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23 Transportation asset management is a data-driven process that requires collecting, processing, storing, and retrieving data from a variety of sources and putting the data to use in investment decision making. Having sufficient, detailed data in a usable format is critical to successful asset management implementation, so much so that data is frequently seen as a valuable asset requiring investment. Analytical tools are used to store and track data, and use the available data to support predictions of future conditions, analysis of investment need, and other applications. This section addresses the data and tools required to im- plement the Interstate Asset Management Framework. Sec- tion 4.1 provides an overview of how asset data and tools sup- port asset management. Section 4.2 describes existing data resources available for supporting management of IHS assets. Section 4.3 discusses existing analytical tools. Section 4.4 presents a set of recommendations for use of data and tools to support IHS Asset Management. Section 4.5 discusses gaps in the available data and tools. Appendix A details the litera- ture review performed as part of this research, and contains more detail on asset management data and tools. 4.1 Overview Implementing an asset management approach for the IHS first requires basic data on the full set of IHS assets. Table 2.1 lists the assets on the system, organized by roadway, struc- tures, safety features, and facilities. At a minimum basic loca- tion, inventory and condition data are needed for each asset type. Additional data on potential actions and their costs are important for modeling asset performance. Agencies typically have data for pavements and bridges, but have varying amounts of data for other assets. Although asset location, inventory, condition, and cost data are perhaps the most obvious and most fundamental types of data required for implementing asset management, the framework described in Section 2.0 establishes the need for other data as well. Specifically, characterizing measures related to mobility, safety and environmental performance necessarily requires data in each of these areas. Figure 4.1, reproduced from NCHRP Report 545 (8), pro- vides a vision for how asset data and analytical tools support asset management. As illustrated in the figure, analytical tools utilize core asset data, together with business rules and analy- sis parameters. These tools use techniques such as life cycle costing, risk analysis, simulation, and optimization to pro- duce their results, including analyses of needs and solutions, evaluations of different treatment options, and details on investment and performance tradeoffs. The ideal system for supporting transportation asset man- agement would be one that includes the following function- ality for all assets and investment types: ⢠Storing and retrieving condition data; ⢠Establishing goals and performance measures; ⢠Identifying needs; ⢠Predicting future conditions and service levels based on different investment scenarios and/or performance targets; ⢠Supporting development of capital and/or operating plans; and ⢠Monitoring results. Such a system could be used to help support each step of the asset management process. Many agencies began explor- ing the potential for management system integration in the wake of the requirements for agencies to implement seven types of management systems that were included in the Inter- modal Surface Transportation Efficiency Act of 1991 (ISTEA). In practice, however, the ideal asset management tool does not exist. Data requirements, business rules, and analysis ap- proaches vary significantly between asset and investment types, greatly complicating any attempt to define a single sys- tem for supporting the entire asset management process. Fur- ther, limits on computer processing speed and memory, C H A P T E R 4 Data and Tools for Interstate Assets
though continually being relaxed, are nonetheless important factors that tend to restrict system functionality and scope. Lacking the single, comprehensive asset management system, agencies must instead use a variety of different informa- tion systems, together with manual and/or spreadsheet ap- proaches, to support implementation of asset management. This section discusses five basic types of tools for supporting the Interstate Asset Management Framework: ⢠Investment AnalysisâThese systems provide general guidance on the performance predicted for one or more asset types given a specified budget level. ⢠Management SystemsâThis category includes pavement, bridge, and maintenance management systems, as well as others. These systems are designed to support a broad range of functions for one or more asset types. They generally have inventory and condition data and may contain additional functionality described for the other categories listed here. ⢠Needs and Project EvaluationâNeeds identification and project/treatment evaluation are central functions of an asset management approach. An extensive set of systems has been developed for supporting these functions. Typically these systems use data from other management systems or require project-specific inputs to perform analysis. Systems in this category include systems for needs identification; testing alternative policies for scoping, timing, or design; evaluation of projects or strategies; project prioritization; lifecycle cost analysis; and risk analysis. ⢠Risk AssessmentâThis category includes tools specifically designed to calculate risks of system failure, predict conse- quences of risk, and assist in prioritizing investments in risk mitigation. ⢠Results MonitoringâThis category includes systems that help monitor performance and costs over time, such as on costs and effects of maintenance and construction actions. 4.2 Asset Management Data Federal and state transportation agencies have been collect- ing highway related data since the 1950s. Although the reasons for data collection have varied, typically data have been col- lected to support infrastructure management practices, com- ply with Federal mandates, support research, and support Federal resource allocation. 24 Geography and Standard Location Referencing Inventory Inspection Traffic Crash Statistics Work History Programmed Work Business Rules ⢠Performance Measures and Standards ⢠Deficiency Criteria ⢠Design Standards ⢠Maintenance Standards ⢠Standard Procedures ⢠Program Categories ⢠Funding Levels Decision Support ⢠Needs and Solutions ⢠Evaluation of Options ⢠Investment vs. Performance Trade-offs Life-Cycle Costing Benefit/Cost Analysis Needs Simulation GIS Query and Analysis Tools Risk Analysis Database Query and Reporting Tools Sketch-Planning Impact Analysis Tools Network Models Specialized Databases Optimization Heuristic Decision Rules Analytical Tools Core Asset Data Analysis Parameters ⢠Unit Costs ⢠Service Life and Deterioration Models ⢠Discount Rate ⢠Value of Time ⢠Accident Costs ⢠Default Average Speeds ⢠Default Auto Occupancy Source: NCHRP Report 545. Figure 4.1. Context for an analytical toolbox.
This section provides an overview of data available for IHS Asset Management, focusing on Federally mandated data sets and other types of databases commonly maintained by state DOTs. Federally mandated data sets have significant potential for supporting the framework because all agencies have access to the same data in the same format. However, the framework is flexible so that agencies can augment Federal data with data from their other databases. While agencies typically follow Federal guidelines for data collection, even when collecting data that goes beyond Federal reporting requirements, in some areas there are no specific standards on how certain types of data are to be collected, stored, or reported. The fol- lowing subsections are organized by asset type, with additional subsections on mobility, safety, and environmental data. Almost every aspect of the Interstate Asset Management Framework requires data of some kind. Fortunately, as de- scribed below, most agencies have already made significant data investments and have access to a wide range of data. It is recommended that agencies work to fully leverage these exist- ing data resources. Roadway Data Highway Performance Management System (HPMS) The HPMS is a national transportation data system pro- viding detailed data on highway inventory, condition, perfor- mance, and operations. It describes functional characteristics, traffic levels, and pavement conditions for all IHS sections. Since its initial development, the HPMS has been modified several times. It has recently undergone a major reassessment, referred to as HPMS Reassessment 2010+. The objectives of this initiative were to reflect changes in highway systems, national priorities, technology, and to consolidate and streamline reporting requirements. The final assessment report (9), described in Appendix A, lists additional data items to be added to the HPMS. The current version of the HPMS includes two measures of pavement condition: PSR and IRI. HPMS 2010 will contain additional measures of rutting/faulting and cracking, consistent with AASHTO standards for pavement data collection. Pavement Management Systems (PMS) Databases Most agencies collect pavement data required to run a pavement management system (PMS). PMS databases are needed for supporting the Interstate Asset Management Framework, but there is no standard format for how this information is collected or stored. For example, in a recent survey of 45 state DOTs performed by Applied Research Associates, more then 80 percent reported that they collect basic pavement inventory data (e.g., pavement type, lane width, shoulder type, shoulder width, number of lanes, layer thicknesses). Between 35 to 40 percent reported that they collect detailed inventory data (e.g., pavement layer material properties, subgrade type, and drainage). The survey also showed variances in terms of the types of pavement condition data collected. All of the agencies sur- veyed collect IRI and rutting data. Over 90 percent collect data on fatigue/alligator cracking and transverse cracking, while 84 percent collect data on longitudinal cracking in the wheel path. In addition, 29 percent of the agencies in the sur- vey collect Present Serviceability Index (PSI), 69 percent col- lect surface friction, and 56 percent collect a composite index. (These figures are for hot-mix asphalt (HMA) pavement. They vary for other types of pavement.) Structure Data National Bridge Inventory (NBI) The NBI is a Federally mandated database of bridge inven- tory and conditions compiled by state DOTs for submission to FHWA. It contains data on all bridges and culverts on or over U.S. roads that are greater than 20 feet in length, and data on many tunnels. The NBI data set contains condition data by bridge component: deck, superstructure, substruc- ture, channel/channel protection, and culvert. It also contains data on a bridgeâs functionality, such as underclearances and load posting information. Pontis Bridge Management System (BMS) In contrast to the situation with PMS, most states (over 40) license the AASHTO Pontis BMS, making this system the defacto standard for BMS data in the United States. The Pontis database contains all the NBI data items, as well as more detailed element-level inspection details. For example, the NBI file contains a single condition rating for a bridgeâs super- structure. The Pontis database contains additional data on the distribution of conditions by condition state for each struc- tural element of the superstructure, including elements such as girders, stringers, floor beams, etc. AASHTO has developed standard element descriptions and condition state language, referred to as âCommonly Recognized (CoRe) elementsâ for use with Pontis and other BMS (10). However, states using Pontis may specify their own elements and most have done so, supplementing or replacing the CoRe element language. Further, most agencies that have implemented Pontis have added agency-specific data items to the Pontis database. Other Structure Data The NBI file described above contains inventory information for many tunnels and inventory and condition information for some culverts (where the length of the culvert measured 25
along the centerline of the roadway is over 20 feet long). There are no other Federal databases containing data on non- bridge structures, including tunnels, culverts, retaining walls, sign support structures, and other structures. However, a number of agencies store data on other structures in their BMS. The collection of data on these assets varies significantly between state DOTs. FHWA has published Guidelines for the Installation, Main- tenance, and Repair, of Structural Supports for Highway Signs, Luminaries, and Traffic Signals (11). This guide recommends performing an element-level inspection for these structures, similar to that commonly performed for bridges, and recom- mends specific elements that should be inspected for each structure type. Separately FHWA has developed the Highway and Transit Tunnel Inspection Manual (12) with recommended inspection practices for tunnels. AASHTO has published the Asset Management Data Collection Guide (13). This document contains recommendations regarding data collection for a number of assets, including drainage assets. There are no Federal guidelines for asset management data for culverts, retaining walls, or noise barriers, though Appendix A describes recent work describing available systems and approaches for these assets. Safety Feature and Facility Data Many state DOTs maintain some form of inventory of their safety features and facilities, ranging from paper files and maps to computer database systems. There are no Federal standards for collection of asset data for safety features and facilities, and no real consistency in the data available from one agency to another. The most common approach to using this to support asset management is through development of maintenance levels of service (LOS) as part of a performance-based budget- ing or maintenance quality assurance program. This approach is detailed in NCHRP Web-Only Document 8 (14). LOS values are typically calculated by maintenance program (e.g., roadside, drainage, vegetation, etc.) and can be reported either on a letter scale (A through F) or numeric scale. Four resources detailed in Appendix A describe the state of the practice in asset management data collection for safety features and facilities. NCHRP Synthesis 371 (15) details data available for signals, lighting, signs, pavement markings, culverts (treated as structures in this report), and sidewalks. The 2006 white paper âThe Use of Highway Maintenance Management Systems in Statewide Highway Agenciesâ describes a survey of maintenance management data and systems performed in 2005 (16). Further, the report on the recent Transportation Asset Management Domestic Scanning Tour conducted as part of NCHRP Project 20-68 details best practices examples for asset data collection in a number of agencies (17). The Asset Management Data Collection Guide (13) provides examples of best practices for a number of assets, and recommends specific inventory and condition data items to collect for assets including signs, guardrails, and pavement markings. Also, this guide presents a set of criteria for deter- mining what data to collect for an agencyâs assets that is par- ticularly applicable to assets classified here as safety features. Mobility Data The HPMS is the major source of mobility data for the IHS, particularly at a national level. It contains average annual daily traffic (AADT) data for all segments and additional functional data needed for modeling mobility-related measures. The FHWA Highway Economic Requirements SystemâState Ver- sion (HERS-ST) takes HPMS data as an input and can be used to model mobility measures. All DOTs have developed some form of database for tracking highway inventory and traffic data in addition to what is required for HPMS reporting. There are no standards for how this additional information is col- lected or stored. NCHRP Web-Only Document 97 (18) details best practices in collecting IHS mobility and operations data. Safety Data Fatality Analysis Reporting System (FARS) NHTSA National Center for Statistical Analysis maintains the Fatality Analysis Reporting System (FARS). Established in 1975, FARS contains data describing all fatal accidents occur- ring on public roads in the United States. Data included in the FARS database are collected by state and local police officers, coroners, emergency medical services, and state motor vehi- cle administrations. Data describing approximately 40,000 fatal accidents are added to the FARS annually. Highway Safety Information System (HSIS) The FHWAâs Highway Safety Information System (HSIS) is a multistate safety database that contains accident, roadway inventory, and traffic volume data. The University of North Carolina Highway Safety Research Center and the FHWA maintain the database. The participating statesâCalifornia, Illinois, Maine, Michigan, Minnesota, North Carolina, Utah, and Washingtonâwere selected based on the quality of their data, the range of data available, and their ability to merge data from the various sources. HSIS is used to study current highway safety issues, direct research efforts, and evaluate the effectiveness of accident countermeasures. State Crash Data Systems Crash data are collected by police officers at the scene of crashes. Every state has a unique system for collecting crash 26
data based on police accident reports (PAR), and an admin- istrative structure for controlling the data. Most PAR data collected by states are similar in nature. Different data schemas have been developed for crash reporting, but there is no national standard for these data. The TransXML schema is one example developed through NCHRP Project 20-64. As described in Appendix A, this schema is based on the Model Minimum Uniform Crash Criteria (MMUC). NHTSAâs National Center for Statistical Analysis maintains a sample of state crash data through its National Accident Sampling System/General Estimates System (NASS/GES). The NASS/GES contains an annual sample of police-reported traf- fic crashes in the United States, which is used to estimate the number of U.S. traffic accidents and their injury outcomes. Unlike the FARS, which only contains data describing crashes involving fatalities, the NASS/GES contains data on both fatal and nonfatal accidents. The NASS/GES was created in 1988. More than 50,000 accidents are recorded in this database each year. State Highway Safety Improvement Plans (HSIP) Each state is required to develop a HSIP on an annual basis detailing its use of Federal transportation safety funding. At a minimum, a stateâs HSIP has sections on planning (including information on data collection and maintenance, identification of hazardous locations and elements, and project priorities), implementation of planned safety improvement and evalua- tion of completed improvements. The HSIP is a useful source of data for information on state-level safety improvement needs and trends. Environmental Data There exists little environmental data available, particu- larly on a consistent basis from agency to agency, for support- ing the Interstate Asset Management Framework. NCHRP Web-Only Document 103 (19) details data sources and analy- tical tools used by transportation agencies for environmental management. Risk-Related Data Supporting the risk management approach described in Chapter 3 requires information on risks to transportation infrastructure and information for predicting consequences, as well as the detailed asset data described in previous sub- sections. Information on the risks to transportation infra- structure is particularly sparse. The NBI contains data on certain types of risks to structures, through detailing bridge design types and materials, through specifying whether or not a bridge has fracture critical details, and by storing information on bridgesâ vulnerability to scour. The review found no other data sources available on a consistent basis for use in assessing risk to infrastructure. However, in many cases individual IHS owners have performed some form of assessment of the risks they perceive to be greatest (e.g., of seismic vulnerability in California, likelihood of flooding in the event of a hurricane in Gulf Coast states, etc.). In theory the National Asset Database provides a comprehensive listing of critical infrastructures and assets. However, both the Department of Homeland Security and Congressional Resource Service have reported significant problems with this database, particularly with regard to consistency in classification of âcritical assetsâ from state to state (20) (21). For predicting consequences of risks, many state DOTs and Metropolitan Planning Organizations (MPOs) do have statewide or regional travel demand models that can be used to model the disruption in the event of system failure. Currently the Interstate 95 (I-95) Corridor Coalition is developing an integrated travel demand model for the states along I-95 that, once developed, could be used for consequence modeling. For bridges, the NBI specifies the traffic on and under each IHS bridge, as well as the detour distance around the bridge. This information can be used to approximate consequences for bridge-related risks. 4.3 Analytical Tools This section describes the evaluation of the available ana- lytical tools for supporting the Interstate Asset Management Framework. The review described in Appendix A yielded a large number of examples of analytical tools for supporting asset management. The existing tools have been organized by the system types described already: investment analysis systems; management systems; needs and project evaluation; risk assessment; and results monitoring. Specific systems available in the public domain are noted. Otherwise, the text describes general functionality available in the existing agency and commercial off-the-shelf (COTS) systems. Investment Analysis The review included several examples of investment analy- sis tools. FHWAâs HERS-ST, the state version of the Federal HERS program, uses HPMS data to predict highway invest- ment needs and measures. The system simulates both pave- ment preservation and highway capacity expansions needs. FHWA itself uses a Federal version of HERS for developing its biennial report on the conditions and performance of U.S. highways, bridges, and transit (the C&P Report). HERS-ST is notable in that it is one of few systems that generates needs for new capacity. This functionality is in contrast to that provided in other tools, which typically can evaluate a set of project or 27
network improvements, but lack functionality for needs gen- eration. Further, HERS-ST projects a wide range of perfor- mance measures, including selected preservation, mobility and accessibility, safety, and environmental measures. Issues agencies encounter in using HERS-ST include: ⢠The system is designed to model HPMS sample sections, using the expansion factor to approximate needs for nonsample sections. One can either run the system using sample section data, or supplement the HPMS data, quan- tifying the additional data items required for sample sections for universal sections, as well. For high-level analysis the use of sample section data is completely appropriate, but for more detailed analyses one may need to supplement the sample section data. ⢠The system has relatively limited modeling of pavement conditions, given the only measures of pavement condition available in the HPMS are IRI and PSR. FHWA plans to revise the pavement models in HERS in conjunction with the planned changes in the HPMS. ⢠The system relies on future traffic predictions in the HPMS. Frequently these values are populated using an overall adjustment factor, rather than through use of a travel demand model. ⢠Indiana DOT (INDOT) has developed an approach for using HERS-ST to support its planning process, accounting for all of the issues described above. The agency models each of its pavement sections as a sample section (supple- menting the HPMS data with additional data available from its road inventory database), and defines specific improvements in HERS-ST, with traffic data generated from the agencyâs statewide travel demand model, where a specific improvement has been scoped. Also, INDOT disables the pavement deterioration models in HERS-ST, instead using its own, more detailed PMS for modeling pavement conditions. The World Road Association (PIARC) offers HDM-4 for analysis of roadway management and investment alternatives. The system has been used internationally to evaluate road proj- ects, budget scenarios, and roadway policy options. HDM-4 has functionality similar to HERS-ST, with a more detailed set of pavement models. However, the system does not use HPMS data as an input, and has not been implemented in the U.S. FHWAâs National Bridge Investment Analysis System (NBIAS) is designed for modeling national-level bridge investment needs. FHWA uses NBIAS in conjunction with HERS when preparing the C&P Report, and has recently made a number of enhancements to the system to facilitate state use. The system uses a modeling approach originally adapted from the Pontis BMS to predict bridge preservation and functional improvement investment needs. FHWA has performed work on NBIAS to modify its models to predict benefits in as similar as possible a manner to HERS, and to populate the system with default data, including default user cost models, agency cost adjustment factors for each state, and bridge deterioration models for each HPMS climate zone. The system takes NBI data as its input, but also can import element-level data where available. The Multi-Objective Optimization System (MOOS) network-level model is a spreadsheet tool for bridge investment analysis detailed in NCHRP Report 590 (7). The system uses data on work candidates generated separately to project future conditions and performance given performance and/or budget constraints and objectives. The tool supports use of a multi-objective approach, but requires extensive data to run, to be specified for each individual bridge using the MOOS bridge-level model. The California DOT (Caltrans) has adapted concepts from NCHRP Report 590 in its recent revisions to the process for updating the Caltrans Strategic Highway Operations and Protection Plan (SHOPP). The bridge analysis performed for the SHOPP update process considers needs for bridge rehabilitation, guardrail improvements, seismic retrofit, scour mitigation, and functional improvement using a multi- objective approach. The process is supported by a number of tools, including the Pontis BMS and AssetManager NT described below. AssetManager NT, developed through NCHRP Project 20-57 (8) and now released through AASHTO, is an investment analysis tool designed to integrate data from other investment analysis and management systems. It takes analysis results generated by systems such as HERS-ST, NBIAS, and agency management systems as inputs, and uses this information to show performance measure results over time for different funding scenarios. The system includes spreadsheet ârobotsâ for automatically running HERS-ST and the Pontis BMS to generate system input. The system is unique in its ability to integrate analysis results from different sources in one display. However, COTS management system vendors have built upon their existing systems to provide similar functionality, where an agency uses the vendorâs system for all of its analysis, as described later. In addition to the systems described, a number of agencies have developed their own investment analysis approaches, fre- quently using spreadsheets, to support the process. The Alaska Department of Transportation and Public Facilities and Michi- gan DOT are two examples of agencies that have developed spreadsheet approaches. Other agencies have developed their own cross-asset analysis systems. The Ministry of Transporta- tion of Ontario (MTO) has developed a prototype Executive Support System (ESS) for cross-asset analysis. The system includes functionality similar to AssetManager, as well as a pre- processor for using work candidate and asset inventory to sim- ulate future conditions and performance. The New Brunswick 28
Department of Transportation has recently adapted the Remsoft Spatial Planning System (RSPS) to perform strategic analysis of its pavement and bridge investment needs. RSPS is a suite of tools originally designed for developing long-term forest management plans. RSPS includes the Woodstock modeling system, used to formulate optimization models, which are then solved using a separate linear program (LP) solver. Management Systems Pavement. PMS are used to collect, store and retrieve pavement inventory and condition data. These systems are used to reduce data, summarize conditions, support develop- ment of pavement treatment rules, model future conditions, and perform analysis of investment needs, and develop stan- dard query reports. All U.S. agencies have some form of PMS, and all use their pavement systems to support HPMS and other reporting. There are a number of commercially avail- able PMS, including systems offered by the Army Corps of Engineers, Deighton, Agile Assets, and Stantec. There are sev- eral additional state-specific systems in use. Typically PMSs allow for specification of multiple mea- sures of distress, including roughness, rutting, cracking/ faulting, and other measures. Most systems support flexible specification deterioration models for each measure of distress and decision rules, allowing for a range of treatments triggered by different distress measures. The available systems use dif- ferent approaches to project-level analysis for recommended specific treatments over time for given pavement section and network-level analysis for predicting overall conditions over time given a budget constraint and/or other constraints. In part because the available systems are so flexible in their design, a challenge agencies face in using the available systems is in determining what distress measures to model, how to predict deterioration over time, and what treatments should be triggered at different condition levels. Increasingly, agen- cies are using the concept of Remaining Service Live (RSL) for developing pavement deterioration models and treatments. RSL typically is defined as the life of an asset from the time it is completed for use until application of the first significant rehabilitation or reconstruction of the asset. The placement of a structural HMA overlay (versus a thin overlay) or recon- struction signals the end of a pavementâs serviceable life; the application of minor maintenance treatments is not considered significant enough to indicate the end of service life. In con- junction with the concept of RSL, survival analysis can be used to determine the mean service life for a family of pavement sections or other assets with similar characteristics (e.g., design, usage, climate). Survival analysis utilizes information on assets still in service and assets that have either failed or been rehabilitated or reconstructed to predict a time-dependent survival probability that can be used to establish a pavement deterioration model. A feature of the more advanced commercially available systems is that these systems can model other assets besides pavement, provided an agency has the data and models to support such an analysis, and subject to a number of modeling assumptions. Also, the systems from Deighton and Agile Assets offer the ability to view analysis results across assets modeled in the system. Utah DOT has successfully used the Deightonâs dTIMS CT system to perform analysis across assets, including pavement, structures, safety, mobility, and maintenance needs. Bridge. BMS are used for storing bridge inventory and in- spection data, supporting reporting, modeling bridge condi- tions, recommending work, and other functions. Nearly all U.S. agencies have a BMS to support collection of bridge in- spection data. Many, though substantially fewer, use their BMS for bridge modeling. Commercially available bridge man- agement systems that have been put into production in the United States and Canada include AASHTOâs Pontis, Delcanâs BRIDGIT, and Stantecâs Ontario Bridge Management System (OBMS). All of these systems store data and model bridges at an element or component level, going beyond characterizing conditions at a finer level than the deck, superstructure, and substructure ratings in the NBI. In addition to these systems, many agencies have devel- oped their own systems for storing bridge inventory and in- spection data and/or modeling bridge conditions. Agencies such as Alabama DOT and New York State DOT developed BMS prior to the release of Pontis and continue to use their agency-specific systems. Other agencies have developed bridge inventory and inspection systems, while using Pontis for any modeling needs, or use Pontis with extensive customizations. For instance, Caltrans has developed the Structures Mainte- nance and Reports Transmittal (SMART) system using a Pontis database and custom tables, with a custom user inter- face. Florida DOT has made extensive customizations to Pontis, and has developed a standalone spreadsheet for bridge analysis (incorporated in the MOOS Bridge Level Tool detailed in NCHRP Report 590). Maintenance. Maintenance management systems (MMS) often are used to inventory and characterize conditions of roadside assets besides pavements and bridges, including road shoulders, nonbridge structures, and safety features. Transpor- tation agencies use a number of different tools to support their maintenance management functions. Different approaches to maintenance management systems can be summarized as follows: ⢠Legacy highway MMSs. Several states use MMS that were developed in the 1970s and 80s. These systems are often 29
mainframe or client/server systems that field crews use to enter labor, equipment, and materials usage by activity type. These systems enable maintenance managers to develop maintenance budgets and plans based largely on what work was accomplished in previous years. In the legacy systems, the inventory data are either nonexistent or consist of a rudimentary features inventory. As these legacy systems have been upgraded, many have evolved into inventory- based systems, as described below. ⢠Inventory-based highway MMSs. These systems provide many of the features of the legacy systems, and add more sophisticated approaches for tracking inventory data. A number of commercially available asset management sys- tems fall in this category, including Agile Assetsâ Maintenance Manager, Inforâs Asset Management Suite, CartêGraphâs Management Suite, Exorâs Highways Suite, and the Mainte- nance Activity Tracking System (MATS) jointly developed by the DOTs of Maine, New Hampshire and Vermont. ⢠Nontransportation work management systems. Many large private sector firms that are responsible for some type of asset maintenance use work order systems to plan, schedule, and track maintenance activities. One example of this type of system is IBMâs Maximo. Work orders can be generated by Maximo automatically based on preven- tive maintenance schedules, or specified by maintenance mangers based on local knowledge. Information associated with work orders can include location, date, activity, per- sonnel, materials, and equipment usage (both planned and actual). Although these systems are not designed specifically to support the public transportation sector, they can be used by transportation agencies wishing to track maintenance work orders. ⢠Enterprise resource planning (ERP) systems. ERP systems are enterprise-oriented products that offer a suite of inte- grated modules covering financial and operations man- agement. SAP is a common ERP system. SAP has several financial modules including General Ledger, Payables and Receivables, Controlling (budgeting), and Asset Account- ing. Its also includes four modules that may be applicable to the maintenance management function: Plant Mainte- nance, Service Management, Materials Management, and the Project System. A cross-application timesheet module (CAT) also is available through SAP, which interfaces to the financial, logistics, and human resource families of products. In addition, a business information warehouse product provides data warehouse capabilities, allowing linkages between SAP and external data. A number of state DOTs are implementing ERP systems to support maintenance man- agement, including Pennsylvania, Idaho, and Colorado. ⢠Performance-based budgeting systems. A number of DOTs have developed spreadsheets or systems for supporting a performance-based maintenance budgeting approach, as detailed in NCHRP Web Document 8 (14) and described previously. This approach requires agencies to conduct physical inspections on a sample of the network and model the relationship between expenditure and the resulting condition. The analytical functionality required to support this type of budgeting is not widely available in the types of systems described above. Therefore, agencies pursing this approach often develop standalone tools that draw infor- mation from their MMS. Other. Management systems have been developed for a variety of other assets, including but not limited to signs, cul- verts, tunnels, ITS equipment, and facilities. Generally, but by no means exclusively, these systems focus on supporting col- lecting and reporting basic inventory and inspection data. There are many best practice examples for these systems, but little or no standardization between them concerning data requirements and functionality. Regarding other structures besides bridges, there are varying practices in use for managing these. Most agencies do store data on culverts that are at least 6.1 meters (approximately 20 feet) long, as these are included in the NBI. In many cases, agencies store data on shorter culverts, tunnels, and other structures in their BMS, as well. This approach, where used, facilitates use of BMS functionality for predicting future conditions and performance. However, often data on other structures is stored separately, or simply not stored in an elec- tronic format. New Yorkâs Metropolitan Transportation AuthorityâBridges and Tunnels uses its Capital Programming System to store detailed data on conditions and predicted future needs for nine major bridges and tunnels in and around New York City. FHWA has issued guidance on tunnel inspec- tion procedures and developed a Tunnel Management System that demonstrates collection of tunnel inventory and inspection data. Also as noted in Section 4.2, FHWA has issued guidance on sign, light, and traffic signal support structures recommend- ing inspection data be collected for these structures using the element-level approach established in Pontis. Needs and Project Evaluation Many of the analytical tools developed for asset manage- ment fall into this category. Typically these tools are used to analyze a user-defined scenario or project, calculating costs, performance measures, or other parameters used to support the decision-making process. Surface Transportation Efficiency Analysis Model (STEAM) and ITS Deployment Analysis System (IDAS) are tools for evaluating network performance for a specified set of trans- portation improvements. Both systems require information on the improvements to be evaluated, and output from a travel demand model. STEAM uses this information to 30
calculate a wide range of measures of transportation and environmental performance for multimodal improvements. IDAS is designed to evaluate the benefits of more than 60 types of ITS investments. A number of tools are available to evaluate project-level costs and benefits. BCA.Net is a web-based tool developed by FHWA for highway benefit-cost analysis. The system predicts costs and benefits for a range of different highway projects, and includes functionality for sensitivity analysis. BCA.Net builds upon the models developed for older benefit-cost analysis systems, most notably MicroBENCOST. A key feature of the system is that because it is web-based, an agency user can run the system without installing any software other than an Internet browser. StratBENCOST is another system that uses MicroBENCOST models, but applies them to multiple project alternatives. TransDec is a tool for multimodal, multi- objective project analysis. It helps prioritize projects or project alternatives considering multiple objectives and measures. AssetManager PT is a spreadsheet tool for project analysis. It helps prioritize projects given information on the costs, benefits, and performance impacts of a set of projects. Several available tools are spreadsheet tools intended for detailed lifecycle cost analysis (LCCA) for pavement or bridge projects. RealCost is FHWAâs current tool for pavement LCCA, replacing a number of earlier LCCA tools. The system includes a number of advanced features, including models for predicting user costs due to construction, and probabilistic modeling of analysis inputs using Monte Carlo simulation. BLCCA is a lifecycle cost analysis tool designed for bridge LCCA. It is designed to use data from systems such as Pontis. Like RealCost, it includes probabilistic modeling of input parameters. The MOOS bridge level model predicts bridge life cycle costs as well. It differs from BLCCA in a number of respects. Specifically, MOOS includes consideration of multi- ple objectives (e.g., minimizing costs, maximizing condition, or maximizing overall utility) and incorporates preservation models from Pontis. However, it lacks features of BLCCA such as modeling of uncertainty in input parameters. Many agencies have developed their own tools for project- level analysis. Often these tools are used for initial screen- ing of candidate projects. For instance, Caltrans uses the spreadsheet tool Cal B/C for project analysis. Wisconsin DOT developed a spreadsheet for calculating project-level user benefits as part of its Mobility Project Prioritization Process. The Ministry of Transportation of Ontario recently developed a prototype spreadsheet tool, the Priority Economic Analysis Tool (PEAT), for project-level benefit-cost analysis, adapting models from HERS-ST and other systems. South Carolina DOT has developed the Interactive Interstate Management System (IIMS) for ranking interchange needs, and calculating the costs and benefits of user-defined inter- change improvements. Risk Assessment There are relatively few tools available for assessing risks of system failure for IHS assets. Appendix A provides a number of examples where risks have been characterized and prioritized using either calculations of economic losses (e.g., through creating risk scenarios and using a travel demand model to estimate consequences) or thresholding approaches. To the extent that tools have been implemented for risk assessment, they typically have been used for assessing risks to structures. As described earlier, Caltrans recently implemented a multi-objective needs analysis approach supported using AssetManager that includes consideration of risks to structures, adapting the approach detailed in NCHRP Report 590. Recently NCHRP published the Disruption Impact Esti- mating Tool (DIETT) for prioritizing risks to transportation choke points such as bridges and tunnels (22). It includes an Access tool for filtering choke points and a spreadsheet for prioritizing choke points based on potential economic losses if the choke point were closed. The system is intended to be used in conjunction with the Science Applications International Corp. (SAIC) Consequences Assessment Tool Set (CATS). Lloyds Register has introduced the Arivu system for pri- oritizing maintenance actions for a range of assets such as bridges, drainage structures and lighting based on risk. This tool has been implemented for transportation agencies in the United Kingdom. The MOOS bridge level model described earlier is intended as a project analysis tool, in that it considers a range of differ- ent projects and is not limited to risk mitigation. However, the tool can be used to prioritize risk mitigation for bridges by focusing on project types intended to mitigate risks of failure (e.g., seismic risks). Results Monitoring Transportation agencies typically use their pavement, bridge, and maintenance management systems, described above, for monitoring asset conditions over time. All agencies have highway inventory systems and geographic information systems (GIS) for storing geospatial data. These systems are integrated to greatly varying degrees, with some agencies integrating most or all of their management and inventory systems with each other and their GIS, while others have minimal integration. For monitoring project delivery all agencies have additional systems for construction and project management. A number of agencies use AASHTOâs Trns*Port suite to support precon- struction and construction management. Trns*Port includes 14 separate modules, with functionality in areas such as con- struction cost estimation, letting and awards, construction administration, bidding, and construction management. 31
Summary of the Tool Evaluation The analytical tools identified through the review were evaluated to determine the degree to which they support the Interstate Asset Management Framework. Table 4.1 lists sys- tems and tools currently maintained, support at least one step in the framework, and are available in the public domain and/or through NCHRP research, from AASHTO or from FHWA. 4.4 Guidance on Data and Tools for IHS Asset Management This section provides guidance on using available data and tools for supporting the Interstate Asset Management Frame- work. Guidance is provided for each asset category, as well as for the areas of mobility, safety, environment, and integrating results. Additionally, Table 4.1 lists specific analytical tools that can be used to support asset management analyses consistent with the guidance presented below. A general issue in considering the data and tools needed for asset management is that of degree of coverage. While it is generally agreed that good data supporting quantification of qualitative policies, goals, and objectives often are required to properly manage highway assets, and agreement that asset man- agement decisions should be supported by data, there is debate over what constitutes good data and exactly how that data should be used. Generally, the more complete, accurate, and timely the data, the more expensive it is to collect. In determin- ing the extent to which data should be collected for the Inter- 32 Table 4.1. Analytical tool summary. Tool System Type Available From Notes AssetManager NT Investment analysis AASHTO Integrates investment analysis results from multiple sources AssetManager PT Needs and Project Evaluation AASHTO Prioritizes projects based on user-specified measures BCA.Net Needs and Project Evaluation FHWA Performs benefit/cost analysis for highway improvements BLCCA Needs and Project Evaluation NCHRP Bridge preservation life cycle cost analysis DIETT Risk Assessment NCHRP Prioritizes risks to transportation choke points HDM-4 Investment Analysis McTrans, Presses de lâENPC (Paris) Simulates highway investment needs, condition and performance HERS-ST Investment Analysis FHWA Simulates highway investment needs, condition and performance IDAS Needs and Project Evaluation McTrans and PCTrans Evaluates network impact of ITS improvements MOOS Bridge Level Model Needs and Project Evaluation NCHRP Assist in developing bridge-level strategies using data from Pontis. Also can be used to prioritize investments to mitigate bridge risks MOOS Network Level Model Investment Analysis NCHRP Uses data from the bridge-level model to perform multi-objective analysis NBIAS Investment Analysis FHWA Simulates bridge investment needs, condition and performance PONTIS Management System AASHTO BMS licensed by most U.S. state DOTs REALCOST Needs and Project Evaluation FHWA Performs benefit/cost analysis for pavement projects STEAM Needs and Project Evaluation FHWA Evaluates network impact of multimodal improvements TRNS*PORT Results Monitoring AASHTO Supports preconstruction, contracting, and construction management
state Asset Management Framework, it is important to weigh the cost of data collection against the cost of poor decisions from incomplete or inaccurate data. Also, it is important to consider the possible use of the data for supporting asset man- agement, and try to avoid the situation whereby an IHS owner collects too little data to support asset management decisions or, alternatively, more data than can practically be put to use in a systematic manner. The guidance below has been developed considering the need to find a balance between these extremes. Roadways. IHS owners should collect roadway inventory and inspection data consistent with HPMS reporting require- ments on an annual basis. All IHS sections should be treated as HPMS sample sections, implying the full set of HPMS data items should be quantified for each section. Further, IHS owners should collect additional measures of pavement condi- tion consistent with the requirements of COTS pavement man- agement systems and expected future HPMS requirements. Agencies should use a pavement management system for assessing current conditions, monitoring performance trends, setting performance and budget targets, and identifying candidate projects when updating the capital plan for pave- ments. Existing COTS and agency-specific systems that support measures of roughness, cracking, faulting and rutting pro- vide sufficient support for implementing the Interstate Asset Management Framework. An RSL approach is recommended for modeling pavement needs. Where possible, survival analy- sis should be used to establish pavement deterioration models considering relevant performance risk. HERS-ST should be run to validate the results obtained from an agencyâs PMS, or to act as a substitute for assessing conditions and setting per- formance and budget targets where an agency lacks a PMS. Structures. IHS owners should collect inventory and con- dition data on all structures, preferably on a two- to four-year basis. This guidance applies to bridges and other nonbridge structuresâtunnels, culverts/drainage structures, noise barrier walls, retaining walls, overhead sign structures, and high mast light polesâas all of these structures have the potential to fail catastrophically, and thus lead to system failure (closure of a portion of the IHS for some period) and possibly loss of life. Bridge data are already collected for IHS bridges consistent with NBI requirements. In addition to collecting NBI data, IHS owners should collect inventory and condition data for all structures at the element or subcomponent level. NBI data is collected at the component levelâdeck, superstructure, and substructure. These data provide an overall picture of bridge condition, but are not sufficient for identifying specific bridge preservation projects such as painting and repairs to elements such as bearings or joints. Element level data pro- vides the finer level of detailed condition required to identify candidate projects. IHS owners should use a BMS for assessing current condi- tions, setting performance and budget targets, and identifying candidate projects when updating the capital plan for struc- tures. Pontis and other agency-specific systems that support analysis of element or component-level conditions provide a strong basis for implementing the Interstate Asset Manage- ment Framework. NBIAS provides sufficient support for as- sessing current conditions and setting performance and budget targets for bridges. Spreadsheet analyses, preferably calibrated using a BMS or NBIAS, may be used to support setting per- formance and budget targets. Safety Features and Facilities. IHS owners should collect inventory and condition data for all assets listed in Table 2.1, including safety features, facilities, and shoulders, sufficient for estimating the asset extent and the percent of the extent functioning as intended. A maintenance manage- ment system, which supports a maintenance quality-assurance approach for assessing current conditions and setting perfor- mance and budget targets by asset category, provides support for IHS asset management. Alternatively, an RSL approach can be used, particularly for discrete assets with known construc- tion dates, such as facilities. The Asset Management Data Collection Guide (13) recommends specific data items to collect for selected asset types, including signs, pavement markings, and guardrails. Also, this guide recommends cri- teria for determining what data to collect as part of an asset management data collection and is a particularly valuable reference for determining what data to collect for assets classified here as safety features. Mobility and Safety. IHS owners typically have basic data required for mobility and safety. The mobility-related data items required for the Interstate Asset Management Frame- work are collected for HPMS sample segments. IHS owners should collect HPMS sample data for their entire IHS. IHS owners already have access to counts of crashes and fatalities. IHS owners should use HERS-ST to help set targets and predict future values for mobility and safety measures. Agency travel demand models, where available, can be used in con- junction with HERS-ST to more accurately assess current conditions and provide better estimates of future traffic. Environment. For this area the primary challenge is in defining a set of environmental performance measures, as described in Chapter 5. At a minimum, IHS owners should document environmental goals and commitments for IHS assets, and track the degree to which they meet these commit- ments on an annual basis. The prototype Environmental Information Management System developed through NCHRP Project 25-23 (2) (19) can be used to support an agencyâs com- mitment tracking process. 33
Risk Assessment. The risk assessment approach described in Chapter 3 should be used to prioritize risk mitigation investments for IHS structures, at a minimum. Existing BMS do model performance risks, but do not consider needs related to potential system failures from scour, seismic retrofit, fracture critical bridges or other risks. Realistically, much of the analysis of risks of system failure must be handled outside of existing systems. DIETT or a multi-objective approach such as that developed by Caltrans and supported using AssetManager can be used to characterize risks to structures. Additional Guidance. Agencies should integrate data from their management systems for setting performance and budget targets across asset and investment types. In performing this analysis, agencies should compare predicted performance across the full range of asset and investment types for multi- ple investment scenarios. AssetManager NT, other emerging COTS systems and/or agency-specific systems can be used to support this analysis. Projects for implementing safety or capacity improvements should be evaluated using BCA.Net or comparable approaches. For major capacity expansion projects, ITS investments, or other candidate projects expected to have significant network effects, network models such as IDAS or STEAM should be used for candidate evaluation. Where analytical tools that explicitly account for uncer- tainty in project costs, outcomes and other parameters are unavailable, sensitivity analysis should be used to test the analysis results for variation in key modeling assumptions. A summary of key assumptions and the sensitivity of the results to uncertainty should be prepared for each tool used for analysis. 4.5 Gap Assessment The research team found the following gaps in the avail- ability of data and tools for supporting the Interstate Asset Management Framework: ⢠Regarding other assets besides pavement and bridges, there is a very large gap between the data and tools needed to support asset management concepts and the reality on the ground. NCHRP Synthesis 371 painstakingly documents this issue for a selected set of assets. Stated simply, many IHS owners do not have ready access to inventory and con- dition data needed to describe their asset inventories or summarize even rudimentary measures of physical condi- tion. The existence of this gap suggests the following with regard to an Interstate Asset Management Framework: â It lends credence to the concept that an agency should focus improving its asset management approach start- ing with the assets on its highest priority network. If there is value to be gained in or a case that needs to be made for improving business processes, it makes most sense to start with the IHS. â It points to the likelihood of challenges in implement- ing a new framework, even one that does focus solely on an agencyâs highest priority network. This issue has been duly noted in development of the implementation guidance in Chapter 6. â It makes the case that the Interstate Asset Management Framework cannot simultaneously be immediately ap- plicable to all IHS assets and push the technical envelope for other assets besides pavement and bridges. To pro- vide a framework that is first and foremost of immediate practical value, this report makes a distinction between âcoreâ and âcomprehensiveâ measures required for an Interstate Asset Management Framework in Chapter 5, and recommends use of a âpercent functioning as intendedâ as a basic measure for characterizing other assets besides pavement and structures. ⢠Improvements to the HPMS, consistent with the planned improvements to this system, will provide better data for IHS assets. Specific improvements for enabling improved IHS asset management include treating all IHS sections as HPMS sample sections and supplementing the pavement measures in the existing HPMS with measures of rutting, cracking, and faulting. ⢠The RSL approach recommended for pavement analysis can be implemented using existing management systems. Unfortunately, the approach requires data and models that are not yet supported in FHWA systems such as HERS-ST. Some COTS pavement management systems can support an RSL approach if appropriately configured. ⢠Although there are a number of tools available for ana- lyzing user-defined improvements, there are few tools for high-level investment analysis, particularly across asset and investment types. For instance, HERS-ST and HDM-4 were the only systems identified through the review that simulate generation of new highway capacity. AssetManager NT was the only system identified in the public domain for combining investment analysis results across asset and investment classes. ⢠The risk management approach described in Chapter 3 assumes that certain risks should be handled programmat- ically using management systems. Some risks are handled in such a fashion, but many are not. The review included examples of approaches to handling risk programmatically, particularly with regard to structures, but more work is needed to introduce these approaches in COTS systems. ⢠Few tools were identified that can be used to support analysis of risks of system failure in support of the approach described in Chapter 3, though many quantitative tech- niques do exist for performing risk assessment. Further 34
research is needed to develop tools and approaches for simplifying the process of prioritizing investments in risk mitigation. ⢠The AssetManager tools are valuable tools for supporting the Interstate Asset Management Framework, but realisti- cally will require further enhancement to provide compre- hensive support. AssetManager NT can be used to inte- grate analysis results for multiple asset and investment types, but can work with data from no more than four sources at a time. AssetManager PT is a valuable tool for project pri- oritization, but is implemented as a spreadsheet prototype and not integrated with AssetManager NT. 35
