239.full

http://pwm.sagepub.com/Public Works Management & Policy

http://pwm.sagepub.com/content/13/3/239The online version of this article can be found at:

DOI: 10.1177/1087724X08327574 2009 13: 239 originally published online 4 December 2008Public Works Management Policy

Aristeidis Pantelias, Gerardo W. Flintsch, James W. Bryant, Jr and Chen ChenAsset Management Data Practices for Supporting Project Selection Decisions

Published by:

http://www.sagepublications.com

can be found at:Public Works Management & PolicyAdditional services and information for

http://pwm.sagepub.com/cgi/alertsEmail Alerts:

http://pwm.sagepub.com/subscriptionsSubscriptions:

http://www.sagepub.com/journalsReprints.navReprints:

http://www.sagepub.com/journalsPermissions.navPermissions:

http://pwm.sagepub.com/content/13/3/239.refs.htmlCitations:

What is This?

- Dec 4, 2008 OnlineFirst Version of Record

- Feb 17, 2009Version of Record >>

at National School of Political on May 20, 2014pwm.sagepub.comDownloaded from at National School of Political on May 20, 2014pwm.sagepub.comDownloaded from

239

Asset Management Data Practices forSupporting Project Selection DecisionsAristeidis PanteliasThe University of Texas at AustinGerardo W. FlintschVirginia Polytechnic Institute and State University, BlacksburgJames W. Bryant Jr.Transportation Research Board, Washington, DCChen ChenAsian Development Bank, Pacific Department, Manila, Philippines

Transportation agencies engage in extensive data collection activities to support their decision processes at various levels,but not all data collected supply useful information. This article summarizes research aimed at formally identifying linksbetween data collection and the supported decision processes, particularly at the level of project selection. The aim was tohelp transportation agencies optimize their data collection and cut down data collection and management costs. The method-ology included a comprehensive literature review that collected information from various academic and industry sourcesaround the world and the development of a Web survey that was e-mailed to specific expert individuals within the 50 U.S.Departments of Transportation and Puerto Rico. The responses obtained from the Web survey were analyzed statisticallyand combined with the additional resources to extract conclusions about the current state of the practice and develop datacollection recommendations in the form of a proposed stepwise framework.

Keywords: asset management; data collection; decision processes; decision levels; project selection; Web survey

Background

Transportation asset management is a strategicapproach to the optimal allocation of resources for themanagement, operation, maintenance, and preservationof transportation infrastructure (Federal HighwayAdministration [FHWA], 1999). It combines engineer-ing and economic principles with sound business prac-tices to support decision making at the strategic,network, and project levels, and its scope spans beyondphysical assets to encompass all transportation assetswithin a state or local transportation agency.One of the key aspects of the development of asset

management is data collection. The way in which trans-portation agencies collect, store, and analyze data hasevolved along with advances in technology, such asmobile computing (e.g., handheld computers, laptops,tablet notebooks, etc.), sensing (e.g., laser and digital cam-eras), and spatial technologies (e.g., global positioningsystems, geographic information systems, and spatially

Public Works Management &Policy

Volume 13 Number 3January 2009 239-252

2009 SAGE Publications10.1177/1087724X08327574http://pwmp.sagepub.com

hosted athttp://online.sagepub.com

Authors Note: Please address correspondence concerning this arti-cle to Aristeidis Pantelias, The University of Texas at Austin, 1University StationC1761, Austin, TX 78712-1076; e-mail: [email protected].

enabled database management systems). These technolo-gies have enhanced the data collection and integrationprocedures necessary to support the comprehensive analy-ses and evaluation processes needed for asset management(Flintsch, Dymond, & Collura, 2004).The concept of asset management has been defined by

various governmental organizations, transportation agen-cies, and industry groups Danylo & Lemer, 1998;TransportationAssociation of Canada, 1999; Organizationfor Economic Cooperation & Development, 2002;National Cooperative Highway Research Program[NCHRP], 2002). The FHWA defines asset managementas follows:

at National School of Political on May 20, 2014pwm.sagepub.comDownloaded from

[Asset management is] a systematic approach of main-taining, upgrading, and operating physical assets cost-effectively. It combines engineering principles withsound business practices and economic theory, and itprovides tools to facilitate a more organized, logicalapproach to decision-making. Thus, asset managementprovides a framework for handling both short- and long-range planning. (FHWA, 1999)

In many cases, however, the data collection activitieshave not been designed specifically to support the deci-sion processes inherent in asset management. As a result,the use of the aforementioned technologies has led agen-cies to collect large amounts of data and create vast data-bases that have not always been useful or necessary forsupporting decision processes.

ObjectiveTo support asset management, agencies must collect,

store, manage, and analyze large amounts of data in aneffective and efficient manner. Agencies have stronglyemphasized collecting and integrating data, and signifi-cant effort has been directed into linking the data collec-tion to their decision-making processes. By focusing onthe use of the data and the nature and needs of the deci-sion levels and processes to be supported, transportationagencies can define what assets and which data aboutthese assets are more important for decision making andtailor their data collection accordingly.This article presents a study of the state of the practice

on how state departments of transportation (DOTs) arelinking their data collection policies, standards, andpractices to their asset management decision-makingprocesses. The study focused primarily on project selec-tion, the decision-making level that functions as an inter-mediate stage between high-level strategic and low-levelproject-specific decisions. Projects under considerationinclude capital and maintenance works of physical trans-portation assets with a specific focus on highway infra-structure (i.e., pavements, bridges, culverts, etc.).

MethodologyThe investigation started with a comprehensive litera-

ture review to retrieve related experience informationfrom academia and industry sources throughout theworld. Several reports have documented current and pastpractices in the United States and Canada as well as inEurope and Australia. The literature review summarizesthe state of the art in asset management, decision making,and data collection.

To complement the literature review, a Web-surveyquestionnaire was developed to capture the current levelof asset management endorsement and implementationwithin state DOTs as well as specific aspects of their datacollection practices and their relationship with the pro-ject selection level of decision making. A link to the sur-vey was distributed to specific DOT officials in all 50states and Puerto Rico, and the responses were storedand subsequently analyzed.The information obtained was ultimately used to

extract conclusions about the current state of the practiceof U.S. DOTs in asset management data collection andrelated decision-making activities, primarily for projectselection. It was also used to develop recommendationsfor effective and efficient data collection by means of astepwise framework. The proposed framework aims tohelp transportation agencies tailor their data collectionactivities according to their real decision-making needsand, in the same way, contribute both to the reduction ofdata collection costs and to a more effective and efficientimplementation of asset management.

Literature Review

Asset Management, Data Collection,and Decision LevelsThe emerging field of asset management has been

extensively researched and many contributions havebeen made to date by transportation agencies and otherrelated stakeholders in the United States and aroundthe world. In particular, the Transportation AssetManagement Guide by the American Association ofState Highway and Transportation Officials (AASHTO)represents a milestone domestic reference (NationalCooperative Highway Research Program (NCHRP,2002). This guide was prepared to assist state DOTs intailoring a generic asset management framework to theirindividual needs and characteristics. It was developed inthe context of the NCHRP and based on up-to-date expe-rience and research findings. This guide describes assetmanagement as a strategic approach to managing infra-structure assets and identifies two major clusters of deci-sion making: resource allocation and use. The processdescribed is oriented to the actual implementation of theasset management concepts and methodologies. Otherimportant recent contributions include NCHRP (2005),NCHRP (2006), and FHWA International TechnologyExchange Program (2005); additional details on support-ing literature can also be found in Pantelias (2005).Good asset management implies a systematic inte-

grated approach to project selection, analysis of trade-offs,

240 Public Works Management & Policy


resource optimization, programming, and budgeting. Thisform of management relies on accurate asset inventory,condition, and system performance information; considersthe entire life cycle cost of the asset; and combines engi-neering principles with economic methods, thus, seekingeconomic efficiency and cost-effectiveness. AASHTO(NCHRP, 2002) presents a framework for transportationasset management as a philosophy, process, and resourceallocation and utilization process. The framework includesthe functions for policy-driven planning based on systemperformance; economic, social, and environmental objec-tives; integrated analysis tools to evaluate trade-off amongasset classes, performance goals, and types of invest-ments; decisions on allocation of resources and invest-ments; implementation of the programs through differentexecuting modes; and system monitoring on the systemperformance evaluation to determine the degree of accom-plishment of the defined goals (NCHRP, 2002).According to FHWA (1999), an asset management sys-

tem should be customer focused, mission driven, inte-grated (system-oriented), long term in outlook, flexible,accessible, user-friendly, and should have explicit goalsfor asset performance. This decision support system has tosupport trade-offs considering costs, benefits, and otherimpacts of the alternative potential investments. Thisrequires appropriate information gathering and process-ing. Thus, one of the key building blocks of any automatedasset management application or integrated asset manage-ment system is a comprehensive inventory of the highwayinfrastructure assets and their respective condition. Thesesystems may be designed to manage one type of trans-portation asset (e.g., pavements or bridges) or severaltypes of assets in a comprehensive and integrated manner.In terms of implementation, most transportation agen-

cies have basic bridge and pavement data for their trans-portation networks; several of them have also made agreat effort over the years to collect, store, manage, andanalyze comprehensive inventory data for their otherhighway infrastructure assets (Data Integration, 2001;Larson & Skrypczuk, 2004; Virginia Department ofTransportation, 2004). Sanford and McNeil (1998) pro-posed a data requirements model for bridge conditionassessment and a bottom-up process that an agency canuse to assess and improve its data collection practices.Furthermore, there has been significant work on dataintegration (Data Integration, 2001; FHWA, 2004; Hall,2005; Vandervalk-Ostrander, Guerre, & Harrison, 2003).Asset management requires the monitoring of the

transportation systems using performance measures thatreflect the agencys goals and objectives. The NCHRP(2006) Report 551 defined performance measurement as

a way of monitoring progress toward a result or goal,more specifically it helps to keep track and forecast theimpacts within and outside the system. Existing perfor-mance measures and analytical approaches are used toevaluate the effects of highway investment on economicproductivity, user benefits (that include reductions infatalities and serious injuries), environmental impacts,and societal benefits, among other factors. The reportidentified numerous performance measures and orga-nized them into the following 10 categories: preserva-tion, accessibility, mobility, operations, maintenance,safety, environmental impacts, economic development,social impacts, security, and delivery (FHWA, 2005).Furthermore, in every managerial process, there exists

an internal hierarchy of decision-making levels. Higherlevel decisions require a higher level of aggregation of thesupporting information and produce decisions with awider scope than those at the lower levels. Transportationasset management is no exception. There are variousdecision-making levels that represent different perspec-tives on the business functions of the agencies, rangingfrom very specific, detailed, project-oriented views togeneralized, comprehensive, and strategic ones. Althoughasset management is mostly perceived as a strategic-leveltool, it nevertheless affectsand can be equally success-ful inlower levels of decision making within a trans-portation agency.The decision levels pertaining to asset management

can be broken down into strategic, network, and projectlevels (Haas, Hudson, & Zaniewski, 1994). Strategicdecision making pertains to strategic resource allocationand utilization decisions concerning all types of assetsand systems within the constructed environment, one ofthem being the transportation sector. Within transporta-tion, it may consider all different modes and all assetspertaining to these modes. The network decision-makinglevel pertains to determining the overall agency-widemaintenance, rehabilitation, construction strategies, andwork programs. This decision level considers sys-temwide budget allocation and transportation planningdecisions, but its scope is narrower than the strategiclevels. Project-level decision making and analysis per-tains to the specific, modewise, assetwise, and geograph-ically determined projects. It addresses the design of theprojects included in the overall work plan needed to meetthe agencys performance measures. It is also called fieldlevel or operational level and refers to how the actualwork is going to be done.The network level is itself often broken down into pro-

gram and project selection levels (Haas, Hudson &Zaniewski, 1994). The program decision-making level is

Pantelias et al. / Data Collection and Project Selection Decisions 241


concerned with the overall, networkwide programmingof actions and allocations. It is involved in policy deci-sions, and the aim is the systemwide optimization offunds allocated to rehabilitation, maintenance, or newconstruction of infrastructure assets. The project selec-tion level is concerned with decisions on funding for pro-jects or groups of projects. This level generates decisionsat a higher level of aggregation than the project level, butit requires more detailed information than the programand network levels. It serves as a link between the net-work level and the subsequent project level of analysis.As explained previously, the different levels of deci-

sion making have different roles: Higher levels aremostly concerned with overall budget allocations andsystem utilization, whereas lower levels tend to focusmore on the administration, funding, and engineering ofspecific functions and processes. In addition, decisionmakers have different backgrounds and interests. As aresult, the decisions at each level are different in scopeand require different aggregation of information, affect-ing directly the corresponding detail and quantity of thedata to be collected. Higher levels require more general-ized information, whereas lower ones require moredetail. The data needed to support decision making ineach of the levels are of paramount importance to thesuccess of an asset management process and directlydetermine the corresponding data collection costs. Thisarticle focuses primarily on the data needs and corre-sponding data collection practices of the project selec-tion level of decision making.

Decision Making at the Project Selection LevelProject selection is a level of decision making that

entails the evaluation of the attributes of different candi-date projects for the purpose of funding and implementa-tion. These projects, as mentioned before, include capitaland maintenance works for different classes of physicalassets. As a decision-making level, project selection func-tions as a bridge between high-level network decisionsand site-specific, detailed project-level decision making.The project selection analysis is based on informationthat is aggregate enough to show the big picture of thecompeting projects and, therefore, identify and assesstheir usefulness and overall impact. However, it is alsodetailed enough to capture the individuality of each pro-ject, provide accurate cost estimates, and identify imple-mentation implications for the agencies and the users.Within the framework of asset management, the

nature of project selection is unique because in manycases the candidate projects concern different assets andtreatments. As an example, trade-offs may consider the

rehabilitation of an existing flexible pavement throughmilling and repaving versus the maintenance of a con-crete pavement through crack sealing. Trade-offs mayalso consider the maintenance of the roadside drainagesystem of a particular segment of a highway versus therehabilitation of the bridges concrete deck or thereplacement of its steel railings.According to Hudson, Haas, and Uddin (1997) pro-

ject selection follows the overall network programmingdecision level regarding the general funds that are goingto be allocated in different types of agency works. Afterthe agency has decided on the amount of funds to bespent in maintenance, rehabilitation, or new construc-tion (or reconstruction), the candidate projects that fallinto each of these work programs need to be determined.The selection of the different projects or differentgroups of projects to be included in a work program isheavily constrained by available budgets and usuallyresorts to some type of prioritization models. Thesemodels usually use optimization, near-optimization, orother ranking techniques to lead to results that can beused by the transportation officials to support decisionmaking (Haas et al., 1994). Furthermore, the type ofcross-asset analysis performed at the project selectionlevel represents a level of decision making of its ownand is not to be confused by similar analyses that cantake place at the program level.Worldwide practice in the area of project selection has

shown that for the analysis to be as comprehensive andas accurate as possible, the effects of economic and tim-ing parameters should also be considered. As a result,recent research in this area has focused on proposingcompetent models that include economic analysis andmultiyear prioritization algorithms among others, andsome of these models also take into account the effectsof risk and uncertainty in their final outcome (Li &Sinha, 2004). The most commonly used economic analy-sis techniques are discussed in more detail elsewhere inHaas, Hudson, and Zaniewski (1994), Haas, Tighe, andCowe Falls (2004), and Flintsch and Kuttesch (2004).Because most agencies carry out project selection by

using specific quantitative and qualitative tools similar tothe ones described above, it is reasonable to assume thatthe data needs of this particular level of decision makingshould include the type and amount of data that form theinputs of these models and techniques. In other words,project selection data needs should be focused on theparticular inputs that the project evaluation modelsrequire. As different agencies use different models andtechniques, the particular data needed for each agencyare also bound to be different.



Data Characteristics and PropertiesAnother important issue of data collection relates to the

particular attributes and characteristics that the collecteddata should possess to be useful in supporting decisionmaking. Regardless of the projected use of the collecteddata, it is of paramount importance that they exhibit thefollowing characteristics (Deighton, 1991, chap. 3):

Integrity: Whenever two data elements representthe same piece of information, they should be equal(in value or meaning).

Accuracy: The data values should representas closely as possible the considered piece ofinformation.

Validity: The given data values should be correct interms of their possible and potential ranges ofvalues.

Security: Sensitive, confidential, and importantdata are protected by restricting access to them andby properly ensuring systematic and frequent back-ing-up in other storage media.

Furthermore, the timeliness of the data is also impor-tant, so that the data reflect the current characteristics andcondition of the assets. It is also recommended that thedata elements be rigorously defined in a data dictionaryand thatin the most ideal of all casesthese definitionsbe common between all agencies and parties involved inthis area of practice (Deighton, 1991, chap. 3).In addition, the Western European Road Directors

(WERD, 2003) guidelines highlighted the importance ofthe following criteria when selecting data required by anagency/organization:

Relevance: Every data item collected and storedshould support an explicitly defined decision need.

Appropriateness: The amount of collected andstored data and the frequency of their update shouldbe based on the needs and resources of the agencyor organization.

Reliability: The data should exhibit the requiredaccuracy, spatial coverage, completeness, andcurrency.

Affordability: The collected data should be inaccordance with the agencys financial and staffresources.

Consequently, agencies planning to engage in data col-lection should take into account the specification of the datato be collected, the frequency of collection, the accuracyand quality that they should exhibit, and their completeness

and currency (WERD, 2003). As a general recommenda-tion, it is noted that the accuracy, quality, and timeliness ofthe data should be decided based on the cost of the data col-lection and the value and benefit associated with the data inquestion: Data should only be collected if the benefits thatthey provide outweigh the cost of their collection and main-tenance (WERD, 2003). Data collection costs can andshould be minimized by collecting only the needed datawhen they are needed. The data collection activities andmethods used should be based on the requirements of thedecision processes to be supported and should produceresults that match the levels of accuracy, precision, and res-olution required by these decision processes (Smith &Lytton, 1992).

Web-Based Transportation AssetManagement Survey

To complement the literature review and to capturethe corresponding state of the practice in the UnitedStates and Puerto Rico, a two-part Web-based surveywas developed and posted on the Internet. The surveywas sent to 103 transportation officials from all 50 statesand Puerto Rico, and responses were stored in a specifi-cally designed database and subsequently processed andanalyzed.The first part of the survey, entitled General Agency

Information on Asset Management, contained questionson the following:

asset management endorsement and implementa-tion by the state DOTs;

existing and/or planned management systems forspecific asset types;

existing levels of decision making within stateDOTs;

identification and rating (based on their perceivedimportance) of existing asset management decisionprocesses and functions within state DOTs; and

identification and rating of existing criteria used bythe agencies for project selection.

The second part of the questionnaire, entitledRoadway Asset Management, required more special-ized and detailed input on current agency practices ondata collection and project selection, and it containedquestions on the following:

datamanagement, collectionmethods, and integration; rationale behind existing and/or future planned datacollection;



evaluation of roadway asset data used for projectselection; and

identification of formally documented linksbetween data collection and project selection orasset management decision processes in general.

The contents of the survey were refined several timesfor suitability of content, wording of the questions, andoverall format. Comments and input were requested andreceived from the Statistics and Survey Department ofVirginia Tech as well as from the AASHTO expert taskgroup supervising the project. The authors will be happyto provide copies of the complete survey forms that wereused to create the Web-based questionnaire on requestfrom readers.

Survey AnalysisA total of 48 responses from 40 different states were

received. Based on the initially targeted number of states andrespondents, a response percentage of 78% was achieved interms of individual states and 47% in terms of individualrespondents. The respondents were the main asset manage-ment contacts (e.g., director of the asset management divi-sion) for the state DOTs as identified by FHWA.For seven states, more than one transportation official

responded to the survey. Furthermore, whereas some ofthe questions on the survey were state specific andrequired only one valid answer per responding state,others requested the personal opinion of the respondingstate transportation officials. Because of this difference,two approaches were followed in processing theresponses of the states that provided more than oneresponse:

1. In the first case, the various answers within thesame state were compared, and discrepancies wereresolved, so that only one answer that was as com-plete as possible was kept, based on the followingcriteria: Priority was given to the most completeresponse; for example, in the case in which onetransportation official reported that the stateagency possessed two management systems forspecific asset classes and another officialreported the possession of three, the finalresponse contained all three individual manage-ment systems from the second response.

Priority was given to the responses of trans-portation officials whose area of expertisemost closely coincided with that of the surveysquestions and input fields.

Therefore, the responses used in the analysis are acombination of the responses provided by the dif-ferent officials.

2. In the remaining cases, in which the survey ques-tions asked for individual opinions, all 48 answersfrom all responding officials were consideredvalid and were used in the analysis.

Answers to essay questions were not considered inthe statistical analysis but were rather used as a guidefor the resolution of discrepancies and also as a com-pass for the overall status of the responding state inrelation to the researched topics.In the following sections, the results obtained from the

survey responses are summarized and discussed.

Asset Management System ImplementationThe responses to the first question concerning the

implementation stage of an asset management systemrevealed that a significant portion of the respondingstates (24 out of 40) are still in the planning phase. Onlyapproximately one quarter (11) of the respondents indi-cated that they have already implemented an asset man-agement system.The responses also revealed that most of the responding

states have been using stand-alone management systemsfor specific asset types, the most predominant amongthem being pavement (39), bridge (39), and maintenance(34) management systems. Other systems include safety(SMS), congestion (CMS), public transportation (PTMS),and intermodal transportation (ITMS) management.However, for most of these states, the level of integra-

tion of these individual systems within an overall assetmanagement framework is still in the planning phase.Pavement and bridge management systems seem to beone step ahead of the remaining systems in terms of thisintegration.

Decision Levels and ProcessesWhen asked to report on their defined decision-mak-

ing levels, most of the responding transportation agen-cies indicated that they have explicitly defineddecision-making levels (Figure 1). These decision levelsin general coincide with the ones found in the literature.The decision-making levels most frequently identified

by survey respondents were programming and budgeting,and project selection. This finding implies that the agen-cies have been focusing their attention on these intermedi-ate levels connecting the generic strategic decisions of thestrategic level to the actual project implementation at the



project level. It also suggests that the right transportationofficials were selected to complete the survey becausethey were familiar with these levels of decision making.Further on, the state transportation officials were

asked to rank a list of identified asset management deci-sion processes in terms of their importance. Figure 2summarizes the responses and shows that most of thelisted decision processes fall in the very important andsomewhat important categories.The relative importance of the decision processeswas deter-

mined by computing the average importance rating for eachdecision process using a score from 1 to 4 (4 = very importantto 1 = not important at all). The resulting ranking reflects theperceived relative priorities of the responding states. The mostimportant decision process reported by the agencies surveyedwas performance evaluation and monitoring, with fiscalplanning following close behind. Project selection,which is themain interest of this investigation, ranked third together with

resource allocations, indicating the perceived significance ofthis decision process among the responding transportation offi-cials. On the other hand, the low ranking of processes such asdevelopment of alternatives and impact analysisprocessesimportant in understanding cost trade-offs and performinginvestment analysescannot be left unnoticed. This showsthat a lot of states have not fully understood some of the basiccharacteristics of assetmanagement and demonstrates a signif-icant gap between state of the art and state of the practice inasset management.Figure 3 summarizes the importance assigned by the

state transportation officials to a list of specific projectselection criteria. The variability of opinions is moresignificant in this question. The criterion of availablebudgets/earmarked funds stands out as most important,followed closely by engineering parameters. The bottompart of the figure shows the average rankings for all thelisted criteria based again on their computed relative


Figure 1Defined Decision-Making Levels



Figure 2Asset Management Decision Processes and Their Relative Importance



Figure 3Project Selection Criteria and Their Relative Importance



importance. An interesting finding from this question isthat public demands/user opinions rank third, showingthe increased interest of transportation agencies in pub-lic satisfaction with the selection and implementation ofprojects.Furthermore, and to the surprise of the research team,

the vast majority (80%) of the responding officialsagreed that the criteria used for project selection cannotor should not be uniform for and consistent across alltypes of roadway assets. This, however, presents a con-tradiction to known common practices identified in theliterature review where cross-asset comparisons areperformed through costbenefit, life cycle costing, andvarious other engineeringeconomic analyses and prior-itization/optimization methods. Although agency, user,and community costs and benefits were proposed as cri-teria for such comparisons, none of them appeared in thetop three relative importance ranking positions denotingthat there is a lack of agreement between the literaturereview and the survey results. This result, again, can beinterpreted as a lack of understanding of what good assetmanagement practices entail, supporting the previouslymentioned lag between the state of the art and the stateof the practice in asset management.

Data Collection ProceduresMost of the responding state agencies (75%) have already

invested time and money in developing asset managementroadway inventories and databases. The majority of theremaining agencies reported that theywere planning for them.Most agencies have also been collecting data predominantlyfor their pavements and bridges. Traffic items (e.g., attenua-tors, guardrail, pavement striping, pavement markings) androadside assets (e.g., trees, historic markers, right-of-wayfence) also made up a large portion of the collected data.Figure 4 summarizes the data collection methods used

for the acquisition of the above data. Whereas for someassets (e.g., drainage) the collection is reported to be takingplace mostly manually, there is a trend toward using a com-bination of manual and automatic methods. This is consis-tent with what was reported recently by Flintsch, Dymond,and Collura (2004).

Data Collection RationaleThe officials were also asked to provide information

about their rationale behind data collection. The responsesappear to support the findings from the literature reviewthat most agencies still base their data collection decisions(e.g., which data to collect) on past practices and staff

experience. However, many respondents also noted thatdata collection practices have been based on data collec-tion standards and input needs of management systemsused or other defined decision processes. This confirmedthe initial finding from the literature review that there is anongoing effort to link data collection to specific decisionprocesses.The next question asked state officials to rate the impor-

tance of identified roadway asset data in choosing betweentwo competitive projects. The ratings are summarized inFigure 5. From the responses obtained, the most importantdata are the assets structural and functional conditions,with usage of the assets following in third place.The results indicate that responding agencies tend to

use the same kind of data to prioritize projects betweendifferent types of transportation infrastructure assets.Again, however, life cycle costs were not one of the topthree responses (although these costs were cited exten-sively in the literature review as key to project trade-offanalysis and comparisons).Finally, the last question of the survey investigated the

level at which state transportation agencies were con-scious about the existence of links between their datacollection activities and project selection. From theresponses, it was determined that several agencies haveidentified (32.5%) or identified and documented in a for-mal way (52.5%) the existence of such links. This is animportant finding because it shows that most agencies(85%) have been trying to rationalize their data collec-tion according to specific decisions to be supported, atleast for the project selection level. This is also consis-tent with the literature that reports significant efforts toimprove data collection and management practices.

Recommended Framework forData Collection

The literature review confirmed that research in thearea of asset management and the related data collectionhas been extensive. Little information, however, could befound concerning specific data collection to support pro-ject selection within the framework of asset manage-ment. As this level links the overall network decisionswith the individual projects to be implemented, it conse-quently requires data that are simultaneously neither toogeneric nor too specific. Generic data, such as network-level performance indices, would not help in projectselection because they would ignore vital project details.On the other hand, it is usually not cost-effective to col-lect very detailed (e.g., project-level) data for the project



Figure 4Roadway Asset Data Collection Types and Methods

selection process, although such bottom-up approachescan also be used in cases where such data already exist.Furthermore, project selection has traditionally been

made between projects that belong to the same assetclass. Asset management supports the broadening of thistraditional practice by encouraging cross-asset compar-isons between the candidate projects for selection. Thishas obviously modified the data needs and has also cre-ated the need for the identification and use of effectiveselection methodologies that can be applied equally andin an unbiased way to all different asset classes. Suchmethods were identified in the literature to some extentbut not in the survey.The survey of current practice suggests that U.S. trans-

portation agencies have clearly identified decision-making

levels and also have relatively uniform perceptions of theimportance of various asset management decisionprocesses, project selection criteria, and correspondingasset data that could support selection between competingprojects.A proposed framework for effective and efficient

data collection is presented in Figure 6. This frame-work leverages findings from the literature review andresults from the survey of state transportation offi-cials. It can help agencies determine which data areneeded and customize their data collection practicesaccordingly.For an agency to evaluate its data collection needs for

project selection, the transportation officials shouldanswer the following questions:



Figure 6Proposed Framework for Project Selection Data Collection


What are the various types of roadway assets inneed of preservation, rehabilitation, or renewalactions? (Step 1)

What are the various types of preservation, rehabil-itation, or renewal treatments that should be con-sidered? (Step 1)

What are the evaluation models, techniques, andcriteria used by the agency to assess the usefulnessof the projects and prioritize/optimize their selec-tion? (Step 2)

What are the inputs required by these models andtechniques for the various projects to be assessed?(Step 3)

What are the data that already exist in available andaccessible databases and/or are already being col-lected? (Step 4)

What additional data need to be collected? (Step 5)

Once the needed data have been identified, the agen-cies can decide on the most appropriate method to obtainthem. This may mean either extracting them fromalready existing databases (where applicable and possi-bly through additional processing) or engaging in addi-tional data collection activities and correspondinglydeciding on their required level of accuracy, precision,and resolution (Step 6).As a final element of the proposed framework, a feed-

back loop should be established after the missing data havebeen collected and the project selections for at least one

cycle have been performed to evaluate the effectiveness ofthe models and their results and to further refine the mod-els, data inputs, databases, and collection methods.This framework, although generic, summarizes the

basic principles and best practices behind data collectionand can function as a starting point for transportationagencies that wish to handle project selection in a moresystematic way. By rationalizing their data collectionneeds, agencies can cut down the collection of unneces-sary data and achieve significant cost reductions.It is, however, obvious that the use of the proposed

framework would only lead to a partial optimization ofan agencys data collection activities. This frameworkaddresses only project selection decisions without takinginto account the needs of the other levels of decision mak-ing (e.g., network-level resource allocation or project-leveldesign) that might require overlapping or complementarydata and may warrant new, modified, or additional datacollection activities.For an overall true optimization of data collection, the

data needs of all levels of decision making (includingstrategic, program, and project levels in addition to theproject selection level) should be taken into considera-tion, and a more comprehensive framework for data col-lection should be established. For this purpose, thefindings of the survey concerning the most importantasset management decision processes can be used. Thesedecision processes can be attributed to all different deci-sion-making levels, and therefore, the consideration ofthe individual data needs of each one and their inclusionin a generalized version of the proposed framework canlead to a higher level of data collection efficiency.

Conclusions

Asset management has been endorsed by most statetransportation agencies as well as other major transportationagencies throughout the world. Asset management princi-ples have been used to manage individual classes of assets(such as pavement and bridges) for years. The state of theart has been steadily advancing, and significant contribu-tions have been made by various stakeholders. However,implementation of integrated asset management is still at itsinitial stages, and there are many hurdles to overcome. Inthis respect, the development of integrated roadway inven-tories and databases is still under way in many agencies andso is the integration of individual management systems.The survey results can be considered as a snapshot

of the degree to which asset management has penetratedthe industry to date and lead to some very interestingconclusions. Transportation agencies in the United States

Figure 6Proposed Framework for Project Selection Data

Collection


FEEDBACKLOOP:

Evaluation ofassessmenttools/ data

inputs/ datacollection andoverall results

STEP 1

STEP 2

STEP 3

STEP 4

STEP 5

STEP 6

(Feedback toupper levels)

Identification of project evaluation/assessment tools andtheir data requirements

Identification and/ordefinition of data needs for theanalyses/evaluations to be performed

Identification of typical candidate projects andcorrespondingpreservation and rehabilitation treatments

for all assets under consideration

Investigation of existing databases, available data, datacollection practices, and data quality assessment

procedures

Identification of missing data elements

Selection of the appropriate data collection methodologyfor the missing data elements and implementation


have explicitly defined decision-making levels and aremoving forward to a rationalization of their data collec-tion activities. Asset management practitioners, in gen-eral, agree that project selection criteria cannot or shouldnot be uniform and consistent for all asset types consid-ered. This is, however, inconsistent with known commonpractices documented in the literature where costbene-fit analyses or life cycle costing, among other methods,are used toward that end and should be a topic for furtherresearch and clarification. Past agency practices and staffculture are still the predominant decision factors behinddata collection but have started to give way to decisionsbased on data collection standards and input needs. Thestructure of the funding program, which is often alignedby asset categories, may also discourage agencies frommaking cross-asset life cost analyses In the particulararea of project selection, there also seems to be a for-mally established relationship between the data collectedand the decisions supported.A data collection framework for project selection is rec-

ommended to optimize the data collection activities forproject selection. Using a data collection framework, suchas the one proposed in Figure 6, can help provide clear andlogical steps toward the rationalization of the data needsfor these decisions. This framework, however, can onlypartially optimize the overall agency data collection activ-ities because it only addresses project selection decisions.Additional effort is needed to generalize the proposed datacollection framework for an overall data collection opti-mization, taking into account all agency decision levels.This research aims to help transportation agencies tai-

lor their data collection activities according to their realdecision-making needs. In this way, it intends to con-tribute both to the reduction of data collection costs andto a more effective and efficient implementation of assetmanagement in everyday practice.

ReferencesDanylo, N. H., & Lemer, A. (1998). Asset management for the public

works manager: Challenges and strategies. Findings of the APWAtask force on asset management. Washington, DC: APWA.

Data Integration for Asset Management Forum and Peer Exchange.(2001). Proceedings data integration for asset management forumand peer exchange. Retrieved October 21, 2008, http://openlibrary.org/b/OL3749208M

Deighton, R. (1991). An advanced course on pavement managementsystems. Boston: TRB, FHWA, and AASHTO.

Federal Highway Administration. (1999). Asset management primer.Washington, DC: Office of Asset Management, Federal HighwayAdministration.

Federal Highway Administration. (2004). Data integration: TheVirginia experience (Transportation Asset Management CaseStudies FHWA-IF-04-012). Washington, DC: Author.

FHWA International Technology Exchange Program. (2005).Transportation asset management in Australia, Canada, England,and New Zealand (FHWA-PL-05-019). Washington, DC: Author.

Flintsch, G. W., Dymond, R., & Collura, J. (2004). Pavement man-agement applications using geographic information systems(National Cooperative Highway Research Project 20-5, SynthesisTopic 34-11). Washington, DC: Transportation Research Board.

Flintsch, G. W., & Kuttesch, J. (2004, January). Application of engi-neering economic analysis tools for pavement management. Paperpresented at the 83rd Annual TRB Meeting, Washington, DC.

Haas, R., Hudson, W. R., & Zaniewski, J. (1994). Modern pavementmanagement. Malabar, FL: Krieger.

Haas, R., Tighe, S., & Cowe Falls, L. (2004, October). Generic pro-tocol for whole-of-life cost analysis of infrastructure assets. Paperpresented at the 6th International Conference on ManagingPavements, Queensland, Australia.

Hall, J. (2005). Enhancing the value of data programs: A peerexchange. Retrieved October 21, 2008, from http://onlinepubs.trb.org/onlinepubs/circulars/ec077.pdf

Hudson, W. R., Haas, R., & Uddin, W. (1997). Infrastructure man-agement systems. NewYork: McGraw-Hill.

Larson, C. D., & Skrypczuk, O. (2004, January). Comprehensive datacollection supporting asset management at Virginia DOT. Paperpresented at the 83rd Annual TRB Meeting, Washington, DC.

Li, Z., & Sinha, K. C. (2004, January). A methodology for multicrite-ria decision making in highway asset management. Paper pre-sented at the 83rd Annual TRB Meeting, Washington, DC.

National Cooperative Highway Research Program. (2002).Transportation asset management guide. Retrieved October 21,2008, from http://downloads.transportation.org/amguide.pdf

National Cooperative Highway Research Program. (2005). Reports545: Analytical tools for asset management. Retrieved October21, 2008, from http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_545.pdf

National Cooperative Highway Research Program. (2006). Report551: Performance measures and targets for transportation assetmanagement. Retrieved October 21, 2008, from http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_551.pdf

Organization for Economic Co-operation and Development. (2002).Asset management for the roads sector. Paris, France: Programmeof Co-Operation in the Field of Research on Road Transport andIntermodal Linkages, Organization for Economic Co-operationand Development, Directorate for Science, Technology andIndustry, Expert Group on Asset Management Systems.

Pantelias, A. (2005). Asset management data collection for support-ing decision processes. Unpublished masters thesis, VirginiaPolytechnic Institute and State University, Blacksburg.

Sanford, K., &McNeil, S. (1998).A practice-oriented approach to devel-oping a data requirements model for bridge condition assessment(Transportation Research Record 1642). Washington, DC: TRB.

Smith, R. E., & Lytton, R. L. (1992). Graduate course in pavementmanagement. Washington, DC: FHWA.

Transportation Association of Canada. (1999). Primer on asset man-agement. Ottawa, Ontario, Canada: Author.

Vandervalk-Ostrander, A., Guerre, J., & Harrison, F. (2003). Reviewof data integration practices and their application to transporta-tion asset management. FHWA-IF-03-023.

VirginiaDepartment ofTransportation. (2004).Assetmanagement data col-lection guide. Richmond,VA:Virginia Department of Transportation.

Western European Road Directors. (2003). Data management forroad administrations: A best practice guide. Retrieved fromwww.roaddata.org



Documents

239.full