Asset management relies on good data and tools to guide investment decision-making. Indeed many agencies have a wealth of data about their infrastructure, but are challenged to leverage information to make better decisions. Information management is the discipline that delivers foundational capabilities for asset management results. Asset management systems connect inventory and condition with analytical capabilities to predict asset condition under various funding and action scenarios. Other information and tools allow for the ability to relate asset actions across assets and with other transportation areas, such as safety and mobility. This section provides a brief overview of information management and how it supports the implementation of the concepts discussed in this guide. More detail can be found in subsequent Chapters. Each section has been crafted to illustrate how data, information and analysis can be leveraged to create better outcomes, and enable agencies to improve how they deliver services.

Data Collection Standards and Processes

Standards and processes for data collection are two important aspects of integrating asset management practices across the agency. Collecting a standard set of data elements for each asset ensures consistency, and better enables analysis and reporting across assets. Standard data elements can include a unique asset identifier, designated asset category and asset type. Geospatial referencing standards are also important. In order to see assets on a map and integrate them spatially, agencies need a standard way to locate them. It is also important to consider the data collection intake process. Before data is collected, agencies should determine if specific data already exists in order to prevent duplication. If the data does not exist and needs to be collected, agencies should consider how new data will integrate with what is available currently. This ensures the data is used in the most effective way possible. Finally, responsibility needs to be assigned to an Asset Data Steward who is responsible for ensuring data standards and processes are followed.

Asset Information Across the Life Cycle

TAM integration also relies on collecting and updating asset information across the life cycle of the asset. It is important to think holistically about the asset life cycle, from the initial design phase and through future maintenance and rehabilitation activities. Technologies and processes are becoming available to extract asset information from design and as-built plans to populate inventories. Many agencies have processes in place to think holistically about assets during the project scoping and design phase.

Agencies face challenges in integrating asset information across the life cycle of the asset, because there is often a disconnect between maintenance activities, planning/ programming and the assets. For example, maintenance divisions may not know about planned projects on particular assets that have been scheduled for repairs. Better linkage between the work an agency is planning for the future, the work they are doing currently and the general condition of the assets is important to cultivate. Maturing agencies are working hard to bridge this gap. Chapter 6 provides more information on updating asset information and connecting with maintenance activities.

Common Set of Asset Management Reporting Processes

Another aspect of information management strategy that can help integrate TAM across an agency is to develop a common set of asset management reporting processes. Many agencies are successfully mapping different types of assets and making this information available on a GIS portal. Typically, these portals have different layers for each asset. This is one example of a consistent process for sharing information about assets.

As agencies seek to make cross-asset tradeoffs and scope projects considering multiple types of needs, having a common set of reporting processes and consistency across different tools becomes even more important. An example of the challenge agencies face in doing this is seen in the TAMP development process. Developing a TAMP requires information about the needs of different assets. This information must then be communicated with a common set of definitions and combined with funding information. Practitioners have to be aware of the funding and cost assumptions used in every tool before they can report numbers in the TAMP. For instance, the pavement management system might only include costs for the pavement work, whereas other planning tools might incorporate guardrail costs and other costs related to the work. Different tools might also use different assumptions for inflation. In order to bring all this information together in a TAMP, agencies need to make sure their reporting and assumptions are consistent.

TAMP Scope

A highway agency focused on complying with Federal requirements will typically focus on including its NHS pavements and bridges in its TAMP. While these assets make up the greatest portion of a typical state highway agency, an agency may wish to include additional assets in its TAMP. Also, the agency may wish to extend the network scope of the TAMP. In updating a TAMP with NHS pavement and bridges, an agency may include other assets, such as drainage assets, traffic and safety features, or the agency may wish to include all of the assets it owns.

For transit TAMPs, the initial focus is on revenue vehicles, facilities and infrastructure, as these are the assets that require the greatest investment. An agency may wish to expand its TAMP to include additional assets that are important to the systems, albeit less costly, such as bus shelters and signage.

TAM Webinar #55 - TAM Tools Miniseries 02: Management Systems

After evaluating the priority of asset classes, asset stewards need to understand the intended functions, potential failure possibilities, available maintenance options, and the consequences of failure for each asset class. This information is typically dispersed across various areas or business units within an agency. Gathering this information necessitates coordination among different groups within the agency. The ideal approach involves following a framework to establish a performance-based management strategy for ancillary assets, identifying the optimal data elements for collection, and selecting the most suitable data collection techniques for each asset class.

The Handbook (FHWA-HIF-19-006) offers a potential framework of interconnected processes that can be tailored to an agency's specific requirements. The process for developing a performance-based management strategy employs the Reliability-Centered Maintenance (RCM) approach, which is elaborated upon in Chapter 4. RCM utilizes a series of risk-based questions to assist agencies in identifying the most effective and efficient management strategies. The three primary components of an RCM program pertinent to the life-cycle planning of ancillary highway assets are condition-based maintenance, interval-based maintenance, and reactive maintenance. This is illustrated in Figure 2.12, as adapted in the Handbook from NASA (2008. Reliability-Centered Maintenance Guide for Facilities and Collateral Equipment. National Aeronautics and Space Administration, Washington, DC). A decision tree can be used to establish an appropriate management and maintenance approach for each ancillary asset class to inform data requirements. This is also illustrated in Figure 4.5 of Chapter 4.

• Condition-based maintenance includes predictive maintenance and real-time monitoring. Inspections note current capital and maintenance interventions as well as current state to be considered by maintenance teams and inputted into predictive models.
• Interval-based maintenance is conducted independently of the asset's condition and involves performing inspections or replacements at predetermined intervals.
• Reactive maintenance assumes that failure is low risk to operations and where there are no practical monitoring approaches and/or regular deterioration or failure patterns. Repairs are made after the failure. Table 2.4 presents a summary of the applicability of condition, interval, or reactive maintenance for each of the asset classes.

Table 2.4 Typical Maintenance Approaches by Asset Class

* - Preferred for landscaping, access ramps, and bike paths

** - Feasible for landscaping, access ramps, and bike baths

Data-driven decisions depend on asset data to guide effective investment choices. Effective data collection practices allow an agency to execute strategic RCM maintenance and streamline work order distribution. With accurate data, metrics can be derived, and performance measures compared to assess the effectiveness of a maintenance strategy and identify areas for improvement. Ancillary asset data can be categorized as either essential or desirable, depending on the management approach. Regardless of the data type, it is crucial that the data be complete and reliable. Generally, the RCM process required the following essential data for effective management.

Table 2.5 – Essential Data by Maintenance Approaches for Ancillary Assets

There are various strategies and technologies that support data acquisition that can be found in Chapter 7 and in the Handbook for Ancillary assets (FHWA-HIF-19-006). Other desirable data can augment decision-making by:

• Providing additional clarity and accuracy to the essential data collected.
• Supporting different departments within an agency.
• Assisting in generating accurate work orders.
• Helping manage asset risks.
• Tracking the asset’s full life cycle to make informed decisions.

The above list is not all-inclusive but provides clear examples of reasons why additional data collected in the field could be beneficial to an agency. Each of these items is described in more detail in the handbook. Other desirable data attributes have been referenced elsewhere (HMEP 2013), and may also be considered, including:

• Maintenance intervals.
• Frequency of failure.
• Allocated risk factors.
• Maintenance requirements.
• Engineering specific data

Integrating ancillary asset data into an agency’s overall data management system ensures that decision-making associated with management, maintenance and capital investment is based on the best available information across the organization. An agency will, at the same time, improve transparency and public trust. The following guiding principles distinguish good data management practices from less comprehensive approaches to data management:

• Strategic Plan—Interdepartmental coordination.
• Authoritative Hub—Integrated database and web services.
• Common Data Dictionary—Agency agreement on assets and attributes.
• Business Improvements—Query, analyze, display, and report data.

Data management concepts are discussed in more detail in Chapter 7.

Typically, U.S. transportation agencies perform some or most of their maintenance work internally, and contract out a large portion – if not all – of their capital projects. The line between the types work performed as maintenance and capital projects varies by organization and is often blurred. Agencies can often use maintenance forces in a flexible manner to perform a wide variety of activities, including preservation activities on pavements, bridges and other assets. However, in the near term, an organization’s maintenance resources – staff and equipment, in particular – are fixed. Consequently, the asset owner is challenged to optimize use of these resources to meet immediate needs, such as winter maintenance and incident response, while performing additional work to improve asset conditions wherever possible.

The ability to contract out maintenance work, such as through Indefinite Delivery/Indefinite Quantity (IDIQ) contracts, provides an agency with flexibility in meeting near-term needs. Other approaches for contracting out maintenance work include use of portfolio or program management contracts in which certain operations and maintenance responsibilities for some group of assets is delegated to a contractor over a specified period of time. Section 4.3.3 provides additional details on considerations involved in outsourcing asset maintenance.

Regarding contracting approaches for capital projects, in the U.S., most transportation agencies rely on Design-Bid-Build (DBB) model for delivering their capital programs. With this approach, the project owner designs a project (or contracts for a private sector firm to prepare a design) and solicits bids for project construction following completion of the design. This provides the project owner with control over the process, but can be time consuming and can result in cases where bids for project construction exceed the expected cost developed during design. In recent years, many transportation agencies in the U.S. and abroad have explored improved approaches to work planning and delivery to accelerate completion of needed work, leverage alternative financing approaches and transfer program and project risk.

All of these approaches are intended to reduce the time from initial conception of a project to its completion, and in many cases transfer risks associated with project completion from the public sector to the private sector. As these examples help illustrate, major trends in this area include:

• Group work together by geographic location or type of work to develop fewer, larger, and more easily contracted projects
• Use Design-Build (DB), Design-Build-Finance-Operate-Maintain (DBFOM) and other contracting strategies, wherein a single contract is awarded to design and complete a project, as opposed to separate contracts for design and construction
• Encourage development of Alternative Technical Concepts (ATCs), wherein a contractor proposes an alternative approach to meeting a contract requirement in the bidding phase
• Select contractors earlier in program/project development through use of Construction Manager-General Contractor (CM-GC) arrangements, where a contractor is selected as Construction Manager while design is still underway
• Use IDIQ contracts and other flexible contracts to provide a more efficient mechanism for performing smaller projects
• Incorporate performance-based specifications, time-based incentives and other specifications in contracts to improve project outcomes
• Outsource operations and maintenance of an asset using program or portfolio management contracts.

Both in the U.S. and abroad there are many examples of public agencies making extensive use of alternative contracting strategies, such as Public-Private Partnerships (P3s) and performance-based contracts to speed project delivery and transfer risk.

While alternate strategies for work planning and delivery hold great promise, all of the approaches described here have advantages and disadvantages and carry their own risks. Use of alternative approaches can save taxpayers money and provide improvements more quickly than a traditional model. Success stories typically result from improving the efficiency of the process and incentivizing the use of better technology and methods, but there are also many cautionary examples in which these strategies have failed to achieve cost savings, time savings or risk transfers as desired. Asset owners should consult the separate body of research in this area (referenced at the end of this section) when exploring the use of alternative approaches and carefully weigh the expected return, advantages and disadvantages of whatever delivery approaches they consider.

Differences in Performance and Condition

The terms ‘performance’ and ‘condition’ are often used interchangeably, although they have different meanings in a performance-based environment. The performance of an asset relates to its ‘ability to provide the required level of service to customers3’ while condition is generally considered to mean the observed physical state of an asset, whether or not it impacts its performance. For example, a bridge with scour may continue to perform adequately in the short-term even though it may receive a low National Bridge Inventory (NBI) rating because of the deterioration.

Inventory Information

An asset inventory provides information other than performance data important for estimating the amount of work needed, identifying the location of work in the field and determining characteristics capable of influencing the type of work to be performed. The RCM approach introduced in Chapter 4 can be used to help an agency determine what information is needed to support the management of each type of asset. The asset inventory requirements for those assets managed based on a specified interval for repair, such as pavement markings, is very different than those required for an asset managed using a condition-based approach, such as pavements or bridges. Regardless of how detailed the asset inventory is, it is important an agency establish processes to ensure data quality and keep the inventory current over time.

There are several basic data attributes essential to effectively managing transportation assets, including asset type, quantity and location. Additional information that is important is to differentiate between the types of work to be performed, which may also be added to the inventory, the type of material used to construct the asset, the last time work was performed and factors influencing the use of the asset (e.g. traffic levels, highway functional classification or climatic conditions).

As discussed in Chapter 7, managing asset inventory information using an integrated approach to data management helps promote consistency in asset data across an agency and provides access to help ensure the data is used by decision makers at all levels of the organization. An out-of-date inventory makes it difficult for an agency to estimate work quantities accurately for budgeting purposes.

Condition Information

Asset condition information is used to determine how assets are performing and how performance changes over time. The lack of condition information may lead to premature or unexpected failures with the potential to be very costly, negatively impacting system performance and increasing agency risks. Methods of collecting asset condition information are discussed further in Chapter 7. To ensure that condition information remains current, it is important that the information is updated on a regular basis.

Asset Condition

There are several approaches for assessing asset conditions, each of which is influenced by the type of asset and the resources available to support the process. Typically, an assessment of asset condition involves a method of evaluating the presence of deficiencies and/or deterioration at the time of inspection. The results are used to assign a rating or LOS used to determine the need for maintenance, rehabilitation or replacement now or in the future. Asset condition ratings may also be used to establish rates of deterioration, allowing an agency to forecast future conditions for planning purposes.

Examples of commonly used types of asset condition ratings are listed below.

• A pavement condition index based on the type, amount and severity of distress present, which could be on a 0 to 100 scale, with 100 representing an excellent pavement.
• The National Bridge Inventory (NBI), which assigns a rating between 1 and 9 based on the deterioration present in each element (deck, superstructure, substructure and culvert).
• A LOS rating of A to F for maintenance assets, such as the percent blockage in a culvert or the percent of guardrail not functioning as intended.

Maintaining asset condition information is important for evaluating performance to determine whether improvements are needed to achieve the agency’s strategic objectives. The lack of current condition information, or a lack of confidence in the condition information, makes it difficult to present investment needs to stakeholders with any degree of confidence.

Asset Performance

The results of condition surveys or inspections are used to evaluate the performance of each asset in terms generally understood by stakeholders, such as Good, Fair or Poor.

It is common for transportation agencies to report the percent of the network in Good or Fair condition or the percent of drivers traveling on roads in Good and Fair condition. Asset performance can also be reported in terms of a health index, such as the Remaining Service Life (RSL) used by some state DOTs to indicate the amount of serviceable life left in the asset. In the maintenance community, some state DOTs have developed a Maintenance Health Index or overall LOS grade to represent the performance of the entire Maintenance Division rather than report the grades of each category of assets separately.

Asset performance also influences overall system performance, as demonstrated by the impact on system reliability associated with unplanned road or bridge closures due to flooding or an on-going lack of maintenance. Performance data related to delay, unplanned closure frequency, GHG emissions, and crash locations may all be impacted by asset conditions and affect an agency’s ability to achieve its broader, strategic performance objectives such as system reliability, congestion reduction, environmental sustainability, and freight and economic vitality. For example, it is important to monitor performance characteristics such as travel time reliability to determine whether capital improvements are needed to add additional lanes or whether ITS assets could improve traffic flow during peak periods.

Maintaining Inventory Information

One of the challenges transportation agencies face is keeping their asset inventory current, because it can require business processes dependent on individuals or agency work areas that differ from the primary asset owners. For example, construction may be responsible for installing new guardrails as part of a pavement-resurfacing project, but the information is not always made available to the maintenance division responsible for budgeting and scheduling guardrail repairs.

Establishing Processes to Update Inventory Information

Some types of inventory information change regularly while other information changes infrequently. As a result, it is important to classify each type of data and establish procedures in order to ensure the inventory is updated as information changes. An agency should establish business processes to ensure any changes to the inventory are reflected in relevant databases. For example, each time a pavement improvement project is completed, the database should be updated with information about the new surface type, the project completion data and the other assets replaced as part of the project. Establishing these processes and holding individuals responsible for updating this information are important for the ongoing success of a performance-based management approach.

Maintaining Condition Information

Asset condition and performance information must also be updated on a regular cycle. In some cases, data collection cycles are mandated by regulations, such as federal requirements for reporting pavement and bridge condition information on the National Highway System. Where there are no requirements in place for condition reporting, the update frequency should be determined based on the resources available, how the asset is managed and the data analysis cycle. Different update frequencies may be established for different types of assets.

Asset condition information may be collected based on a regular interval schedule or an inspection may be triggered based on the asset’s condition. For example, an asset in poor condition may require inspection more frequently than an asset in good condition. In general, asset information is updated on a 2- to 4-year cycle, but in some cases asset data is collected more frequently. For instance, some agencies collect performance data on maintenance assets several times a year to ensure they are in good working order and performing as expected. The condition of other assets with a slower rate of deterioration may be conducted less frequently.

As discussed in Chapter 6, there are several different types of information needed for TAM decision making. These include:

• Asset inventory and design information including location, type, quantity, material, and design details. This also includes summary level information about the asset as a whole as well as information about individual asset components (e.g. different pavement layers or bridge elements). It may also include asset valuation information (calculated based on deteriorated replacement cost, historic cost, or fair market value).
• Asset condition and performance information including results of visual inspections, measured condition (such as roughness or cracking for pavements), and computed measures of performance (such as remaining service life or “deficient” status designation). This also includes aggregated network level measures (such as the percentage of pavement in good condition).
• Contextual information such as system or network characteristics, functional classification, highway geometric characteristics, traffic volumes, congestion and reliability, crash history, adjacent land uses, weather and features of the natural environment. This information is helpful for understanding factors that may impact the asset service requirements or goals, physical deterioration, funding eligibility, and/or project needs and constraints.
• Work information including date, cost and scopes of work proposed, scheduled and completed on assets – including installation, replacement/reconstruction, rehabilitation, preservation and maintenance. When projects include multiple assets, it is valuable to itemize the work performed by asset.
• Revenue and funding allocation information including historical and forecasted funds available for asset installation, replacement/reconstruction, rehabilitation, preservation and maintenance – by source; and historical allocations by asset category and work type.
• Analysis information including forecasted condition and needs under varying funding or program scenarios, treatment life or life extension results, or project prioritization ratings or rankings.

Agencies store and manage TAM-related data within several different information systems:

• Asset Management Systems (AMS) – this includes pavement management systems (PMS), bridge management systems (BMS), management systems for other specific asset classes (sign or signal management systems), and systems used to manage information for multiple asset classes. All of these systems are used to store inventory and inspection data, and track work performed on an inventory of assets. They also typically include contextual information needed for modeling and analysis, such as traffic, functional classification, number of lanes, and presence of a median. More advanced management systems may identify and forecast preservation and rehabilitation or replacement needs, and analyze funding scenarios. However, often agencies use multiple systems for this purpose, with separate systems for maintaining the asset inventory and predicting future conditions. Pavement and bridge management systems are typically used as the sources for federal Highway Performance Monitoring System (HPMS) and National Bridge Inventory (NBI) reporting.
• Maintenance Management Systems (MMS) – used to plan and track routine maintenance activities. These systems typically store information about planned and completed maintenance activities and resources (labor, materials, equipment) consumed. MMS may include customer work requests, work orders, and maintenance level of service (LOS) information. Some MMS do not store any asset inventory data. In such cases, work is tracked by maintenance activity category and route section rather than specific asset. Note that there are many commercial Asset Management Systems that provide full functionality for asset inventory, inspection/condition assessment, work planning, and work tracking.
• Program and Project Management Systems (PPMS) – used to manage information about capital and major maintenance projects from initial planning and programming through completion. There may be separate systems for managing programming/funding information, preconstruction/design information and construction phase information. Some agencies integrate data from these various systems to obtain a single source of project information. Project information typically includes a mix of tabular data as well as unstructured data (for example, documents and images). Unstructured data may be managed within an engineering content management system separately from other data.
• Financial Management Systems (FMS) – used to manage and track revenues, expenditures, budgets, grants, payments, receipts, and other financial information. These systems are often supplemented with special purpose tools supporting budgeting, revenue forecasting and analysis.
• Enterprise Resource Planning Systems (ERP) – incorporate features of financial systems as well as a wide variety of other modules for functions including human resources, payroll, purchasing, maintenance management, inventory management, equipment management, project programming, project financial management, and revenue forecasting.
• Highway Inventory Systems (HIS) – used to store and report administrative and physical characteristics of the roads and highways. Federal Highway Performance Monitoring System (HPMS) requirements and the Model Minimum Inventory of Roadway Elements (MIRE) define standard road inventory elements; some DOTs maintain additional elements. HPMS elements include pavement type, pavement condition (roughness, cracking, rutting and faulting), and structure type. These systems may include Linear Referencing System (LRS) management capabilities or, may be integrated with a separate LRS management system. Per FHWA’s All Roads Network of Linear Referenced Data (ARNOLD) requirements, state DOTs must submit an LRS for all public roads to FHWA, linked to their HPMS data.
• Crash Data Systems (CDS) – used to store and report data about collisions and resulting injuries and fatalities; which when combined with traffic data and road inventory data provides information for identifying traffic and safety asset needs.
• Traffic Monitoring Systems (TMS) – used to store and report traffic data, required for federal reporting and used for a wide variety of purposes, including TAM processes for asset deterioration modeling, treatment selection and prioritization.
• Engineering Design Systems (EDS) – used to create design drawings or models including design details for different assets. As agencies adopt 3D object-based design modeling practices, there are opportunities to share information about assets between design models and other asset data systems used across the life cycle.
• Enterprise Geographic Information Systems (GIS) – used to manage spatial information, including asset location. Assets may be represented as point, linear or polygon features; location may be specified based on coordinates and/or based on a linear referencing system (LRS). Asset features maintained within GIS may be linked to asset information within other systems.
• Imagery Databases (ID) – used to store highway video imagery and mobile LiDAR data that can be used for manual or semi-automated extraction of asset inventory.
• Data Warehouses/Business Intelligence Systems (DW/BI) – used to integrate data from source systems for reporting and analysis. These may be tailored for TAM decision support.
• Other – there may be other specialized decision support tools that produce analysis results – for example, tools for life cycle cost analysis, cross-asset optimization, or project prioritization.

Table 7.1 provides an overview of different systems with the types of information they typically contain. Note that this may vary within each agency.

Table 7.1 - TAM Data and Systems Overview

Common components included in computer-based asset management information systems are shown in Figure 7.1. Network inventory, network definition (e.g., location), and asset condition information serve as the primary components in a database, which may or may not be external to the management system. Agency-configured models are used to predict changes in asset condition over time and to determine what treatments are appropriate as the assets age and deteriorate. These models may be developed and updated based on historical condition and cost data.

When developing a computer-based model, an objective (performance, condition, financial, risk) must be defined within the model for it to evaluate these criteria to develop and select optimal strategies. Metrics such as benefit-cost, risk, condition and treatment costs are often used.

A typical pavement management system performs some type of benefit/cost analysis that determines the performance benefits (typically in terms of improved condition) and the costs associated with each possible treatment timing application. By selecting the projects and treatments with the highest benefit/cost ratio, an agency can demonstrate that it is maximizing the return on its investment.

Bridge management systems more typically rely on optimization to perform a single-objective analysis, such as minimizing life cycle costs or maximizing condition, or a multi-objective optimization analysis that considers factors such as condition, life cycle cost, risk of failure, and mobility. Project- and/or network-level benefit/cost analyses are used in a bridge management system to explore all feasible treatment options over an analysis to determine the most cost-effective set of treatments with the highest benefits to the network.

Figure 7.2 shows an example of how the different systems listed in Table 7.1 might be integrated, adapted from the approach used by a U.S. state DOT.

Linking information across different systems enables agencies to quickly answer important questions that might have taken hours of staff time without integrated data. Integrating data opens up access to previously siloed data sets across the organization. It allows an agency to reduce duplicative effort, achieve efficiencies and derive greater value from its data. Some questions that rely on integrated data are:

Investments and Accomplishments

• What have we spent over the past ten years on route X in county Y (across all assets and including both maintenance and rehabilitation)?
• What percentage of deficient pavements will be addressed by our current capital and major maintenance programs?

Work Costing and Scoping

• What does it cost us to restripe a mile of pavement markings in each district?
• What locations identified along the linear referencing system (LRS) are planned for next year?
• Do the costs estimated by our pavement management system match what we are actually seeing in our projects?
• If we upgrade our guardrails whenever we do a paving project, how long will it take, and what will it cost to eliminate the current backlog?
• How can we best plan our projects to address multiple needs that may exist along a corridor?

Performance

• How many years does our standard mill and fill pavement treatment last for roads in different traffic volume categories?

Tradeoffs and Prioritization

• How should we prioritize our asset replacement/rehabilitation projects, considering not only life cycle management strategies but also stormwater management, safety, congestion, non-motorized, transit and ADA needs?
• How should we allocate our available funds across multiple asset types?

Disaster Recovery

• What assets were on route X in county Y prior to the storm? What will it cost to replace them?

An integrated approach to asset data collection, management and reporting not only makes it easier to answer these questions; it also can reduce costs. Opportunities for achieving efficiencies include:

• Using a single application to manage information about multiple assets.
• Using Data Warehouse/BI and GIS tools to provide reporting and mapping functions rather than investing effort to develop these capabilities within individual asset management systems.
• Gathering data on multiple assets through the same approach – using mobile technology, video imagery and/or LiDAR (see section 7.2)
• Sharing asset data across the life cycle – for example, automating methods for extracting asset data from design plans to update asset inventories (described further below).

Emerging technologies and new data sources are making an integrated approach to asset data management even more important. For instance, there are increasingly opportunities to use data collected from cell phones and connected vehicles that may cut across many asset categories. Also, there has been and will likely continue to be rapid advancement in machine learning techniques, such as for extracting asset data from video imagery or predicting optimal maintenance interventions given a wide array of data. Using these techniques typically requires establishing large, integrated data sets.

In addition, advances in computer-aided design and engineering software are making it possible to integrate asset data across the life cycle and achieve efficiencies and cost savings in maintaining asset inventories. See the discussion in section 7.1.4 further on.

Step 1: Establish Requirements

What is the purpose of the integration? To create a publicly available map showing asset conditions and projects for both internal and public use? To create a BI environment for answering a range of questions about asset performance and cost? To integrate asset data across different systems used for planning, design, construction, and maintenance?

Based on the identified needs, determine what data will be integrated and at what frequency. Consider whether this will require historical data, current data, future projections, or a combination.

Early collaboration between business units and information technology units is important to establish a shared understanding of both business needs as well as technical requirements and constraints. A strong business-IT partnership is essential for successful information integration initiatives.

Step 2: Identify and Evaluate Data Sources

Identify the available data sources to meet requirements. Determine where the data reside, and in what form – such as engineering design systems, relational databases, spreadsheets, document repositories, etc. Assess the current level of data quality to make sure that the source is ready for integration, based on discussions with the data steward or through examining the data. For design files/models a key quality consideration is whether established standards have been consistently applied. It is also important to determine the level of spatial and temporal granularity – or what does each record represent (such as a pavement condition observation for a 0.1 mile section for April 2019; a paving project on a 1.5 mile section due to open for traffic sometime in 2020).

Step 3: Analyze Linkages

Identify how different data sources will be linked. Spatial linkages are a good place to begin. If GPS coordinates are used, make sure that the Coordinate Reference System (CRS) used is documented, along with positional accuracy. If a Linear Reference was used, determine what method was used to establish the measure along the route, and what version of the agency’s LRS was used to establish route identifiers and reference points. Find out if the linear reference has been updated to reflect changes in the LRS since the data were last collected (if applicable).

Identify other types of (non-spatial) linkages that may be needed to join different data sources – for example, project numbers, account codes, work order numbers, etc. Agencies may want to profile the data for these elements to understand variations in coding and formats.

Step 4: Design Data Flows and Select Technology Solutions

Based on the requirements, available data, the linkage analysis and the tools and resources available within the agency, design how the data will flow from sources to target systems, and select the technology solutions to be used for performing the integration itself. The target system might be a general purpose enterprise geodatabase, an enterprise asset management database, a BI tool reporting data source, data warehouse, or a data lake. Data Extract-Transform-Load tools are available from data warehouse vendors; simple integration tasks may be accomplished through scripting. There are also a variety of specialized tools available for transforming and combining spatial data, and for extracting data from CAD/3D models.

Step 5: Design and Implement Integration Methods

Develop the technical approach for transforming link fields so that they are consistent across databases and if applicable, joining the different data sets and combining common data elements from the different sources. This may involve spatial processing (such as dynamic segmentation), aggregation, coding conversions, and other transformations.

Short term integration strategies include:

• Creating GIS data layers and making them accessible in available web and desktop-based GIS software. This strategy requires that each data source uses compatible spatial referencing, or can be converted to a common referencing system.
• Creating a database or view combining data from various source systems, and using available BI/Reporting tools to create reports and data visualizations. This strategy requires identifying common “dimensions” across source systems and/or normalizing data so that it can be summarized. For example, if the agency wants to report asset quantities by district or county by year, it will be necessary to make sure that each source has these data elements and that the data can be converted to a consistent set of values.
• Exposing data from authoritative sources as services via Application Programming Interfaces (APIs).

In the longer term, agencies can consider re-architecting or consolidating their systems so that they work better together. A logical way to approach this is to document the “as is” situation and then map out a “to be” architecture. This will allow the agency to chart a path from the current state to the desired future state. It will also provide a framework for capturing requirements for any new systems that are brought into the agency.

Integrated asset management systems are not a new concept and there are several commercial systems that support information management and work planning for multiple asset types. However, some agencies are challenged to integrate information about major assets (pavements and bridges) with information about various other ancillary assets – given that approaches to planning and budgeting for major assets are more sophisticated and require a greater level of detailed information and analysis. Also, it can be a challenge to integrate information about operations and maintenance with capital projects given differences in how work is categorized, performed, and tracked.

Integrating information across the transportation infrastructure life cycle is an area of significant interest in the transportation industry. Several terms have been used to describe the collection of processes, standards and technologies for accomplishing such integration – including Civil Integrated Management (CIM) and Building Information Modeling (BIM) for Infrastructure. In 2019 ISO issued its first BIM standard, ISO Standard 19650. This builds on an earlier standard published by the British Standards Institute (BSI).

Traditionally, information created at one phase of the life cycle is archived and not made available to downstream processes. There are substantial opportunities for cost savings by using a shared, electronic model of the infrastructure, defining information needs at each life cycle phase, and establishing procedures for information handoffs across the life cycle. For example, information about assets included in a construction project can be compiled during design and linked to the model representations of the assets. This information can be confirmed and corrected during construction and made available to asset management systems when the project is completed and turned over to maintenance and operation.

Such integration can reduce duplicative data collection efforts, and speed the time required to make decisions and perform work. Implementing these techniques requires much more than adoption of technology supporting 2D and 3D models. A commitment to common standards and processes is needed. Recognizing that this scale of change takes time, maturity models and levels of implementation have been defined to guide agencies in developing roadmaps for enhancing life cycle information integration over time. See the references at the end of this chapter for further information.

Overview

Transportation agencies are facing increasing pressure to make more effective use of data and information systems to support their TAM programs. Addressing this need, NCHRP Project 08-115 produced NCHRP Report 956, a Guidebook for Data and Information Systems for Transportation Asset Management. AASHTO hosts web-based versions of the guidebook and tools, where subsequent implementation guidance and DOT implementation experiences have also been shared.

Built upon a data life-cycle framework which addresses five distinct stages in the use of data for TAM, the NCHRP Report 956 and associated AASHTO TAM Data Guide provide a structured approach for transportation agencies to:

• Assess current TAM data and information system practices and establish a desired state.
• Identify and evaluate data and information system-related improvements.
• Secure agency support for improvements and plan an implementation strategy.

Figure 7.4 Stages in the Use of Data for TAM

This guidance is supplemented with valuable support materials, including:

• Practical implementation tips to support the TAM data and information system assessment, improvement selection and evaluation, and action planning processes.
• Supporting materials and templates (such as assessment scoping guidance, stakeholder engagement and facilitation materials, and assessment summary and action planning templates).
• User guidance, quick reference materials, and tutorial videos to guide tool use and application.
• Research implementation examples based on four real-world implementations of the guidance at the New Hampshire, New Mexico, and Virginia DOTs.

Other Related Methodologies

For those exclusively focused on better understanding and improving how they manage their TAM data, NCHRP Project 20-44(12) was completed in 2022, providing improved tools, supplemental guidance, materials, and detailed case studies on implementation of the NCHRP Report 814 Data Self-Assessment Guidance. The outcomes of this project included detailed agency-specific assessment experiences, including several applications of the data management maturity and data value assessment frameworks in TAM-specific contexts.

Deciding what data to collect involves identifying information needs, estimating the full costs of obtaining and managing new data and keeping it up to date, and then determining whether the cost is justified. Just as agencies don’t have unlimited resources to repair and replace their assets, there are also limitations on resources for data collection and management.

A 2007 World Bank Study summarized three guiding principles for deciding what data to collect:

• Collect only the data you need;
• Collect data to the lowest level of detail sufficient to make appropriate decisions; and
• Collect data only when they are needed.

Chapter 6 can be used to help identify the information needed to track the state of the assets and investments to maintain and improve them. The basic questions one needs to answer to identify needed data are:

• What decisions do we need to make and what questions do we need to answer that require asset data? Typically, an organization needs to be able to answer questions including but not limited to its asset inventory, the conditions and performance of the inventory, and how resources are being spent on its assets. Also, an organization needs to determine what work is needed and how much that work will cost.
• What specific data items are required or desired? Next, one must identify the data required to meet the established information needs. There may be other data items that are not strictly required, but that may be useful if collected in conjunction with the required data. For instance, answering questions and making decisions regarding pavement an organization would typically want to have an inventory of existing pavement, details on paving materials used, and details on current conditions. Additional information on treatment history or substructure conditions might not be strictly required, but if available could enhance the decision-making process.

It is also important to incorporate standard data elements for location and asset identification into requirements, ensuring consistency with other asset data in the agency.

• What value will each data item provide? It is important to distinguish “nice to have” items from those that will clearly add significant value. The cost of collecting and maintaining a data element should be compared with the potential cost savings from improved decisions to be made based on the element. Cost savings may be due to asset life extension, improved safety, reduced travel time, or internal agency efficiencies. In addition, proxy measures for information value can be considered such as the number and type of anticipated users, and the number and type of agency business processes to be impacted.
• What level of detail is required in the data? Level of detail is an issue for all assets, but is particularly an issue for linear assets such as pavement, where one may decide to capture data at any level of detail. For instance, to comply with Federal reporting requirements for pavement condition a state must collect distress data at 1/10 mile intervals for one lane of a road (typically the outside line in the predominant direction). For other applications it may be necessary to collect data for additional lanes, or at some other interval.
TIP
Look for ways to "collect once, use multiple times" by leveraging existing data and planning data collection efforts to capture information about multiple assets.

• What level of accuracy is needed? The degree of accuracy in the data may have a significant impact on the data collection cost and required update frequency. Ultimately the degree of accuracy required in the data is a function of how the data are used. For instance, for estimating the clearances under the bridge for the purpose of performing a bridge inspection it may be sufficient to estimate the clearance at lowest point to the nearest inch using video imagery. However, more accurate data may be required when routing an oversize vehicle or planning work for a bridge or a roadway underneath it. If a high degree of accuracy is not required it may be feasible to use sampling strategies to estimate overall conditions from data collected on a subset of assets.
• How often should data be updated? Is the data collection a one-time effort, or will the data need to be updated over time? If data will need to be updated should the updates occur annually, over a period of multiple years, or as work is performed on an asset?

Table 7.2 below illustrates examples of data collection strategies that might address different information needs.

Table 7.2 Example Data Collection Strategies

Once a general approach has been established, more detailed planning for what data elements to collect is needed. Prior to selecting data elements, identify the intended users and uses for the data, keeping in mind that there may be several different uses for a given data set. Identify some specific scenarios describing people who will use the information, and then validate these scenarios by involving internal stakeholders.

One common pitfall in identifying information needs is failing to distinguish requirements for network level and project level data. While advances in data collection technology make it feasible to collect highly detailed and accurate information, it is not generally cost-effective to gather and maintain the level of information required for project design for an entire network of assets.

A second pitfall is failing to consider the ongoing costs of updating data. The data update cycle can have a dramatic impact on data maintenance costs. Update cycles should be based both on business needs for data currency and how frequently information is likely to change. For example, asset inventory data is relatively static, but condition data may change on a year-to-year basis.

A third common pitfall is taking an asset-by-asset approach rather than a systems approach in planning for both asset data collection as well as downstream management of asset information.

Even when there is a strong business case for data collection, it is sometimes necessary to prioritize what data are collected given budget and staffing constraints. Some agencies do this by establishing different “tiers” of assets. For example:

• Tier 1: Assets with high replacement values and substantial potential cost savings from life cycle management (such as pavements and bridges)
• Tier 2: Assets that must be inventoried and assessed to meet legal obligations (such as ADA ramps, stormwater management features)
• Tier 3: Assets with high to moderate likelihood and consequences of failure (such as traffic signals, unstable slopes, high mast lighting and sign structures)
• Tier 4: Other assets that would benefit from a managed approach to budgeting and work planning (such as roadside signs, pipes and drains)

While updating data can be expensive, various strategies are available for combining data collection activities to reduce the incremental cost of collecting additional data. For instance, one approach to collecting data on traffic signal systems is to update the data when personnel perform routine maintenance work. Also, in some cases data can be extracted from a video log captured as part of the pavement data collection process.

Given limited resources for data collection, it may be helpful to formally assess the return on investment from data collection or prioritize competing data collection initiatives. A formal assessment may be of particular value when considering whether the additional benefits from collecting additional data using a new approach justify the data collection cost. NCHRP Report 866 details the steps for calculating the return on investment (ROI) from asset management system and process improvements, including asset data collection initiatives.

There are many different approaches to collecting asset and related data. Often a mix of approaches is used, including visual inspection, semi-automated and automated approaches. The technologies for data collection are advancing rapidly, allowing for increased use of semi-automated and automated approaches for collecting more accurate data at a lower cost. Examples of recent innovations include:

• Improvements in machine vision that allow extracting some forms of asset inventory data from video or LiDAR.
• Use of unmanned aerial vehicles (UAV, also called drones) for allowing bridge inspectors to obtain video of hard-to-reach areas of a bridge.
• Improvements in non-destructive evaluation (NDE), allowing for greater use of techniques such as ground penetrating radar (GPR) for pavement and bridge decks and instrumenting bridges to monitor performance over time.
• Improvements in hand-held devices allowing for increased field use, reducing cost and time of manual data collection.

Several of these technologies provide opportunities to save money by collecting data for multiple assets within a single collection effort. Table 7.3 provides a summary of potential data collection approaches for common roadway asset classes.

Table 7.3 - Example Data Collection Approaches

Once data are collected, it is essential to put in place regular processes for updating the data. This can be accomplished through periodic data collection cycles, or through updating as part of asset project development and maintenance management processes.

Once an organization has determined what data to collect and how to best to collect it, the next step is to prepare for data collection.

Step 1. Coordinate

An important step prior to collecting data is to coordinate with other stakeholders in the organization concerning the data collection effort. It may be possible, through such coordination, to identify opportunities for coordinating data collection activities to reduce costs. Alternatively, other stakeholders may identify needs for collecting related data to address other needs. Another possibility is that a different business unit in the organization has already collected data that may impact the data collection plan.

Step 2. Specify

In this step one must identify exactly what data will be collected, the means used to collect the data, and who will collect the data. If data collection is being outsourced, at this point it is necessary to establish contract specifications for data collection.

Also as part of this step one should establish the approach for quality assurance (QA)/quality control (QC). A QA/QC plan specifies the desired accuracy of the data to be collected, and describes the measures used to assure data are of the specific level of accuracy, review data quality as data are acquired, and address any data quality issues that arise. If data are collected using automated means, the plan should specify the approach for calibrating any measurement devices used for data collection. If data are collected through visual inspection the plan should detail training requirements.

Note that given data QA/QC is an area of particular concern for pavement condition data collection, given the expense involved in collecting this data and increased reliance on automated data collection techniques. The Federal performance management requirements described previously include a requirement for State DOTs to establish a QA/QC plan for pavement data collection.

Step 3. Contract

This step involves determining whether to outsource data collection and to contract for services if applicable. Decisions to outsource are typically made to tap into a vendor with specialized equipment and experience with a particular data collection technique, and to enable accomplishing a major collection effort within a compressed timeframe, which would not be possible using internal staff resources. Some agencies may implement a hybrid approach, hiring a contractor while using internal staff (or a separate independent contractor) for supervisory or QA functions.

Step 4. Train

The last step prior to collecting data is to train the staff involved in data collection and review in how data collection should be performed, as well as in their specific roles and responsibilities. Training is important for any data collection effort, but is particularly important in cases where the collection effort relies on visual inspection (for inspecting bridges). In these cases, the training requirements for inspectors should be carefully established and implemented. Even where there are no formal requirements for inspectors, it can be highly valuable to assemble inspectors prior to the start of data collection to review the data to be collected, walk through the data collection process, and perform inspections in a test scenario to ensure consistent interpretation of condition assessment language and other areas where differences in human judgement may impact how data are collected.

Once these steps have been performed the next step is to collect data, following the approach established in Step 2 for data collection and QA/QC.

As with the design of reports and visualizations, designing a data sharing strategy should begin with an understanding of the different audiences for data and their needs. A variety of options for data sharing are available that can be employed. Table 7.5 outlines some of these options and suggests some questions to consider in selecting an appropriate option.

It is helpful to establish guiding principles for data sharing in order to achieve a consistent agency approach that provides maximum benefits in a cost-effective manner. Possible principles include:

• By default, data should be shared unless it is sensitive, protected by law or if sharing it would pose unacceptable risks or cost burdens
• Self-service methods of data sharing should be used when there is a relatively large pool of data users and data limitations can be readily communicated via standard metadata
• Avoid proliferation of single purpose data sharing applications by adopting standard platforms where multiple data sets can be shared
• When it is necessary to share the same data set through multiple channels, the source data should be stored in a single location or a single data refresh process should be used to reflect updates
• The process of preparing data for sharing, reporting and visualization should be governed to ensure quality, ensure adequate documentation, and avoid inconsistency

Table 7.5 Data Sharing Options

A standard data preparation process should be used before moving data to any official reporting source – whether it is a data warehouse, a geodatabase, or a file uploaded to an open data portal.

A data preparation process might use the following checklist:

• Is the data derived from a designated authoritative source system?
• Have data quality checks been applied?
• Has metadata for the data set been prepared, including explanation of the data source, date of last update?
• Is an individual or business unit identified for data users to contact for further information?
• Is an individual or business unit identified for reporting database or system managers to contact regarding any issues that arise?
• Has metadata for the data elements included been prepared (data dictionary)?
• Has the metadata been reviewed for completeness and quality?
• Has a data owner or steward signed off on the data publication?

Data governance can be implemented to:

• Improve quality and consistency of data
• Ensure coordination across different business units
• Maximize efficiency in data collection and management processes
• Enable data integration and shared solutions to make the most of available IT resources
• Ensure there is a solid business case for new data collection
• Ensure that data will be maintained once it is collected

Agencies may be motivated to establish a formal data governance function as they try to move from a siloed approach to collecting and managing data to one that is more coordinated and centralized.

For example, implementing a reporting system that takes data from multiple sources within the agency creates the need for standardization, documentation, and agreed-upon update cycles. It is important to get agreement on standard data definitions, formats and code lists from different business units to achieve consistency. It is also important to clarify who is responsible for fixing errors and the process for error correction in the event that errors occur.

Data governance is a means to an end. It is important to clearly define and communicate why an agency needs to strengthen data governance: what is happening now that the agency may want to avoid (such as data duplication)? What is not happening now that the agency may want to achieve (such as standardized data)? The effort involved in putting data governance in place should not be underestimated, since it involves changes in how decisions are made and changes in behavior. A full scale agency data governance model can take years to mature. However, data governance can be rolled out incrementally to focus on short term objectives. It is a good idea to adopt a set of principles to provide the foundation for data governance policies and practices. The AASHTO Data Principles (see callout box) can be used as a model.

A first step in data governance is to identify key decision points to be governed. These may include:

• Adopting common data definitions or standard code lists
• Adopting location referencing standards
• Adopting standard tools for field data collection
• Collecting new asset data to be included within an integrated asset management system
• Archiving or deleting existing data
• Modifying data elements for an existing TAM data source
• Adding new data layers to an enterprise GIS repository
• Adding new data marts to a data warehouse
• Adding new reports or controls to a BI environment
• Responding to an external request for data

It is best to take an incremental approach to setting up governance processes, starting with a few high impact areas that are aligned with what the agency is trying to achieve. For each of the selected decisions to be governed, think both about the criteria or guidelines to be followed as well as all the people who should be consulted or involved in making the decision.

• Criteria and Guidelines: Developing guidelines for key decisions is a good way to institutionalize practices that reflect the agency’s goals for data. For example, some agencies have established “readiness checklists” that need to be completed before data can be added to an enterprise repository. These ensure (among other things) that a data owner or point of contact has been identified, that necessary metadata is provided, that a refresh cycle has been specified, and that the authoritative source system of record has been identified.
• Decision Making Process: Consider who should be involved in each of these decisions – who is responsible for making technical recommendations, who should be consulted, who has approval authority, and who needs to be informed about the decision. Define a process for resolving issues and conflicts; and a process for granting exceptions to established standards.

Agency data governance bodies can be responsible for adopting both guidelines and process flows impacting decisions that impact multiple business functions. If there are no existing governance bodies or if decisions to be governed are specific to TAM, a separate TAM data governance group can be established.

Keep in mind that the function of governance bodies is to make decisions. Use technical advisory groups, working groups or communities of interest to do the collaborative work required to develop standards or make recommendations about changes to data and systems.

There are several different assessment tools tailored to DOT data programs that can be used or adapted as needed. In addition, several DOTs have created their own tools. Most of these tools are based on a maturity model.

A typical maturity model could include the following levels:

• Level 1-Initial
• Level 2-Repeatable processes
• Level 3-Defined and documented processes
• Level 4-Measured and managed processes
• Level 5-Optimizing processes (continuous improvement)

For TAM information and systems, maturity levels can be assigned to different aspects of data management and governance. Assessments can also be conducted at different levels of the organization – from the agency-wide level, to the level of individual information systems (or even data elements).

Table 7.6 shows the data management and information system-related assessment elements from the TAM Gap Analysis Tool, developed under NCHRP Project 08-90.

Table 7.6 - TAM Analysis Tool Assessment Elements

Figure 7.6 illustrates the data assessment guidance created under NCHRP 08-92. This process is suitable for application either at the agency-wide level, for an individual data program, or for a business process. It goes into greater depth than the TAM Gap Analysis Tool.

BIM Overview

Building Information Modeling (BIM) for Transportation is an emerging practice that supports asset data integration across the planning, design, construction, and lifecycle management and operation of an asset. A robust BIM implementation can support the development and maintenance of a digital twin of an asset, useful in asset maintenance and operations decision-making throughout the life of the asset. A digital twin is defined as a highly detailed virtual representation of a physical asset, reflecting its real-world configurations, historical updates, and maintenance activities throughout its lifecycle. It includes information on rehabilitation and repair actions, as well as the impacts of other related projects. The tool allows transportation agencies to enhance their efficiency in operating, maintaining, planning, scoping, developing, and delivering future investments related to the asset.

In the realm of transportation infrastructure, Building Information Modeling is revolutionizing asset management practices by providing a comprehensive digital representation of assets throughout their lifecycle. BIM's ability to integrate asset data, facilitate condition assessment, and enable informed decision-making is transforming the way transportation agencies manage their valuable infrastructure.

BIM serves as a centralized repository for asset information, encompassing geometric details, material properties, maintenance records, and inspection reports, thereby streamlining access to crucial information, and enabling efficient analysis and better decision-making. Moreover, BIM allows real-time monitoring of asset condition, enabling proactive maintenance and early detection of potential issues. This proactive approach prevents costly failures and extends asset lifespan, optimizing resource allocation and minimizing disruptions to transportation operations.

BIM's ability to simulate maintenance activities allows for more efficient planning and scheduling of tasks, ensuring optimal resource allocation and minimal disruptions to transportation operations. Furthermore, BIM enables performance analysis and simulation under various scenarios, such as traffic loads, weather events, or natural disasters, thereby identifying potential vulnerabilities and assessing the effectiveness of mitigation strategies. This comprehensive approach to asset management ensures the continued performance and reliability of transportation infrastructure, enhancing safety and resilience.