An Analytical Hierarchy Process to Improve Project Prioritization in the Austin District

Transcription

1 An Analytical Hierarchy Process to Improve Project Prioritization in the Austin District Juan Diego Porras-Alvarado, MS. Graduate Research Assistant Michael R. Murphy, Ph.D., P.E Research Engineer Hui Wu, Ph.D., P.E. Research Associate Zhe Han, MS Graduate Research Assistant Zhanmin Zhang, Ph.D. Associate Professor and Fellow of the Clyde E. Lee Endowed Professorship in Transportation Engineering Mike Arellano, P.E. Pavement Preservation Engineer The University of Texas at Austin Department of Civil, Architectural & Environmental Engineering (CAEE) Austin, TX Center for Transportation Research Austin, TX 0 Texas Department of Transportation Austin District Austin, TX 0 Word Count: Text Figures x 0 0 Tables x 0 00 Total Submitted for Publication in the 0 Transportation Research Record and Presentation at the th Annual Meeting of the Transportation Research Board, Washington D.C., January -, 0 August, 0

2 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano 0 ABSTRACT A project candidate selection and ranking model (PCSRM) is commonly applied in pavement maintenance and rehabilitation (M&R) planning. Typically, these models are developed using empirical mathematical expressions that do not have a clear physical meaning and cannot accurate effectively assess the priority of transportation agencies or pavement engineers. In an attempt to overcome this limitation, this study develops a methodology based on the analytical hierarchy process (AHP) for identifying pavement-related factors that can be used to improve the accuracy of the PCSRMs currently used by the Austin District -year plan. The aim is to identify pavementrelated factors that can reflect the engineering judgment of transportation agencies and pavement engineers. The proposed methodology consists of three major components: AHP design, AHP survey and results analysis, and factors selection. A three-tier hierarchy structure consisting of one objective, five goals and alternatives was defined. Additionally, a system to check for consistency following literature recommendations was defined. An Excel-based survey tool was developed with the objective of providing a relatively easy visual interface for the participants. The design and development of the survey tool was carried out with the direction of the District Pavement Preservation Engineer. A total of TxDOT pavement experts completed the AHP survey with the required consistency ratio. Finally, clustering analysis was performed to identify the pavement-related factors that show higher priority. The identified factors with the highest overall weights should be considered in future PCSRMs improvement. Keywords: Project candidate selection, project ranking model, pavement M&R, analytic hierarchy process, AHP, -year plan, AHP survey

3 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano INTRODUCTION The Texas Transportation Commission (TTC) set a goal to preserve the asset value of all pavements by maintaining a good or better pavement condition score on 0 percent of Texas roads. A study conducted to evaluate Texas pavement maintenance needs by the year 00 estimated that achieving and maintaining this goal would require about $. billion per year on average (). Based on the funding projection conducted by TxDOT, available funds are clearly insufficient to achieve and maintain the 0 percent good or better pavement condition goal in the next 0 years. More precisely, with the current funding allocations and projections, the good or better pavement score will drop below 0 percent by Fiscal Year (FY) 0; by FY 0, the score will drop below 0 percent (). The negative projection has encouraged TxDOT to pursue sustainable pavement management (PM) programs. For this reason, TxDOT has created the -year Pavement Management Plan Committee (PMPC) to develop maintenance and rehabilitation (M&R) plans for the State of Texas. As part of these plans, the district pavement engineers are requested to submit the list of pavement projects for a -year period. Then, by combining the twenty-five individual district plans together, the statewide -Year Pavement Management Plan is created and reviewed by the all levels within TxDOT (). In Texas, districts are responsible for selecting candidate projects, project-scheduling, and budget estimates. Usually, this starts with a networklevel screening to select candidate projects by district pavement engineers (DPE) using Pavement Management Information System (PMIS) data. Then, DPE reviews the candidate list with area engineers (AEs) to discuss potential projects in their counties. Additional projects may be added or adjustment may be made during this process due to local considerations. TxDOT has no recommended standard procedure for selecting and ranking candidate projects. As a result, the current project selection practices are mostly based on empirical methodologies, which are based on district pavement engineers experience and local conditions. To overcome this limitations, Chi et al. (0) has developed a Project Candidate Selection and Ranking Model (PCSRM) which is currently used in the Austin district to aid their networkscreening process and preparation of the -year plan (). The model considered four main factors: condition score, change in condition score since last year, distress score, and change in distress score since last year. These factors are extracted or computed using PMIS data from the current and the previous fiscal year (). This information is used along with an algorithm to optimize the length of a candidate project to provide the district with a candidate project list with ranking. Then, the DPE of the Austin district uses this list compared with the existing -Year Plan to made adjustment for overlapping projects and remove any candidate projects that are already in the plans. Meeting with AEs allows for the consideration of other factors (i.e., traffic level, distress types, local consideration) in the process. The model has been shown to correctly identify about % of candidate projects that eventually are included in the -year Plan (, ). The PMIS is one of the largest pavement databases in the U.S., containing relevant pavement information for more than 00,000 road sections of roughly 0. mile in length (). TxDOT updates this database every year with new pavement conditions and other inventory data by conducting an annual PMIS data collection survey at the beginning of each fiscal year from September to December. The pavement information stored in the system typically includes road type, location characteristics, and other indices such as pavement condition score, distress score, and ride score (, ). These data are generally used by DPEs to select pavement projects.

4 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano Moreover, Kim et al. (0) developed a logistic regression model to select and prioritize network-level projects for the Austin District. This logistic regression model was developed based on common rules of district engineers knowledge and experience (). The model s objective was to create a knowledge-based management system to aid engineers in automating their usual procedure for selecting and ranking projects. A knowledge management (KM) is the process of capturing, developing, sharing, and effectively using organizational knowledge ( ). Transportation agencies typically prioritize their M&R plans based on a priority index computed using empirical mathematical expressions (). Although these mathematical expressions are convenient to apply, their physical meaning usually does not accurately and effectively convey the priority assessment of pavement engineers. Moreover, some of the factors considered in the selection and ranking of M&R projects are qualitative, which adds an additional dimension to the problem. The PCSRM developed by Chi et al. (0) exhibits some of the limitations previously discussed. For this reason, this study explores the use of the analytical hierarchy process (AHP) to identify pavement-related factors that can be used to improve the accuracy of the PCSRM protocol currently used. The aim is to identify factors, quantitative and qualitative, used by pavement engineers and expert to selecting and ranking projects through the AHP framework. In other words, this study will capture subjective judgment from various pavement experts towards improving currently used models by the Austin District. It is important to understand that the candidate project selection process does not pick projects and identify the treatments automatically; rather, the process significantly reduces the amount of time needed to process PMIS data on the over,00 lane miles of pavement in the Austin District. THE CONCEPT OF AHP AHP is a mathematical technique for multi criteria decision making (MCDM) developed by Saaty in the 0s to facilitate the selection of the best alternative by decision makers (, ). The AHP provides a framework to decomposing and structuring complex problems where decisions makers are faced with challenging MCDM. By decomposing and structuring these types of problems, the decision makers often gain a better understanding of the problem and the relationships of the individual criteria or attributes ( ). Moreover, the AHP has been used to compare alternatives on a ratio scale and permits qualitative data, in addition to quantitative data, to be included (,, ). The AHP provides a framework for solving complex and unstructured problems through a systematic logical analysis. This analysis involves the following four principles: a) structuring of a hierarchy, b) prioritizing through pair-wise comparisons, c) synthesizing of pair-wise comparison to obtain a priority vector, and d) checking for consistency of the preference judgements. The AHP first principle is the structuring of a hierarchy. A hierarchy decomposes a problem into individual independent elements and is a fundamental concept of the AHP (, ). According to Saaty, a hierarchy is an abstraction of the structure of a system to study the functional interactions of its components and their impacts on the entire system (). Structuring a hierarchy requires a unique process that involves the following steps: a) the decision makers must identify the elements of the problem, b) the decision maker must group these elements into homogeneous sets, and c) these homogeneous sets must be arranged into different levels or tiers (, ). The next phase is to conduct the pairwise comparisons of the criteria. AHP, as recommended by Saaty, uses a nine-point scale to determine the comparative difference in a

5 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano 0 0 pairwise comparison of two elements (). The preference judgment is made by assigning a value of to the elements if they are of equal importance, to a weakly more important element, to a strongly more important element, to a very strongly important element, and to an absolutely more important element. Moreover, the intermediate values,,, and can be used to express judgment not following the previous preferences. The outcome of each set of pairwise comparisons is expressed as a positive reciprocal matrix and represents the intensities of the expert s preference between individual pair-wise comparisons (,, ). A = (a ij ) () where, a ii = a ij = a ji for all i, j n Therefore, the positive reciprocal matrix can be expressed as follows: a a n A = [a ij a ij ] = ( a a n ) () a n a n where, n a ij a ji = number of alternatives being compared within one set of pair-wise comparison = preference of alternative i over alternative j = preference of alternative j over alternative i The next step is to synthesis the preferences assigned to each pair-wise comparison in matrix A into a priority vector, w, that contains the priority of each element. Typically, the AHP uses a principal Saaty s Eigenvalue Method to derive priority vectors ( ); however, several other methods such as the geometric mean or Logarithmic Least Squares Method (LLSM) and the Least Squares Method (LSM) have been proposed (, ). Various studies have proven that when the positive reciprocal matrix is consistent, all these methods lead to the same outcome. However, in real life, judgements are frequently inconsistent, and these methods could generate different priority vectors (, ). Saaty s Eigenvalue Method is often used to derive the priorities of the alternatives by calculating the principal eigenvector, w. This vector corresponds to the largest eigenvalue, λ max of matrix A, as follows: Aw = λ max w () where, λ max n w = principal eigenvector

6 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano 0 0 The priority vector is the normalized principal eigenvector of the pairwise comparison matrix. It is established for each criterion and sub-criterion, as well as the alternatives under each sub-criterion. The overall priority weight of alternatives is computed as follows (, 0): V i = W j X ij where, j V i W j X ij = overall priority weight of alternative i = weight assigned to criterion j = weight of alternative i given criterion j Equation means that w is an eigenvector of A with eigenvalue n if the judgments are consistent, though that might not always be the case. If the judgments are consistent, the following relationship holds: a ij a jk = a ik () The AHP has a built-in mechanism to detect and avoid inconsistent comparisons. To check for consistency in the decision maker s judgment, Saaty defined the consistency ratio (CR), which is the comparison of the consistency index (CI) and the random consistency index (RI). The CI is based on the λ max, and as previously mentioned, the λ max is always greater than or equal to n for a positive and reciprocal matrices. For a matrix with consistent judgments, λ max equals n. Moreover, Saaty and various researchers have generated various random matrices for each n size matrix and have calculated their mean CI values. This mean CI value is known as the random index (RI). A matrix is considered consistent only if CR is 0.. The CR is expressed as follows (): CR = CI RI where CI is given by, CI = λ max n n where, n = size of the matrix The problem of checking for consistency has been greatly discussed, especially the relation between the consistency and the scale used to represent the decision maker's judgements. Various studies reported limitations of the CR developed by Saaty. Some authors claim that Saaty s -point scale is very restrictive, while other studies have shown that the -point scale proposed gives results which are outside the accepted consistency when n increases (). For this reason, Alonso and Lamata (00) have proposed a new consistency measure based on statistical analysis (). This consistency measure is shown as follows: () () ()

7 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano 0 λ max n + α (.n +.) () where, n = size of the matrix α = level of consistency required Using the Alonso and Lamata (00) approach, a matrix of preference is said to be consistent if the matrix satisfies Equation criterion. In this system, the simplicity of the criteria and its adaptability is guaranteed compared to the Saaty s consistency ratio (). METHODOLOGY The main objective of this study is to explore the use of the analytical hierarchy process (AHP) to identify pavement-related factors that can be used to improve the accuracy of the PCSRM protocol currently used by the Austin District -year Plan. The methodology presented in Figure provides an overview of the major components and the relationships among them to achieve the objective of the study. The proposed methodology consists of three major components: AHP design, AHP survey, and factors selection. The first component refers to the AHP framework that was developed for this study. As previously mentioned, complex problems can be decomposed using hierarchy structures. The hierarchy structure was comprised of objectives, goals, and alternatives, which were carefully defined by the authors together with the DPE. Then, the structure of the pair-wise comparison was also defined. Lastly, the consistency measure protocol was selected. After the AHP was defined, an Excel-based survey tool was developed. The survey tool provides a relatively easy interface for participants to complete the survey, and adequate procedure to summarize the results. Additionally, to aid users in achieving consistency faster, a consistency measure protocol was built into the survey. Parallel to the development of the survey tool, pavement experts within TxDOT were selected to participate in this study. Additionally, workshops were conducted to help the participants to understand the ultimate goal of the study. Finally, a system to select the pavement-related factors was developed.

8 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano Enhancing Candidate Project Ranking Model -Year Pavement Management Plan Design of the Survey Define Hierarchy Structure Pair-Wise Comparison Consistency Measure Protocol Survey & Results Analysis Develop Excel-Based Survey Tool Conduct Workshops Analysis of Results Select Pavement Experts Select Factors with the Highest Importance 0 FIGURE Methodology of the Study DESIGN OF THE AHP SURVEY Problem Formulation In this study, one objective, five goals, and pavement-related factors are considered. The goals and pavement-related factors were defined together with the Austin DPE. The five goals and their corresponding definitions are presented as follows: Goal - Enhance safety (pavement-related): To restore and preserve pavement surface properties to enhance operator safety and to reduce accident rates on an annual basis. Goal - Preserve and increase short-term pavement condition: To restore and preserve pavement condition to distressed pavements within a -year pavement management plan. Goal - Preserve and increase long-term pavement condition: To restore and preserve pavement condition to distressed pavements within a -year pavement management plan. Goal - Enhance system structural capacity: To rehabilitate and increase the overall structural capacity or to extend the remaining service life of the pavement system to support. Goal - Enhance user s satisfaction: To restore and preserve pavement characteristics, such as ride quality & pavement noise, to enhance operational comfort and serviceability.

9 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano The pavement-related factors that are considered in the AHP are shown in Table. The currently used CPSRT already considers some of these pavement-related factors. It is important to mention that ranking algorithm is based on empirical basis. Therefore, the overall weight obtained using the AHP will be used to develop an improved ranking model based on more tangible results. TABLE Pavement-related factors considered in the AHP Factors Abbreviation Failure Failure Rutting Rutting Fatigue Cracking Fat_Crack Longitudinal Cracking Longi_Crack Truck ADT Truck_ADT Total ADT Total_ADT Pavement-related % Remaining Structural Life Rema_Struc_Life Factors Routine Maintenance Expenditure Routine_ME Ride Score Ride_ Score Change in Ride Score Since Last Year Chan_Ride_Sco Preventative M&R Expenditures M&R_Expen Change in Distress Score Since Last Year Chan_Dist_Sco Years Since Last Resurfacing Y_sl_Resur Years Since Last Structural Rehabilitation Y_sl_Rehab Next sections use the abbreviations instead of the factors complete designation Figure shows the three-tier hierarchy structure used for the AHP analysis in this study. The overall objective is to enhance the ranking module of the CPSRT, and is placed at the top level of the hierarchy. The five goals previously mentioned are placed at the second level of the hierarchy, while the bottom level contains the pavement-related factors that are compared to each of the five goals. Level (Objective) Enhancing Candidate Project Ranking Model Level (Goals) Goal Goal Goal Goal Goal 0 Level (Factors) Factor Factor Factor Factor FIGURE Hierarchy Structure for the AHP analysis The Saaty s -point scale was identified as the most appropriate scale to rank the relative importance of factors that could contribute to selecting and ranking a pavement project for treatment. As discussed previously, the scale ranges from one to nine, and one refers to factors with equal importance, while nine refers to the extreme importance of one factor over the other.

10 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano 0 Additionally, the consistency ratio and the Alonso and Lamata (00) consistency approach were used to check for consistency (). Excel-based Survey Tool The Excel-based survey tool was developed with the objective of providing a relatively easy visual interface to the participants. The design and development of the survey tool was carried with direction of the DPE. The survey tool provides tabs for evaluating all five goals, as well as the pavement-related factors with respect to each goal. The survey tool records a survey taker s preference and strength of preference for each pair-wise comparison and provides guidance for improving consistency. The tool also calculates the weight and rank of each criterion in relation to each Goal, and the weight and rank of each criterion based on an overall average weight. SURVEY RESULT ANALYSIS Workshops were conducted to introduce the importance of this study to maintenance and rehabilitation (M&R) practices within TxDOT. The main objective was to explain the AHP analysis to the various participants, which was thought to improve the quality of the results. Each workshop was divided into three sections: an introduction of the AHP analysis, a description of the Excel-based survey tool, and the completion of the survey by the participants. In the introduction of the AHP section, the discussion covered information related to the study objective, the background of the AHP analysis, and AHP examples. The second section was used to explain the use of the Excel-based survey tool by describing its objectives and features. Then, the participants were asked to complete the survey. A total of TxDOT pavement experts completed the AHP survey with the required consistency ratio. Figure shows the overall relative weights for the goals comparison.

11 Overall Relative Weight J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano Safety ST CS LT CS SC Comfort 0 Goals Note: Safety = Enhance safety: Goal ST CS = Preserve and increase short-term pavement condition:goal LT CS = Preserve and increase long-term pavement condition: Goal SC = Enhance system structural capacity: Goal Comfort = Enhance user s satisfaction: Goal FIGURE Overall Relative Weights for the Goals Comparisons By examining the results shown in Figure, the following important observations can be obtained: The overall relative weights (ORW) indicate that enhancing safety is a key element in screening candidate projects for treatment in Texas. This result is consistent with TxDOT strategic goals to maintaining a safe transportation system. Regardless of the participants background and experience, safety turns out to be one of the most important goals in transportation agencies. ORWs for the goals show large variations for all the five goals considered. In this study, large variations were expected since participants came from different backgrounds and experience. For example, a pavement expert from the maintenance division could suggest that the short-term condition score is more important than enhancing pavement safety. For this reason, the AHP analysis was used to capture subjective judgments with the objective of better understating pavement experts within TxDOT. Since the AHP analysis was based on a distributive-mode relative AHP procedure, the ORW should not be used as an absolute value. The ORWs only provides a relative comparison of the various goals being compared. Therefore, the ORW should not be

12 Overall Relative Weight (ORW) J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano confused with coefficients obtained from regression analysis, and the ORWs should not be used directly in a model. The ORWs for the goals suggest the possibility of developing hierarchy ranking module, where a project list for each goal could be generated. By ranking projects by goals and not only using pavement-related factors, the final -year Plan could be enhanced. Figure shows the ORWs for the pavement-related factors considered in the AHP. These ORW results indicate the average relative preference of the participants. Moreover, these ORWs are considered the outcome of the AHP analysis. 0 0 FIGURE ORWs for the Pavement-related Factors By examining the results shown in Figure, the following important observations can be obtained: 0 The PMIS Rater s Manual () indicates that the ranking distresses from of worst to best for determining the lane to be rated are: failures, alligator cracking, block cracking, patching, longitudinal cracking and transverse cracking. As seen in Figure, the top ranked factors are consistent with the PMIS Rater s Manual. If a pavement presented distresses such as failures and alligator cracking, it will be a candidate for M&R treatments.

13 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano Rutting and ride quality are not rated by the PMIS Rater s Manual, but rather collected using automated visual distress measurement vehicles. A physical factor such as rutting is expected to have a higher ORW compared to other non-physical factors. Since the currently used PCSRM is based on condition score, change in condition score since last year, distress score, and change in distress score since last year, it is expected that factors related to these parameters are ranked on the top of the list. Distress score is captured correctly by the ranked factors presented in Figure ; however, ride score, change in ride score, and change in distress score are ranked lower than expected. This result suggests that the PCSRM ranking could be enhanced by incorporating additional factors in the model. Various studies have suggested that factors such as routine maintenance expenditure, M&R expenditures, years since last resurfacing, and years since last rehabilitation could play an important role in selecting and ranking M&R candidate projects. However, these factors ORWs were among the lowest values in the list. Similar to the ORWs for the goal, the variability for the ORWs is large. Since the AHP procedure in this study was developed to capture subjective judgment, large variability is expected. This large variability is due to the different line of thoughts expressed by the various participants. APPLICATION OF THE AHP The main result of the AHP survey is the ORWs for each of the pavement-related factors considered in the AHP analysis. In this section, clustering analysis was further used to group the identified pavement-related factors according to their ORWs. By clustering these factors, decision makers would able to discard factors not considered important by the pavement experts at TxDOT. The objective was to select the highest ranked pavement-related factors to be incorporated in potential PCSRM improvements. By selecting the highest ranked factors, efforts in data collection would drastically reduce without compromising the quality of the results. Cluster analysis is the organization of a collection of patterns (usually represented as a vector of measurements) into clusters based on similarity (). In this study, the k-mean clustering technique was used. The k-mean clustering technique is an algorithm for partitioning n data points into K disjoint subsets S j containing N j data points so as to minimize the sum-of-square criterion. This criterion is shown as follows: K J(V) = x n μ j () where, J(V) K j= nεs j S j x n μ j = equation to minimize of the sum-of-square criterion = number of clusters = disjoint set containing n data points = vector representing the nth data point = geometric centroid of the data points in S j x n μ j = Euclidean distance between x n and μ j

14 ORW J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano 0 A major problem with this algorithm is that it is sensitive to the selection of the initial partition and may converge to a local minimum of the criterion function value if the initial partition is not properly chosen. However, since there are no specified patterns in this study, this problem was not considered. Another important issue that needs to be addressed before applying the k-means is how many clusters there are in the data. This is not known a priori and, in fact, there might be no definite or unique answer as to what value k should take. Due to the limited data set, cluster sizes from two to five were analyzed. After carefully analyzing results, a cluster size of four generated the best outcome. Additionally, each cluster was assigned with a description according with its cluster average value. Figure and Table shows the results of the clustering analysis. 0 Failure Rutting Routine_ME M&R_Expen Y_sl_Resur Y_sl_Rehab Fat_Crack Truck_ADT Total_ADT Rema_Struc_Life Clusters FIGURE Clustering Analysis Results TABLE Clustering Analysis Statistic Results Cluster Importance Elements Mean SS Essential Discard.. Strong..0 Moderate. 0. Longi_Crack Ride_ Score Chan_Ride_Sco Chan_Dist_Sco Goodness of Fit.

15 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano By examining the results shown in Figure and Table, the following important observations can be obtained: The currently used PCSRM already considers the elements in the essential cluster through the PMIS condition score. The PMIS combines pavement distress data such as rutting, failures, and cracking. However, using these factors as direct parameters in future PCSRM improvements could increase the matching between ranking projects and the DPE final project list. Elements in the essential clusters should be considered in future PCSRM updates. Datasets for these elements should be easy to integrate to the PCRSM, which could increase the accuracy of the model. Fatigue cracking is already considered through the condition score; however, similarly to the essential cluster elements, more importance should be given. Factors already considered in the PCSRM, such as the change in condition score since last year, distress score, and change in distress score since last year, were located in the moderate cluster. Results suggest that additional factors could aid in enhancing PCRSM features. Elements in the discard cluster were not considered important when selecting and ranking candidate projects for M&R. Therefore, these elements should not be considered in further analysis. CONCLUSIONS The overall objective of this paper is to identify pavement-related factors that can be used to improve the accuracy of the PCSRM protocol currently used by using the AHP analysis. A methodology based on the AHP was developed aiming to achieve the objective. The proposed methodology consist of three major components: AHP design, AHP survey, and factors selection. Moreover, as part of this study, an Excel-based survey tool was developed to provide a relative easy visual interface to the participants. The major observations drawn from the AHP include: The AHP analysis provides an alternative to empirical mathematical indices for prioritization of M&R works. These indices usually do not have a clear physical meaning and cannot accurately and effectively convey the priority assessment of highway agencies and engineers. However, the AHP allows decision makers to capture subjective judgments, and prioritize elements according to relative weights. It is worth noting that this study is focused in improving the project selection process conducted in the Austin District. The Excel-based survey tool provided a powerful tool to capture judgments from the various participants. The AHP survey should be carefully designed to guiding the user through the entire procedure. More accurate results are expected if the participants have a better sense of the AHP procedure. Moreover, by introducing the checking for consistency feature, results become more reliable. Furthermore, the major conclusion drawn from the AHP results include:

16 J.D. Porras, M. Murphy, H. Wu, Z. Han, Z. Zhang, and M. Arellano The PMIS combines pavement distress data such as rutting, failures, and cracking into a condition score; however, the results of this study suggest that these parameters should be treated as direct parameters in the PCSRM. In addition factors such as truck ADT and total ADT should also be considered in the algorithm with significant weight values. Factors already considered in the PCSRM, such as the change in condition score since last year, distress score, and change in distress score since last year, were located in the moderate cluster. This result suggests that the importance of these factors should be reduce in the PCSRM or exchange with more significant important factors from the AHP results. Expenditure-related factors were not considered important when prioritizing projects according to the AHP results. These results suggest that the current PCSRM should not include these factors. Moreover, years since last resurfacing or last structural rehabilitation were weighted with low importance.

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