout the evaluation. In fact, the software too} in this case is designed to refine site-number requirements, on the basis of measured data characteristics, and to advise the user of final sampling requirements. in

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4.1 M.O.E Sampling (Data CotIection) Guidelines Sampling guidelines described in this section provide practitioners with straight- forwnrd data-collection requirements to measure enforcement effects using the validated M.O.E.s. This guideline provides users with estimates of observation site numbers and associated truck sample sizes. These estimates are provided for specified roadway classi- fication and truck percentage conditions. Statistical M.O.E. sampling requirements were based on an analysis of nationwide WIM data. This developmental analysis examined M.O.E. data generated for representative locations (i.e., exhibiting prerequisite highway functional classification and truck mix crite- ria) and determined the minimum number of observation sites required to produce repre- sentative M.O.E. distributions. Based on these results, M.O.E. sampling guideline proce- dures were developed to enable users to estunate equivalent sampling requirements. Sampling guidelines are directed toward WIM database gathering. It must also be emphasized that the soundness of the WIM input data and its subsequent analysis to measure the effectiveness of truck weight enforcement is highly dependent on calibration and maintenance of that equipment. Users of this Sampling Guide are not expected to apply expertise in the area of statistics. However, due to the fact Mat this guide was developed via the application of various statistical concepts that affect its output, two statistical concepts and their appli- cation in the guides development are briefly explained as follows. Sampling requirements contained in the guide utilized two statistical concepts, Level of Significance, and Power of Test. Each of these terms is defined as follows, only as a matter of information for users of this guide. Level of Significance refers, in this case, to the probability that the user is willing to risk the error of rejecting a valid change in M.O.E. occurrence. In statistical terminology,

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the Level of Significance is the maximum probability with which we would be willing to risk a Type I error. A Type 1 error occurs when a true hypothesis is rejected, i.e., that base- line (no enforcement) versus enforcement M.O.E. vanable sets are statistically different. The .05 Level of Significance was applied in Me development of this guide. Power of Test refers to the likelihood of making a correct statistical assessment, i.e., that the proper hypothesis is accepted, statistically speaking. The issue is to what extent is the user milling to risk accepting an invalid change in M.O.E. occurrence. In statistical ter- minology, the Power of a Test is the maximum probability with which we would be willing to risk a Type 2 error. A Type 2 error occurs when a false hypothesis is accepted, i.e., Mat baseline versus enforcement M.O.E. variable sets are not statistically different. The .80 Power of Test was applied in the development of this guide. 4.~.~. Sampling Observation Levels Separate observation levels for sampling truck-weight violations were devised in order to meet the diverse evaluation requirements of varied truck weight enforcement op- erations. Three designated sampling observation levels are as follows: (1) statewide or regional, (2) highway corridor or local level, and (3) spot or location-specific. Figure 1 on the next page is a conceptual representation of the three designated observational lev- els. At the broadest level, the implementation of revised regional or statewide policy may require sampling over a vast geographic area, covering hundreds of square miles. At the opposite end of the spectrum, spot truck-weight enforcement procedures are fre- quently required due to location-specific factors, e.g., pertaining to local hauling condi- tions. Finally, a major concern for enforcement and highway agencies is weight-law compliance along specific highway corridors. The critical nature of weight monitoring alone corridors stems from a number of factors, including trucker avoidance of weight enforcement and costly pavement damage to local highways. 12

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t: :. A: ~ :-::::: A::::::.:: :~:: A:: ::::::::.: ::: A. ~: ~: :-: ::: :. : ::: : it: ~ . ~:: . ~ I:: ~ :::::: . A::: : :: :: :: ::::. ::.: :::: ::::: - .~;(ide.J' : .: .... ^.R.eg':ona.'.l.-'.::. ': Corridor't-~ ~~ at' ~ 'at I.,: :., :.: :. .-. -. ,., : .-. : :: . : -: . . -. - .:. .:: :, . ~ Location _ _ . ................ . .. ... - ...... . . . . _ . ..... _ . ~ Figure ~ . Illustration of Varied Data-sampling Observation Level Concept 4.~.2 Statewide or Regional M.O.E. Sampling Statewide or regional M.O.E. sampling is applied to evaluate any truck weight en- forcement program that affects large geographic areas which exceed the bounds of a defin- able highway corridor. The derivation of sampling requirements was based on actual ob- served statewide M.O.E. distributions; however, this guide is also applicable for smaller geographic regions. Site number requirements contained in this guide indicate minimum numbers to produce representative results for a designated region. Data collection site re- quirements are designated on the basis of regional characteristics, i.e., highway functional class and associated truck percentage combinations, which comprise the area under study. An example application of this procedure is shown in Section 4.~.3 ofthis report. WIM Data Site Number Requirements Guidelines for determining the required num- ber of observation sites for a statew~de/regional study of truck weight enforcement e~ec- tiveness were determined on the basis of observed M.O.E. distributions2 from representative See Appendix F. 13

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nation-we locations. Site number requirements for a designated region were based on the region's composition in terms of specified highway functional classification and associated truck percentage criteria. The user's deteITnination of study site numbers shall commence via the application of the guidelines shown in Table 2, which specify site number requirements for each func tional-cIass/truck-percentage category. Site numbers indicated in the table are intended as a starUng point for establishing final regional observation site number requirements. The ~ta-analysis software generated in this project is designed to refine site number requirement based on individual user's specific data characteristics. Table 2. Minimum Site-number Guideline for Selected M.O.E.s in State/Regional Truck Weight Enforcement Evaluations Rural Interstate < 15 % Trucks 15to30% Trucks , , > 30% Trucks Rural Primary Arterial < 9 TO Trucks 9 to 30 % Trucks l l > 30% Trucks Rural Minor Arterial Urban Interstate < 9% Trucks . . TANDEM AXLE :- i: i: ~ -: VIOLATIONS ~::~:: --: i: ~ --SINGLE- Amp- E-- :-~0~4T~ -~- . ~ ! ~ EXPRESS : ~E~ --- : ~:~ Urban Primary Arterial < 9% Trucks > 9% Trucks , , NOTE: flue accompanying NCHRP Project 20-34 software generates site number requirements based on user's data. The statewide/regional M.O.E. sampling procedure involves two preparatory steps. First, the geographic area, e.g., jurisdictional territory, to be affected by the enforcement program under study must be clearly defined. Second, the highway network within the de fined study region must be reviewed to determine its composition, in terms of route fi~nc tional classification and associated truck percentage as a function of overall traffic volume, on each affected route. 14

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The initial number of required study sites is then determined on the basis of corre- sponding site-number designations shown in Table 2, subject to revision by the associated software. The total number of study sites In a given region wait be the sum of those applied in each functional class and truck-ratio which are represented in the region, as demonstrated In the next paragraph. Each functional class represented In the region under study must be included in the array of designated observation sites. For example, when designating We primary M.O.E. of interest to be the "Proportion of Gross Weight Violations", then the number of required sites for each highway category watt be denved from numbers shown in the left-most column of Table 2. That is, at least three data collection sites are required to represent Rural Interstates with less Man ~ 5 percent trucks, six to represent Rural Interstate s with ~ 5 to 30 percent trucks, etc. The total number of sites for the study region wall be equal to the sum of site numbers for all functional- cIass/truck-percentage categories represented In the region, i.e., 36 sites. This procedure is illustrated In Me example application of a regional sampling plan development shown in section 4.~.3. It is important to note a number of user precautions and associated considerations underlying the development of site numbers contained in Table 2. These caveats relate to the analysis and application of nationwide representative data used to estimate requ~re- ments for conducting a regional truck weight enforcement evaluation. First, the nationwide analysis determined that a single observation site, within selected functional-cIass/truck-percentage categories, was occasionally sufficient to sta- tistically detect certain enforcement effects. However, application of sound sampling strategy to a regional enforcement study requires a significant degree of generality to en- sure its validity, therefore, Table 2 mandates a minimum of two sites for each functional highway classification condition.

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Second, site number requirements outlined in Table 2 were based on observed M.O.E percentage reductions found to be associated with enforcement activity3. However, for situations in which an observed enforcement activity is expected to produce greater or lesser percentage M.O.E differences, an appropriate adjusunent to the number of observa- tion sites would be required to statistically measure the effect. For example, in a given re- gion where 7 data collection sites may be required to detect an LO-percent reduction in gross weight violations, only 5 sites would likely be required to detect a 20-percent reduction. Importantly, with the current application, the user wail be appropriately informed of the level of affected M.O.E. change (and the associated number of required sites to validly observe this effect) via application of the software package accompanying this guide. Ike software application is explained in Section 4.2 of this report. Third, site numbers designated in Table 2 were based on measured statistical M.O.E. distributions. By taking into account normal sample sizes and associated variability of these M.O.E.s, they indicate the number of observation sites required to capture representative M.O.E. distributions. However, a number of application-specific considerations are neces- sary in He user's interpretation of the table. Specifically, truck weight surveillance over a large geographical area may logically require larger site numbers Han indicated In He table. For example, many cells in He table indicate the necessity of only 2 or 3 study sites, given certain highway classification and truck ratio conditions. Yet, in He case of a statewide en- forcement program over a very large area, the limitation of 2 or 3 study sites may be consid- ered inadequate. Thus, the final designation of observation sites must consider prevalent conditions, e.g., specific hauling and commodity demands that affect truck-loading operations and the subregional areas to which they apply. Specifically, He user is cautioned against combin- ing sites characterized by known non-homogenous loading conditions when applying the sampling procedure. 3 See Appendix F. Tables F-40 through F-42. 16

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Finally, as previously noted, Table 2 is a guideline (i.e., a procedure by which to undertake a course of action, which may be modified at the discretion of the user) to pro- vide the user with the starting point for determining site number and data-collection sam- ple sizes. Its final application relies on engineering judgement in the context of specific study situations. Designation of Data Collection Periods In view of known commodity shipping pat- terns, both weekend and weekday data collection periods are recommended in applied re- gional M.O.E. sampling efforts to evaluate truck weight enforcement programs. Impor- tantly, designated data collection periods need to be sensitive to seasonal conditions, e.g., agncultural commodity hauling patterns. A minimum two-day data collection duration is required at each site for each observed enforcement condition. Based on NCE~P Project 20-34 findings, the user is advised to expect maximum violation to occur during the early morning hours, e.g., 3 a.m. to 7:30 a.m. on weekdays, and during Me late evening hours on Sundays. Minimum site-specific truck sample sizes are shown In Table 3 for designated com- binabons of highway functional class and associated truck percentages for designated M.O.E.s. Sample size estimations shown in the table are based on the requirement to de- tect differences in truck proportions exhibiting the array of generally applied M.O.E.s at the .05 level of statistical confidence. 4.~.3 Example of a Regional M.O.E. Sampling Application Consider the hypothetical example of a geographic region with a distribution of 100 WIM data collection sites as shown in Table 4. This distribution was estimated on Me basis of traveled vehicle-miles by functional cIassification4 with adjusunents for traf- fic monitoring prioritization. 4 U.S. Department of Transportation, Bureau of Transportation Statistics, National Transportation Statis- tics, Washington, D.C. 1996 17

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Table 3. Minimum Site-specific Number of Required Truck Observations . . :~C~.~SS' -I ~ -'' ~'].m9D-Mid, - .,~, :-:,-. , .~ .-A- -- :- :~ ~ .. .. . Rural Interstate < 15 % Trucks 175 l5to 30 % Trucks 300 > 30% Trucks 200 Rural Primary Arterial < 9 % Trucks 225 9to 30 % Trucks ~325 > 30% Trucks 100 Rural Minor Arterial 200 Urban Interstate~ < 9% Trucks 100 > 9% Trucks 200 Urban Primary Arterial < 9% Trucks 125 > 9% Trucks 100 * Over a minimum 2-day data collection period. The assignment of available WIM sites to monitor an ongoing regional truck weight enforcement program, according to the scheme previously shown in Table 2, pro- duces the sampling scheme shown in Table 5 on the next payee Table 4. Available WIM Monitoring Locations in Example Sampling Application A...::.. :',,2~' ?.,, .. i.,x;,: j,. ,.":; ~ i?.. be. ,,... :.:.A,.,.... ~ '2'".~. .Id~.. Bilge''"' i"'''' - ~A~...~.S~ Rural Interstate < 15 % Trucks 15to 30 % Trucks _ > 30% Trucks . Rural Primary Arterial < 9 % Trucks 9to 30 % Trucks > 30% Trucks Rural Minor Arterial Urban Interstate < 9% Trucks > 9% Trucks Urban Primary Arterial < 9% Trucks > 9% Trucks TOTAL 4 8 4 7 7 14 15 8 8 11 100 18

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An examination of Table 5 indicates that of the 100 available WIM-monitor~ng sites, only 36 sites are required for region-wide monitoring of two M.O.E.s, i.e., Gross Weight Violations, and Tandem Axle Violations, on all highway fi~nctional-class/truck- percentage categories. In order to obtain a non-biased estimation of truck weight en- forcement effects, the user agency is advised to assign data collection locations in a ran- dom fashion (within appropriate fi'nctional-class/truck-percentage categories) when all available WIM installations are not statistically required for the evaluation. A larger number of data collection sites is required within a region to statistically represent less-frequently-occulting M.O.E.s (See Appendix F Sampling Plan Develop- ment). Consequently, in the cuIrent example, the latter two M.O.E.s required more data collection sites within certain fi~nctional-class/truck-percentage categories than were available. In these instances, the percentage of available WIM sites is indicated In the appropriate cells of Table 5. For example, while 8 sites were previously suggested in the Table 2 Guide as the minimum number of sites to detect enforcement effects in terms of Single-Axle Violations, the 4 available sites comprise 50% of this requirement. In such instances, Me regional evaluation is necessarily limited by WIM-site availability, and the issue site selection bias defers to applied site-location decision rationale. Table 5. Reco~runended WIM Data Collection Site Distribution for ~> ~_~-~ < ~ Rural Interstate < 15 % Trucks 15 to 30 /0 Trucks > 30/0 Trucks Rural Primary Arteriai < 9 /0 Trucks 9 to 30 /0 Trucks > 30/0 Trucks Rural Minor Arterial Urban Interstate < 9/n Trucks 2 2 Urban Primary Arterial < 9/0 Trucks > 9/0 Trucks _ TOTAL 19 4 (50%) 8 (38%) 4(31%) 7 (64%) 7 (29%) 14 (58%) ~ 31~) T 74 4 (44%) 8 (25%) 4 (12%) 7 (47%) 14 (93%) 9 8 (80%) 15 5 (10%) 1 1 (79/0) 87

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The non-availability of 100 percent of the Sample Guide's recommended site numbers does not necessarily mean than the region can not be evaluated in teens of the latter two M.O.E.s in this instance. Conversely, more sites may be needed in some in- stances than indicated by the Table 2 Guide. The reason is that the exact number of re- quired sites is determined by the data variance that is actually measured. Again, we em- phasize that site numbers indicated in Table 2 are guidelines, based on nationwide obser- vations of expected M.O.E.s vanances, and these estimates are prescribed as the starting point for development of the final sampling plan. Precise site number requirements are determined via application of the data analy- sis software developed in this project, the Truck Weight Enforcement Evaluation Too! (TWEET), described in Section 4.2 of this report. This software computes customized site number requirements based on the user's collected data. Specifically, it performs site-number requirement calculations based on actual measured variances, as is statisti- cally appropriate. Thus, this process provides the necessary site-number refinement cal- culation to define final sampling requirements. However, it can not replace the Table 2 Guide, as the user needs initial estimates for evaluation study planning purposes. The data analysis software contains a "Sampling Guide" dialog box that computes site number requirements for various levels of statistical precision (see Figure 2~. The ex- ample dialog box in the figure hypothetically considers data collected at 36 sites. This is He minimum number of prescribed sites in Table 2 assuming that the study region con- tains sites in all eleven functional-cIass/truck-percentage categories. This software sam- pling aid prescribes site-number requirements as a function of He specific enforcement- program effectiveness threshold, i.e., designated percent change in specific M.O.E.s, that the user wishes to consider. For example, looking at site-number requirement shown in the figure for Gross Weight Violators M.O.E., we see that if users want to detect an en- forcement effect based on an expected 40-percent violation reduction, only two data col- lection sites are required. However, to apply a more rigorous statistical requirement, for example a statistical test that is sensitive to a ten-percent reduction, seven sites would Hen be required. 20

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File Name: enforce.p~n Directories. ] .c:\tweetl 2 List Files of Type: Data files (' tWI, -.pi Olives: ...... 3c: 1uS-DOS_61 ~ Figure 9. The "Data File for Enforcement Condition" Dialog Box The Pavement Analysis dialog box (Figure 10) provides the user with an option to conduct a pavement design-life enforcement-effects analysis. The program asks for specific (and detailed) pavement design data. Because of the complexity of the pavement design-life analysis, He user has the option of skipping the pavement analysis simply by clicking the 'skip pavement analysis' option. Depending upon whether the user selects Flexible or Rig~d pavement, different variables appear in the Pavement Characteristics portion of the dialog box. This box will prompt the user for appropriate pavement design parameters. A comprehensive "Help" screen associated with the Pavement Analysis Dialog boxes explains the design theory, including He AASHTO design equations, underlying He computations utilized in the software. As further assistance to He user, Appendix E to this report contains a comprehensive explanation of relevant pavement design considerations and background references regarding overweight axle effects on pavement life. 29

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Please enter information about the characteristics of your pavement. If you are unsure about any of these, you may use the default values. ~ Skip pavement analysis . ... _- .. .._ ~_.. ......... ~__., rS erect Pavement lulaterial- - fib Flexible [i e. Asphalt] C, Rigid [i e., Portland Cement Concreted Flexible Pavement Characteristics Please enter values for each of the following pavement variables. If you do not know one or more of the values for your particular pavement use the defaults. as they were chosen as the most probable values for a flexible pavement. If you do not know the value of SN. but you do know the materials which comprise: your pavement, press the "Calculate SN .~.' button, and TWEET will compute SN based on the material composition of the pavement. SN: ~ I Po 14-2 1 1 1 hi R 15000 ZR: |-1 64 So: .35 Figure 10. The "Pavement Analysis" (Flexible Example) Dialog Box 1 Flexible pavements are discussed first. Default values are shown on the dialog box for the following parameters. . SN Pavement Structural Number. TWEET offers Me option of computing this variable based on input values provided by the user. pa Initial Serviceability Index pi Tenninal Serviceability Index MR Default Resilient Modulus ZR Standard Normal Deviate corresponding to design reliability SO Standard Deviation associated wad pavement performance prediction The above parameters are defined arid their design Implications are explained In detailed 'Help' screens in the software. Because of the importance of the pavement's Structural Number (SN), TWEET provides the user with alternative approaches to its calculation. First, the user may accept 3Q.

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the commonly applied value shown as the default. Second, the user may apply a known value for SN, based on his knowledge of the site. Third, if the user knows the material composition of Me pavement, TWEET can automatically calculate the SN value. In this case, the user clicks on 'Calculate SN', and the Automatic Calculation of SN dialog box appears as shown in Figure ~ I. The dialog box shown in We figure allows the user to select We appropriate surface, base, and sub-base charactenstics, i.e., pavement layer thickness (in inches), and strength coefficient. According to We specified matenal type, the program wait suggest We most appropriate default Strength Coefficient. Pavement materials and design personnel who curl this software have the option of overriding default values, depending upon their own knowledge of pavement matenals and design procedures along win specific pavement characteristics associated wad We truck weight enforcement location. ._ _ . ~. ......... ....................... ...... . - ~ ;~ A: lo,:- ;~ :` pi ~ !~ . . . ... ...... . . .... . . .. ..... ..... . , , . .... it. ,. ~ ~ . . ... .... . ............................................................................ Please enter the correct information about your parement by checking the radio buttons below. and press OK when you are finished TWEET will then calculate your pavement's structural number {SN] and set that number in the SN entry field in the Pavement Analysis dialog box Each radio button has a default Strength coefficient associated with it; however if you know these values exactly you can enter them direct4 in their ~espet:tive fields. Press Help for more information. -Surface Characteristics ~.~.High stability asphalt concrete. _~ ~.~_~_.~.~ C) Low stability asphalt concrete Thickness: ~| Strength coefficient: [-~4 | 1 -Subbase Characteristics ~ Sandy gravel C' Sand or sandy clay Thickness: l . Strength coefficient: .11 1 - Base Characteristics At. Gushed Stone C; Sandy gravel ~ Cement treated stone G.4sphalt treated stone ~ Lime treatment Thickness: l l Strength coefficient: .14 -Second Subbase Characteristics ~ Sandy gravel C' Sand of sandy clay Thickness: [= Strength coefficient ~ 11 Figure ~ i. The "Automatic Calculation of SN" Dialog Box ~3 ~

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In the event Me user had selected Rigid Pavement, the Pavement Analysis (Rigic! Pavements dialog box would appear, as shown in Figure 12. ; vocables. If you do not l:now one or ~ e at the wa yes for i ~ yo ~ particular pavement use the defaults, as they were chosen as the most probable values for a rigid pavement. k: ~ 00 1 Please enter information about the characteristics of your pavement. If you are unsure about any of these, you may use the default values. Skip pavement analysis. -Select Pavement 1~1ate~i~1 C~ Flexible [i.e., Asphalt] , _ ~. ~_ ~_ Nonrigid [i e, Portland Cement Concrete]; ........ _ . ~_ _ ~ ._ -Rigid Pavement Cha'.cteristics - Please enter values for each of the following pavement E- 5000000 1 id, 11 1650 1 ; Po- 14-5 ~ D: 11 To | Pt i-5 l Figure 12. "Pavement Analysis" (Rigid Example) Dialog Box This dialog box provides the following design values for user application: k E D s c Po Modulus of Subgrade Reaction PCC Elastic Modulus Slab Thiclmess (inches) Modulus of Rupture Initial Serviceability Index Terminal Serviceability Index As was the case with the Flexible Pavement Charactenstics box, the most likely de- fault design values have been provided in the case of Rigid Pavements. The user has the option of manually entering values specific to the highway study site. 32

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The program wall then read the WIM data files and perform all calculations. Unless data files are extraordinanly large, these calculations should take no more than a few seconds. An animated graphic Status dialog box (Figure 13) will appear to advise of the program's progress on the computational process. The truck on Me screen moves from left to right on the roadway section as the calculation is completed. Calc^~;n~.. 71 ~ din o% 108~ _1_ ~ _ . . . Figure ~ 3. "Calculation Status" Dialog Box 4.2.2 Viewing Results of Calculations Once We calculations are completed, the user wall be presented with a series of "out- put" dialog boxes that display calculated values based on input data. The first M.O.E. out- put dialog box, Severity of Violations (Figure 14 on the next page), also reports sublunary information, i.e., enforcement condition, highway type, total vehicle, and Muck sample. The first part of the dialog displays Me observed number of violations, i.e., gross vehicle weight, single axle weight, tandem axle weight, tandem axle weight, and Bridge Formula violations. The second dialog displays the average number of overweight pounds (or Metnc equivalent) for each grouping noted above. The Calculatecl Percentages of Overweight Trucks dialog (see Figure 15 on the next page) displays the calculated percentages of overweight trucks in the sample. It lists four calculations based on the data files, i.e., (1 ~ percentage of trucks over the legal gross weight limit, (2) percentage of trucks over the single axle weight limit, (3) percentage of trucks over the tandem axle weight limit, and (4) percentage of Sucks violating the Bridge Formula. -$ ~ ~-

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Enforcement Condition: Baseline Condition -Number of overweight trucks Gross Vehicle Weight: Single Axle Weight: Tandem Axle Weight: T'idem Axle Weight: Bridge Formula Violations: 33 82 47 o 13 out of out of out of out of out of 266 266 266 266 266 trucks total trucks total trucks total trucks total trucks total -average Ibs overweight ~ 1 Gloss Vehicle Weight: 6872 Ibs overweight Single Axle Weight: 1304 Ibs overweight Tandem Axle Weight: 3393 Ibs overweight T'idem Axle Weight: Bridge Formula Violation: o 2307 Ibs o~relweight Ibs overweight Figure 14. "Seventy of Violations" Dialog Box Enforcement Condition: Baseline Condition Percentage of trucks over the legal gross weight limit: 1~41 Percentage of trucks over the legal single axle weight limit: Percentage of trucks violating the legal tandem axle-weight limit: 17.67Z Percentage of trucks Violating the legal t~idem axle weight limit: Percentage of trucks violating the Bridge Formula: 30.83:t O OOZ ~ 89Z Figure ~ 5. "Calculated Percentage of Overweight Trucks" Dialog Box The Violation Data by Truck Classification dialog box (Figure 16) indicates viola- tors, by Duck number and percentage, for each class of Duck. This dialog box displays

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violation information, broken down by truck classification. This information is usefiA in determining violation distributions according to truck type. 1. i,` ~ 1,..e L i;. :,, ~ ~ ; Enforcement Condition Baseline Condition Select truck classification: , i, ,. . . i: . iE2 I: . - . . :: . , . E: 1. I., ~2 At . ' -Violation Data for Selected Classification: ------ l~lotorcycles Passenger Cars O the' Two-Axle, Fou'-Tire Single Unit Lehigh Buses Two-Axle, Six-Tire. Single Unit Trucks Thee-Axle Single Unit Trucks '` Four or Hare Axle Single Unit Trucks Fou' or Less Axle Sinule Trailer Trucks ~ _ ~ ~ ~_~ Total Number of Tracks: Number of Violators: Percentage Violating: Proportion of total violations committed by this type: 211 78 36.97:t 87.64z Figure 16. "Violation Data by Truck Classification" Dialog Box The dialog consists of two parts: Truck Classification List Box This box lists all of the truck classifications which were input by the user during the beginning of the analysis, or if Me default was selected, the FHWA 13-type classifications. Violation Data This part of the dialog lists violation data for the currently selected truck classification. First, the Total Number of Trucks field displays the number of trucks of the selected type which were in We sample (regardless of whether they were violators). Second, the Number of Violators field lists the number of trucks of the selected type that violated the weight limits. Third, the Percentage Violating field lists the percentage of trucks of the selected type which were violators (this percentage is simply the Number of Violators divided by the Total Number of Trucks). Finally, the proportion of the tote sampled violations represented by the selected truck class is indicated. 35

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The Breakdown of Violations by Day-of-Week (not shown) dialog then displays the percentage of violations occuning on each day of the week. The dialog simply lists each day of the week, and next to it lists the percentage of all violations which occuITed on that day. The Breakdown of Violations by Time-of-Day (not shown) dialog then displays the percentage of violations occulting at different hours of the day. Because it would be overly complex to display the percentage of violations occurring at each of Me 24 hours of the day, the five hours with the most violations are listed. If it is necessary to know what percentage of violations occulted at every hour of the day, the Print option will be of use. The printed copy of the data, urdike We on-screen display, does display We per- centage of violations for each hour of the day. The f?SAL Data dialog box (Figure 17) indicates average ESAL calculations using the FHWA Traffic Monitoring Guicle procedure according to We number of axles. This dialog also indicates computed Excess ESAL violations by Suck axle-count. Enforcement Condition: it. I! .., i: if? ;} . ok? ? i Baseline Condition -Average Number of ESALs: Truck Type: Average ESALs: Average Excess ESALs: 2-axle trucks .2468189 0 3-axle trucks .4756302 0 4-axle trucks 2083355 0 5-axle trucks 1.622841 1.573099 6-axle trucks 0 0 7-axle tucks 0 0 All Trucks: 1.?419015 1.573099 Figure ~ 7. The "ESAL Data" Dialog Box 36

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Now, TWEET begins its presentation of the data analysis results. The Comparison of Enforcement Conditions dialog (Figure I8) indicates to the user whether or not the ob served M.O.E. differences are statistically significant. This dialog box contains results of applied statistical significance tests to the computed M.O.E.s and indicates to the user whether or not Me observed differences are significant. Separate tests of statistical signifi- cance are applied to M.O.E.s depending upon whether the measure was calculated as a mean (i.e., average gross weight violation) or a proportion (i.e., proportion of gross weight viola- tors). Significance tests are applied at Me .05 level of statistical confidence. This dialog allows you to determine the effectiveness of your enforcement activity by comparing the differences in the calculated violation data for each enforcement condition. For each I.IOE, a check has been placed in either the "Significant.' or "Non-significant " column. This shows whether the difference in the calculated value of that MOE between the first and second enforcement condition is statistically significant. Press Help for more information insignificance of Proportions and l~leans . HOE Percentage of Gross Weight Yiolators Percentage of Single Axle Weight Yiolators Percentage of Tandem Axle Weight Yiolators Percentage of Tridem Axle Weight Violators Percentage of Bridge Formula Violators Average Pounds over the Gross Weight Limit Average Pounds over the Single Axle Limit Average Pounds over the Tandem Axle Limit Average Pounds over the Trident Axle Limit Average Pounds over the Bridge Formula Limit Average ESALs Average Excess ESALs Sionificant Won-sionificant f Figure I8. "Comparison of Enforcement Conditions" Dialog Box The Sampling Guide dialog box (Figure 19 on page 39) page is an aid to determine how many sites wall be needed to be surveyed in order to detect regional changes for desig- nated M.O.E.s given specified levels of statistical confidence. The user is first presented with a "Sampling Guide Options" table, allowing the option of specifying two parameters 37

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related to the precision of Me statistical estimate. These are the desired [eve! of Significance and Me Power of Test, explained as follows. [eve! of Significance refers, in this case, to Me probability that the user is willing to risk the error of rejecting a valid change in M.O.E. occurrence. In statistical terminology, the Level of Significance is the maximum probability with which we would be willing to risk a Type ~ error. A Type 1 error occurs when a true hypothesis is rejected, i.e., that base- line versus enforcement M.O.E. variable sets are statistically different. In practice, a sign~fi- cance level of.O5 or .01 is customary. Power of Test refers to Me likelihood of making a correct statistical assessment. This is achieved when the proper hypothesis is accepted. At issue is the extent to which the user is willing to risk accepting an invalid change in M.O.E. occurrence. In statistical te~mi- nology, the Power of a Test is the maximum probability with which we would be willing to risk a Type 2 error. A Type 2 error occurs when a false hypothesis is accepted, i.e., baseline versus enforcement M.O.E. variable sets are not statistically different. The main feature of the Sampling Guide dialog box is a table indicating the number of sites which are required for data collection if specified levels of M.O.E.s changes (i.e., 5, 10, 15, or 20 percent) are to be detected. These numbers are based on TWEET's analysis of the measured statistical characteristics (e.g., variance) of Me observed M.O.E.s. The user will note Mat fewer sites are necessary for larger differences. This effect is due to the fact that smaller differences in real-world truck-weight enforcement compliance are subtler and therefore require more statistical rigor to detect. The final dialog box (Figure 20 on the next page) presents results of Me Pavement Effects Analysis. Results contained in this dialog box are based on a theoretical pavement design-life effect, associated with differential enforcement-related ESAL loading conditions. Had the user opted to include the pavement design-life effect computation, this screen would be displayed. Displayed information consists of the calculated pavement ESAL ca- pacity, the estimated pavement life under both observed enforcement conditions, and esti 38

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mated percentage pavement-life charge due to the observed ESAL-Ioading difference asso ciated with Me enforcement activity. -Sampling Guide Options - Level of Significance: Power of Test: | 0.80 s ~. . , This guide is intended to assist you in determining the number of data collection t sites required to detect specified level* of change for various hlOEs You can i~ change the following options to control the creation of the sampling guide it Hi . . ~ it .. s ., . ~ i} Cal current number of sites: Number of Required Data Collection Sites ~ MOE Percentage of Gross Weight `Violators Percentage of Single Axle Wt. Violators Percentage of Tandem Axle Wt. Violators Percentage of Bridge Formula Violators Average Pounds Over Gross Weight Limit Average Pounds Over Single Weight Limit Average Pounds Over Tandem Weight Average Pounds Over Bridge Weight Limit Average ESALs Average Excess ESALs Percent change to be detected _ 10 15 20 26 12 26 25 82 77 78 106 369 246 3 6 6 41 38 39 53 185 123 3 3 3 27 26 26 35 123 82 2 1 2 20 19 20 27 92 61 Figure 19. "Sampling Gliide" Dialog Box Calculated pavement E SAL capacity: Estimated pavement life BEFORE enforcement activity ~rears]: Estimated pavement life AFTER enforcement activity [years]: Percentage increase in pavement life due to enforcement activity: 4162490 21.s 23.4 8.81 2: Figure 20. The "Pavement Effects Analysis" Dialog Box 39