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APPENDIX B DEVELOPMENT, EVALUATION, AND RANKING OF MEASURES OF EFFECTIVENESS (M.O.E.s) Implicit in the development of M.O.E.s is the definition of venous terms and con- cepts related to truck weight enforcement. Consideration is first given to truck weight en forcement goals and procedures. The M.O.E. concept is then discussed as it relates to truck weight enforcement. Truck Weight Enforcement Goals and Procedures Truck weight is enforced for two reasons: (~) to avoid excess damage to the road way and structures caused by overweight loads, and (2) to assist the safe operation of trucks and other vehicles in the vicinity of trucks. Goals of state enforcement agencies that operate truck weight enforcement activities are the following: to deter truck operation in an overweight condition and/or operating with inappro- priate axle-spacing, to control pavement and bridge damage from overweight trucks, 3. to protect the public from safety risks associated with overweight Mucks, and 4. to protect law-abiding truck operators Mom illegal competition. Truck weight enforcement procedures involve activities and programs by which weights are monitored. An individual activity is designated by the specific type of hard- ware used, operating schedule, or other strategy affecting the driver's knowledge that his vehicle will be weighed when using the highway. Appendix B

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General classes of enforcement procedures are: I. Permanent roadside weigh scales (highly visible facility, hours of operation may be scheduled or random) 2. In-pavement weigh-in-motion (visible, but unobtrusive; Fill or part-time operation) 3. Bndge or culvert weigh-in-motion (invisible, full or part- tune operation) 4 Unscheduled roadside truck Inspections with portable scales (period of effectiveness is quite brief because of communications via the Ducking network) Relevant evidence prosecutions (effective all the time, affecting only part of the trucking fleet) An area-w~de weight enforcement program involves monitoring trucks using a network of activities such as those listed above. A program is more comprehensive and considers applied procedures, operating schedules, required enforcement staff, type of road where deployed, route arid diversion routes where enforcement is deployed, and other char actenstics. Truck Weigh! Measure of Effectiveness (M.O.E.s) The "measure of effectiveness" of a weight enforcement activity is defined as a "a determinable quantity, i.e., of what is achieved as the result of truck weight enforcement activity". Its application should also be applied to quantify the contribution that activity makes toward achievement of one or more of the goals defined above. In order to quantify effectiveness Mere must be measures which show benefits In terms of: (~) compliance op- erational weight and axle-spacing regulations, (2) pavement/bridge preservation, or (3) minimizing accidents, deaths, injuries, and property damage. Histoncally, measures of electiveness have used indices such as the number of Sucks weighed, number of overweight Sucks pulled off He road, size of He overloads de- tected, amount of fines imposed, number of prosecutions, trends with time, arid compliance ratios. In marry cases, these indices do not express the effectiveness in meariingfill terms Appendix B

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that relate to overall goals, e.g., the number of overweight trucks detected does not relate to preservation, punishment or prevention. Development of Candidate Measures The designation of candidate measures addressed one central question: what needs to be measured (and how) in order to reliably determine overweight violations? Task 2 activity first involved the identification of candidate M.O.E.s based on the Task 1 literature review and the collective expertise of the project team. Following the identification of candidate M.O.E.s, the measures were ranked on the basis of designated criteria These criteria were designated prior to the initiation of work in NCHRP Project 20- 34. The initial set of candidate measures was identified through independent contnbu- tions of NCHRP Project 20-34 team members. These individuals, and the basis for their M.O.E. development assessments, are as follows. Fred R. Hanscom, P.E. Pnocipal Investigator. The basis of Mr. Hanscom's M.O.E. development assessment consisted of: (~) his review of the literature in those areas identified in Task I; and (2) his 23 years of traffic operational research expenence, Including the conduct of numerous truck operational safety studies, in addition to WIM reliability determinations and WIM data collection activities. Thomas D. Gillespie, Ph.D. Director of The University of Michigan's Transporta- lion Research Institute. The basis of Dr. Gillespie's measures development assess- ment consisted of his 28 years of highway safety research experience which has emphasized heavy vehicle characteristics arid Heir effects on pavements. Dr. Gil- lespie is also published in the area of WIM reliability assessment. Benjamin H. CottreR TMG/~MS Truck Measures Consultant. The basis of Mr. Cottrell's measures development input is his ~ 5 years of experience in traffic engi- neenng research which includes specific research addressing He development of a truck weight sampling pearl using the Tragic Monitoring Guide. Appendix B

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The development of candidate measures first considered the primary truck weight objective, i.e., to deter Muck operation In an overweight condition. Second, the question of candidate M.O.E. development then addressed manpower and equipment resources avail- able to enforcement and highway agencies. Finally, a list of potential measures was based on current and foreseeable clata-gathering capabilities, given likely agency resources, and what measures are most efficacious given these resources. Canclidate M.O.E. Evaluation and Ranking Procedures The initial list of developed candidate M.O.E.s considered a variety of applications; i.e., effectiveness determinations for truck weight enforcement site-specific activities, corn- dor enforcement, and program application. The developed candidate M.O.E.s, described in this report section, are amenable to any of these operations. Their specific application with regard to site-specific versus program effectiveness determination, for example, wait be sub- sequently addressed in this NCHRP project's development of M.O.E. data sampling plans. The evaluation of derived candidate M.O.E.s was conducted via application of He following cnteria: A. Practicality of ~ O.K. application Of primary importance is state agency data col- lection ability, efficiency, cost requirements, and ease of measurement as applied to each candidate M.O.E. For example, high priority was given to M.O.E.s which can be readily derived Dom existing data sources, e.g., WIM devices, shipping records. B. Reliability of candidate M.O.E. Reliability refers to measurement precision, e.g., confidence Hat repeated measurement will yield consistent results. A reliable M.O.E. is one which correctly represents the true distribution of weights, classifi- cation, percentage of overweight trucks, percent of bridge formula non-compliance, etc. within the study region. This concept is of paramount importance in assessing the performance of technologies applied In buck weight measurement and classifi- cation activity. Appendix B

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C. Support statewide random sampling. Traffic monitoring in the vicinity of weigh stations (including alternate truck routes) presents a limited perspective of over- weight hauling practices. Therefore, monitoring procedures, designed to achieve statewide random weight sampling and consistency with Safety and Pavement Management System technologies, was designated to gamer M.O.E. data. It was therefore necessary Mat designated M.O.E.s be comprised of variables which can be denved from these systems. D. Absence of bias with regard to enforcement/monitoring procedure. It is imperative that the applied M.O.E. data-gathertng procedure not be biased with regard to either a weight enforcement program or a particular traffic monitoring method. Applied M.O.E.s must be generally sensitive to prevailing truck characteristics regardless of enforcement activity. Furthermore, care must be taken to ensure that overweight truck presence is not influenced by specific traffic-monitor~ng or weight- enforcement instigations. Therefore, M.O.E. selection criteria considered the sus- ceptibility of candidate M.O.E.s to potential bias. E. MO.E. compatibility with state agency data collection methods. The designated procedure for states' measurement of enforcement effectiveness must be achieved within Me state's data collection capabilities. Therefore, emphasis was placed on emerging technologies, i.e., Safety and Pavement Management Systems, In order that Me developed M.O.E. assessment procedure have fixture applicability. SHRP WIM Installations were considered a primary data source, therefore vanables col- lected by this system were given high priority. F. Sensitivity to infrastructure damage One objective of truck weight enforcement is to control pavement and bridge damage from overweight trucks. Certain truck loading conditions, e.g., excessive axle-weight as opposed to excessive tandem- weight are more likely to result in pavement damage. This consideration is ~mpor- tant with regard to assessing Me meets of candidate M.O.E.s. Applicability to fixture technology The use of Pavement Management Systems, Bridge Management Systems, Maintenance Management Systems, and Safety Man- agement Systems present an emerging technology in many states. An objective of is Appendix B

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NCHRP Project 20-34 is to enable highway agencies to efficiently assess the effec- tiveness of truck weight enforcement programs. Therefore, designated M.O.E.s in- cluded those measures which can be determined via use of these systems. Each candidate M.O.E. was evaluated on the basis of each of the above cnter~a. In order to rank candidate M.O.E.s, a numerical rating scheme was applied in the evaluation process. As each criterion was applied to each candidate M.O.E., the suitability of the M.O.E. was assessed on the basis of each criterion using the numencal scale indicated in Table ~ below. Table ~ . Applied Numerical Rating Scheme to Evaluate Candidate M.O.E.s o 3 5 N~n:erical-~Soore ~ ~ Assessment Ciitenon~ ~. . No value whatever Insignificant Worm Some Utility Moderately Useful Significantly Valuable Superior Ment Using the above scale, the average rating across He six criteria was assigned to each M.O.E. to determine the final ranking. Candidate M.O.E.s The development of this M.O.E. was guided by two pnnciples. First, derived meas- ures should be consistent with capabilities of potential data sources, e.g., commercially available WIM equipment and SHRP LTPP data output. Second, derived measures should be functionally capable of apprising enforcement agencies of target truck characteristics. Due to the fact that existing data sources are not fully compatible with M.O.E. sensitivity requirements, two classes of traffic operational measures are listed. Direct M.O.E.s are those to be evaluated; however, due to the fact that highway/enforcement agencies are cur- rently dependent on data collection procedures which do not directly generate these precise Appendix B

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measures, a list of indirect measures, i.e., those required to derive the direct measures set, was also generated. Indirect measures - These measures consist of data output gathered by WIM systems. These data must be recorded in ASCII or similar computer readable files for subsequent manipulation into direct Traffic Operational M.O.E.s listed below. 1. Total truck sample data, generated by WIM system, e.g., a. Total buck volume. b. Grouped by weight-regulation and axle-configuration classification. c. Grouped by specified time interval. 2. Vehicle-specific data, generated by WIM system, i.e., c. d. e. a. Total Duck weight, in pounds and ESALs b. Individual axle weights, in pounds and ESALs Axle-grouping configuration, i.e., tandem, tridem Axle-grouping weight, in pounds and ESALs Spacing between axles/axle-groupings. Direct M.O.E.s - These measures are derived from WIM system output. Commercially available equipment Dom manufactures such as Golden River, Intentional Road Dynam ice, PAT, Inc. directly supports most of the developed M.O.E.s. All of the following seven candidate M.O.E.s can be denved Mom software programming of output Dom this equ~p ment. Proportion of Overweigh! Trucks in Sample - The fraction (or percentage) of the truck sample exceeding the applicable weight limit based on any of the parameters listed below, based on a statistically valid sample size. a. b. c. d. Gross Vehicle Weight Individual Axle Weight Individual Axle-grouping Weight Truck Typef FHWA 13-classification scheme) 7 Appendix B

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The M.O.E. significance of each of the above parameters is as follows. The impact of trucks on pavement detenoration vanes according as to how the stress is applied. There- fore, gross truck weights, as well as weights exerted by individual axles and axle-groupings, are important. Furthermore, whether a particular classification of truck is more (or less) prone to overweight violations may be of assistance to enforcement agencies due to visual characteristics associated with specific truck types. This M.O.E. was evaluated in terms of receiving a racking (using the scheme given in Table I) for each of Me previously discussed M.O.E. evaluation criteria. Results of the ranking procedure are discussed for each criterion as follows. A. Practicality of Al O.K. Application Ranking: 5 Superior. Commercially available WIM equipment generates data for easy computation of this measure. B. Reliability of Candidate M.O.E Ranking: 4 Significantly valuable. Commercially available equipment is be- coming increasingly reliable. C. Supports Statewide Random Sampling Ricing: 4 Significantly valuable. Commercially available WIM equipment is commonly applied in statewide sampling procedures. D. Absence of Bias with Regarc! to Enforcement/Monitoring Procedures Ranking: 3 Used. Subject to We same bias as any weighing operation. E. MO.E. Compatibility with State Agency Data Collection Methods Ranking: 4 Significantly valuable. This measure is compatible with emerging technology. F. Sensitivity to Infrastructure Damage Ranking: 2 Some Utility. This measure may be associated win pave- ment/br~dge damage. Appendix B

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G. Applicability to Future Technology Ranking: 5 Superior. This measure is highly amenable to emerging technology. 2. Severity of Overweight Violation - The extent to which collected data on any of the parameters listed below exceeds the allowable legal weight limit, expressed as a percentage exceeding the allowable legal weight, grouped by range to indicate 5, 1 0, 20, 30' and 40+ percent overweight). a. Gross Vehicle Weight b. Individual Axle Weight c. Individual Axle-grouping Weight d. Truck Typef FHWA 3-classification scheme) This M.O.E. was evaluated In terms of receiving a ranking (using the scheme given in Table 1) for each of Me previously discussed M.O.E. evaluation criteria. Results of the ranking procedure are discussed for each criterion as follows. A. Practicality of ~ O.K. Application Ranking: 5 Supenor. Commercially available WIM equipment generates data for easy computation of this measure. B. Reliability of Candidate MO.E R=king: 4 Useful. Commercial equipment is becoming increasingly reliable, yet this M.O.E. demands precision. C. Supports Statewide Ranclom Sampling Ranlcing: 4 Significantly valuable; Commercial WIM equipment applied in statewide sampling procedure, yet this M.O.E. demands precision. D. Absence of Bias with Regard to Enforcement/Monitoring Procedures Ranking: 3 Usefill. Subject to the same bias as any weighing operation. 9 Appendix B

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E. ~ O. E. Compatibility with State Agency Data Collection Methods Ranking: 4 Significantly valuable. This measure is compatible with emerging technology. F. Sensitivity to Infrastructure Damage Ranking: 5 Superior. Severity of overweight violations is highly sensitive to pavement/bridge damage. G. Applicability to Future Technology Ranking: 5 Superior. This measure is highly amenable to emerging technology. 3. Distribution of Overweigh' Trucks in Sample - While the two M.O.E.s noted above are essential to describe the overweight truck problem to enforcement and highway agencies, simple distributions, e.g., the numbers of overweight trucks and associated excess loading over legal limits, are necessary for dispatching enforce ment personnel to locations in which enforcement operations can be conducted in the most cost-e~ective manner. This M.O.E. was evaluated In terms of receiving a ranking fusing the scheme given in Table 1 ) for each of the previously discussed M.O.E. evaluation criteria. Results of the ranking procedure are discussed for each criterion as follows. A. Practicality of M O.K. Application Ranking: 5 Superior. Commercially available WIM equipment generates data for easy computation of this measure. B. Reliability of Candidate MO.E Rarefying: 4 Significantly valuable. Commercially available equipment is be- coming increasingly reliable. Appendix B

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C. Supports Statewide Random Sampling Ranking: 5 Superior. Commercially available WIM equipment is commonly applied in statewide sampling procedures. D. Absence of Bias with Regard to Enforcement/Monitoring Procedures Ranking: 3 Useful. Subject to the same bias as any weighing operation. E. MO.E. Compatibility with State Agency Data Collection Methods Ranking: 4 Significantly valuable. This measure is compatible with emerging technology. F. Sensitivity to Infrastructure Damage Ranking: 2 Some Utility. The proportion of overweight trucks is marginally sensitive to pavement/br~dge damage. G. Applicability to Future Technology Ranking: 5 Supenor. This measure is highly amenable to emerging technology. Bridge Formula Violations - Axie-spacing information, in combination with indi vidual-axle and axle-group~ng weights, applied to spacing criteria specified by the applicable Bridge Formula This M.O.E. was evaluated in terms of receiving a ranking (using Me scheme given in Table 1) for each of the previously discussed M.O.E. evaluation criteria. Results of the ranking procedure are discussed for each criterion as follows. A. Practicality of Al O.K. Application Ranking: 4 Significantly valuable. Much commercially available WIM equip- ment generates data for easy computation of this measure. B. Reliability of Candidate Al O.E Ranking: 4 Significantly valuable. Commercially available equipment is be- coming increasingly reliable for axle-spacing data. 11 Appendix B

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C. Supports Statewide Random Sampling Ranking: 4 Significantly valuable. Commercial WIM equipment applied in statewide sampling procedure, yet this M.O.E. demands mea- surement specificity. D. Absence of Bias with Regard to Enforcement/Monitoring Procedures Ranking: 3 Usefi~. Subject to the same bias as any weighing operation. E. M:O.E. Compatibility with State Agency Data Collection Methods Rarefying: 4 Significantly valuable. This measure is compatible with emerging technology. F. Sensitivity to Infrastructure Damage , .' ~ . Ranking: 5 Superior. By definition, the Bridge Formula is sensitive to pave ment/br~dge damage. G. Applicability to Future Technology Running: 5 Superior. This measure is highly amenable to emerging technology. Equivaier't Single Axle Load RESAY) - This measure is defined as a unit of meas- Clement equating the amount of pavement consumption caused by an axle or group Of axles, based on Me loaded weight of Me axle group, to the consumption of a s~n- gle axle weighing IS,000 pounds. This M.O.E. provides a direct measure of pave- ment wear exhibited by a single truck. The usefulness of this measure derives from Me fact Mat pavement design life is determined in terms of ESALs. This M.O.E. was evaluated in terms of receiving a ranking (using the scheme given In Table I) for each of the previously discussed M.O.E. evaluation criteria. Results of the ranking procedure are discussed for each criterion as follows. Appendix B

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~4. Practicality of Al O.K. Application Ranking: 5 Superior. Commercially available HIM equipment generates data for easy computation of this measure. B. Reliability of Candidate Ad O.E Ranking: 4 Significantly valuable. Commercially available equipment is be- coming increasingly reliable. C. Supports Statewide Random Sampling Ranking: 5 Significantly valuable. Commercially available WIM equipment is commonly applied in statewide sampling procedures. D. Absence of Bias with Regard to Enforcement/Monitoring Procedures Ranking: 3 Useful. Subject to the same bias as any weighing operation. E. MO.E. Compatibility with State Agency Data Collection Methods Ranking: 4 Significantly valuable. This measure is compatible with emerging technology. F. Sensitivity to Infrastructure Damage Rallying: 5 This measure is highly sensitive to pavement/bndge damage. G. Applicability to Future Technology Ranking: 5 Superior. This measure is highly amenable to emerging technology. 6. Excess ESAls The definition of excess ESALs as determined by the Wisconsin study (Stein, ~ 988) is "excess ESALs equal the sum of the total ESALs attributable to the illegal portion of the individual single or tandem axle group." The signifi cance of application of this M.O.E. is that forty percent of observed ESALs on Wis cons~n's Rural Interstate System were attributable to excess ESALs. This M.O.E. is to be gathered by vehicle class. 13 Appendix B

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This M.O.E. was evaluated in teens of receiving a ranking (using the scheme given In Table 1) for each of the previously discussed M.O.E. evaluation criteria. Results of Me ranking procedure are discussed for each criterion as follows. A. Practicality of M. O.K. Application Ranking: 5 Superior. Commercially available WIM equipment generates data for easy computation of this measure. B. Reliability of Candidate MO.E Ranking: 4 Useful. Commercial equipment is becoming increasingly reliable, yet this M.O.E. Remarks precision. C. Supports Statewide Random Sampling Ranking: 4 Sigriificarltly valuable. Commercial WIM equipment applied in statewide sampling procedure, yet this M.O.E. demands precision. D. Absence of Bias with Regard to Enforcement/Monitoring Procedures Ranking: 3 Useful. Subject to We same bias as any weighing operation. E. M O.K. Compatibility with State Agency Data Collection Methods Ranking: 4 Significarltly valuable. This measure is compatible with emerging technology. F. Sensitivity to Infrastructure Damage Ring: 5 This measure is highly sensitive to pavement/bndge darnage. G. Applicability to Future Technology Ranking: 5 Superior. This measure is highly amenable to emerging technology. 7. Projected Distance Traveled by Overweigh! Truck - Total pavement wear is obvi ously more severe for overweight trucks traveling longer distances. Application of WIM surveillance devices on corridors of known truck travel patterns will enable Appendix B

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enforcement agencies to prioritize enforcement operation in a manner to minimize regional pavement wear. This M.O.E. was evaluated in teens of receiving a ranking (using the scheme given in Table ~ ~ for each of the previously discussed M.O.E. evaluation cr~tena. Results of the ranking procedure are discussed for each cr~tenon as follows. A. Practicality ofM:O.E. Application Ranking: 3 Useful. Commercially available WIM equipment generates data users! for computation, however manually generated travel distance factor must be applied. B. Reliability of Candidate MO.E Ranking: 2 Some Utility. Application of travel distance factor comprises a reli- ability threat, e.g. not possible to determine Gavel distar~ce for entire truck sample; estimated from planning data. C. Supports State widle Random Sampling Ranking: 2 Some Utility. Estimation requirement presents problem. D. Absence of Bias with Regard to Enforcement/Monitoring Procedures Ranking: 2 Some Utility. Subject to more bias than other weighing operations. . E. MO.E. Compatibility with State Agency Data Collection Methods Ranking: 2 Some Utility. This measure requires integration of WIM technology and have] estimation techniques, a process which produces a barrier to its application. F. Sensitivity to Infrastructure Damage Ranking: 2 Some Utility. Distance Raveled has secondary impact on pave- ment/bridge damage. , .. . . 15 Appendix B

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G. Applicability to Future Technology Ranking: 2 Some Utility; Applicable emerging technology, e.g., AV1, is slow to materialize and may induce bias with regard to this measure. 8. Distribution of Above Measures by Day~f-Week, Hour-of-Day The Issue of whether to collect temporal distributions of the above M.O.E.s was based on the ability to assist enforcement agencies with manpower-allocation decisions to facili tate We optimization of resources. This M.O.E. collection strategy was evaluated in terms of receiving a ranking (using Me scheme given in Table 1) for each of the previously discussed M.O.E. evaluation crite- ria. Results of the ranking procedure are discussed for each criterion as follows. A. Practicality of M. O.K. Application Ranking: 4 Significantly valuable. Most applicable data collection methods, e.g., automated devices, rely on temporal observations. B. Reliability of Candidate M.O.E Ranking: 4 Significantly valuable. Commercially available equipment has proven reliable in terms of temporally recording data. C. Supports Sfatewicle Random Sampling Ranlcing: 4 Significantly valuable. Random sampling Uris commercially avail- able WIM equipment is readily applied in statewide sampling proce- dures. D. Absence of Bias with Regard to Enforcement/Monitoring Procedures Ranking: 3 Useful. Temporal observation is not expected to complicate any bias problem. E. MO.E. Compatibility with State Agency Data Collection Methods Ranking: 5 Superior; Commercially available WIM equipment readily generates data by day-of-week arid hour-of-day. Appendix B

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F. Sensitivity to Infrastructure Damage Ranking: 2 Some Utility. Pavement/bridge damage is marginally affected by temporally-related usage. G. Applicability to Future Technology Ranking: 5 Superior. This measurement strategy is highly amenable to emerg ing technology. Ranking of Candidate M.O.E.s The six M.O.E. evaluation criteria were defined earlier in this discussion. Applied criteria were as follows. Practicality of Application B. Measurement Reliability Supports Statewide Random Sampling D. Absence of Enforcement-induced Bias E. Data Collection Melons Capability F. Sensitivity to Infrastructure Damage G. Applicability to Future Technology Ranking of M.O.E.s was achieved by assessing We applicability of each M.O.E. to truck weight enforcement procedures. This procedure was achieved by assigning a point value to each M.O.E. based on the following critenon. No points ~ point 2 points 3 points 4 points 5 points No value whatever Insignificant worth Some utility Moderately useful Significantly valuable Superior merit discussed in Chapter 3. I? Appendix B . .

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The average raking across the six criteria was assigned to each M.O.E. to detenn~ne the can- didate M.O.E.'s final ranking. Numerical results of the applied rating procedure and M.O.E. rankings in each category are shown in Table 2 on the next page. The list of measures shown in the exhibit generally comprised proposed M.O.E.s for field validation. The single exception was ~at, due to practical considerations that precluded its current application, the "Distance Traveled" measure was not evaluated in the current project. However, this measure is retained for subsequent consideration where applicable, i.e., AV} monitoring procedures. Final M.O.E. Definition In accordance with project objectives and in compliance with He NCHRP Project 20-34 Problem Statement, a final M.O.E. list was developed. This final list consisted of M.O.E.s that were suitable for field evaluation. The list of final M.O.E.s, along with their definitions appears as Table 3 on page 20. REFERENCE Stein, P. et al, The Overweight Truck in Wisconsin: Its Impact on Highway Design, Maintenance, Enforcement, and Planning, Wisconsin Department of Transportation Lansing, Wl, October, 1988 Appendix B

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is - a: ct c, ~ - I:FE=E KR~. Bm us i' u, ~ ~ ~ - ~ co - - - .-i' ~ - - - _ N ~ N _: N -- N An Or co Tic E He to 0 e i ? ~ s3unsv3w 31VOlONVO Table 2. Ranking of Candidate M.O.E.s 19 Appendix B

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Table 3. Designated Measures of Electiveness (M.O.E.s) and their Definitions ~: ~-~ ~ E ~ ~ ~ ~ ~ ~ ~ -aim-- ~ ~ i- -if- ~ ~ ~ - ~ - ~ ~ I- ~ ~-~ D no - i- ~ - ~ ~ ~ ~--~ -- - ~ ~ - ~ ; ~ .. ~ . ~ . ..,~ ;;,;. Aft- it; - ~ ~ ~ ~ -- ~: ~ ~ ~: ~ ~ ~ ~ The fraction (or percentage) of the total ob Gross Weight Violation, Proportion served truck sample which exceeds the le gal gross weight limit. The extent to which average measured Gross Weight Violation, Severity gross weights for the observed sub-sample of gross weight violators exceeds the legal gross weight limit. The fraction (or percentage) of the total ob Single-axle Weight Violation, Proportion | served truck sample with one or more axles which exceeds the legal single-axle weight limit. The extent to which average measured sin Single-axIe Weight Violation, Severity ale-axle weights for the observed sub sample of single-axIe weight violators ex _ceeds the applicable legal limit. The fraction (or percentage) of the total ob Tandem-axle Weight Violation, Proportion | served Duck sample with one or more tan dems which exceeds the legal tandem-axle weight limit. . The extent to which average measured tan Tandem-axIe Weight Violation, Severity dem-axIe weights for the observed sub sample of tandem-axle weight violators exceeds the applicable legal limit The fraction (or percentage) of the total ob Bndge Formula Violation, Proportion served truck sample which exceeds the le gal Bridge Formula weight. _ _ The extent to which average measured Bndge Formula Violation, Severity Bridge Formula weights for the observed sub-sample of Bridge Formula violators exceeds the legal weight. The fraction (or percentage) of the total ob ExcessESALs, Proportion served truck sample exhibiting Excess ESALs; i.e., ESALs attributable to the ille gal portion the individual single or tandem axle group. The average value of Excess ESALs ob Excess ESALs' Severity served for the truck sub-sample exhibiting Excess ESALs. Appendix B 20