CHAPTER 4

Case Examples

Introduction

The preceding survey results were used to select DOTs for interviews in developing the following case examples. This chapter presents the results of the DOT case examples identified through the literature review and the survey questionnaire. The data collected from the case examples includes further detailed information from DOTs regarding their survey responses, interview questions, and analysis of data provided by the selected interviewees. The topics covered in the case example interviews included the following:

• Descriptions of ancillary transportation assets managed by the DOT, formally and informally.
• Description of the maturity level in managing those ancillary assets.
• Descriptions of the performance measures, metrics, and targets used along with any forecasting or planning regarding managing those ancillary assets.
• Discussions of the advantages and disadvantages of managing ancillary assets.

In the end, five DOTs were selected for case example interviews. The selection was based on the following survey questions and feedback:

• Does your DOT have an established (formal or informal) approach to managing ancillary assets (assets beyond pavements and bridges)?
• How many ancillary assets does the DOT manage in their TAM program?
• How many ancillary assets does the DOT formally have in their TAMP?
• Would your DOT be willing to participate in a case example interview?

Further, there was a desire to have geographic dispersion among the case examples, relying largely on the four AASHTO regions to achieve this objective. Based on initial contact with a listing of identified DOTs, the selected states and their selection criteria are portrayed in Table 4.1. The responses from the survey are summarized in Table 4.1 for considering case selection. The details of the programs provided from the interviews may vary slightly from Table 4.1.

The finalized list of DOTs interviewed for case examples were California, Colorado, Maryland, Minnesota, and Tennessee. Details of the individual interviews are outlined in this chapter. The interviews were conducted using a semi-structured approach, and the questions and talking points for the interviews can be found in Appendix C. The case examples are summarized using the following subsections:

• Overview of TAM.
• Ancillary Asset Considerations.
• Discussion of Maturity for Ancillary Assets.
• Performance Measures, Metrics, Targets, and Programming for Ancillary Assets.

Table 4.1. State DOT selections for case examples.

• Benefits, Challenges, and Future Plans.
• Findings and Experiences.

The case examples are presented in alphabetical order in the following sections.

California Department of Transportation

California Department of Transportation Overview of TAM

California Department of Transportation (Caltrans) manages a vast and complex multimodal transportation system with a wide variety of physical assets. The California State Highway System (SHS) includes a highway system of 49,672 lane-miles of pavements, 13,189 bridges, 212,759 culverts and drainage facilities, and 20,481 TMS assets. Caltrans also manages highways as part of the NHS. The NHS in California consists of 57,699 lane-miles of pavements and 10,936 bridges totaling 243,347,047 square feet of bridge deck area.

According to the 2022 California TAMP, California’s transportation asset information is summarized in two ways: for the entire Caltrans-maintained SHS (portions of which are on the NHS) and for the entire NHS (which includes a portion of the state system but also a portion not included in the SHS). This two-method approach, seen in Figure 4.1, is necessary to meet state mandates and to meet the federal requirements for all NHS pavements and bridges in the TAMP. The California Transportation Commission mandates that the TAMP include pavements, bridges, drainage, TMS, and supplementary assets, whereas the federal regulations only require bridges and pavements (Caltrans 2022).

Overall, Caltrans’ Asset Management Program aims to enhance California’s transportation infrastructure’s reliability, safety, and efficiency through proactive planning, risk management, and investment prioritization. By optimizing the use of resources and adopting a holistic approach to asset management, the program contributes to the long-term sustainability of the state’s transportation network.

Caltrans Ancillary Asset Considerations

As mentioned, Caltrans’ asset management program is dual-faceted between their SHS and NHS assets. The management of bridges and pavements is required in both approaches by state and federal regulations. Then, for the SHS, state law requires the management of drainage structures and TMSs. The four core elements of Caltrans’ asset management program are bridges, pavements, TMS, and drainage structures. However, Caltrans, as the steward of California’s vast transportation network, adopts a holistic approach to asset management, encompassing core and ancillary assets alike. This is captured in their TAMP, which provides comprehensive strategies for the preservation, maintenance, and enhancement of primary assets, such as bridges, pavements, TMS, and drainage infrastructure, but also supplementary assets. These are also captured in the State Highway System Management Plan (SHSMP), which drives their project selection decisions and details their investment strategies and performance measures. In recognizing the interdependence and interconnectedness of various infrastructure elements, Caltrans acknowledges the significance of ancillary assets in bolstering the resilience and efficacy of their transportation system.

The ancillary assets managed by Caltrans encompass a diverse array of infrastructure elements, including drainage pump plants, lighting, office buildings, overhead sign structures, safety roadside rest areas, complete streets, transportation-related facilities, and weigh-in-motion scales. These formally define Caltrans’ supplementary asset classes as referenced from the TAMP (Figure 4.2).

The evaluation of ancillary assets necessitates the implementation of comprehensive assessment protocols, encompassing inventorying, inspection, and condition assessment methodologies. While primary assets like bridges and pavements undergo routine physical inspections by specialized teams, ancillary assets may require a more nuanced approach due to their diverse typologies and functional intricacies. Additional factors playing into the nuanced approach for ancillary assets include the level of maturity and availability of asset data, deterioration modeling, establishing life-cycle treatments, schedules, and costs. Caltrans employs a multifaceted approach to asset evaluation, leveraging a combination of manual inspections, data-driven analyses, and advanced technologies to ascertain the condition, performance, and structural integrity of ancillary assets. They must report this annually to their commission. With maintenance, repair, and rehabilitation being the driver, Caltrans prepared an even more extensive categorization (including non-physical assets, such as safety elements and resiliency objectives) of needs for their SHSMP, as seen in Figure 4.3.

For each of the performance objectives, there is an annual inventory and corresponding evaluation in the SHSMP appendix, as seen in Figure 4.4.

Caltrans Discussion of Maturity for Ancillary Assets

Caltrans began implementing an asset management program in 2015, in alignment with federal regulations and a newly developing state law, Senate Bill 1 (SB1). These regulations would require Caltrans to define what assets would be reported in the TAMP and what would be

managed in the SHSMP. In coordination between Caltrans and the California Transportation Commission, an oversight governmental board, guidelines for the primary and supplementary assets were selected and defined. The considerations of the assets were based on the asset’s value, purpose, risk, and other factors. These decisions influenced the maturity of managing these selected assets, especially drainage structures and TMS, being required in the SHSMP. These two ancillary assets would become the most mature in Caltrans’ program, with the primary assets of bridges and pavements also having a high maturity.

The inventorying and inspection of ancillary assets have been revolutionized by technological advancements, empowering Caltrans to achieve greater accuracy, efficiency, and comprehensiveness in asset management practices. GPS-enabled devices, drones, and mobile applications play a pivotal role in streamlining asset inventorying processes, allowing field crews to capture geospatial data with unprecedented precision. Equipped with applications, field inspectors can conduct detailed inspections of ancillary assets, recording vital information pertaining to asset condition, maintenance history, and spatial coordinates. Some of the more advanced of these inventories and inspections occur with drainage structures and TMS.

Regarding drainage structures, this program had a jumpstart in 2005 since Caltrans was not sure what assets they had in this category. Asset deficiency was the primary method of indicating there was a problem. Caltrans decided to begin an inventory so they could attempt to address deficiencies before they became a major problem. In 2018, SB1 provided specific guidelines for inspection and inventory, targeting completion for drainage structures by 2022–2023. However, due to the vast network of structures, this schedule was extended for more developed areas. The inspections performed for drainage structures are well-structured and conducted by trained inspectors. The TMS program is also mature, though its inspections are less controlled than those of drainage structures. District staff inventory the TMS assets and monitor their condition or rely on their life cycle for condition assessment. The inspections of both systems will be further detailed in the next section.

Caltrans Performance Measures, Metrics, Targets, and Programming for Ancillary Assets

A hallmark of Caltrans’ asset management framework is its emphasis on centralized data management systems, which serve as repositories for asset-related information and facilitate data-driven decision-making processes. While each core asset has asset management data systems that bring together statewide asset inventory and condition data, each system is structured and managed separately by the different bridge, pavement, TMS, and drainage programs. Caltrans headquarters Asset Management group uses an enterprise software system called the “Asset Management Tool” to combine all the inventories and link assets to projects, costs, and needs to enable project portfolio development and support overall performance management. By integrating GIS and asset management databases, Caltrans consolidates disparate datasets into a unified platform accessible to stakeholders across the organization. This centralized approach not only enhances the efficiency of asset inventorying and inspection activities but also enables real-time monitoring of asset conditions, performance trends, and maintenance requirements.

Forecasting the life cycle of ancillary assets poses inherent challenges due to the diverse typologies, environmental factors, and operational variables associated with these infrastructure elements. While standardized deterioration models exist for primary assets like bridges and pavements, ancillary assets exhibit greater variability in degradation patterns and life-cycle trajectories. Factors such as material composition, exposure to environmental hazards, and hydraulic dynamics further compound the complexity of life-cycle forecasting for ancillary assets, necessitating a nuanced and adaptive approach to asset management.

Despite the challenges inherent in life-cycle forecasting, Caltrans leverages data-driven predictive modeling techniques to anticipate the degradation and maintenance needs of ancillary assets. By analyzing historical performance data, environmental stressors, and asset-specific parameters, Caltrans develops predictive models capable of projecting asset deterioration rates and estimating future maintenance requirements. These predictive models serve as valuable decision support tools, enabling Caltrans to allocate resources strategically, prioritize maintenance activities, and optimize asset life-cycle management strategies. Again, the more advanced assets in this area are drainage structures and TMS.

Drainage structure inspections are building toward completion for the Caltrans system. All culverts are inspected every five to seven years to determine the current condition of the asset sufficiently. Once inventoried, the asset is assigned to a geodatabase. The drainage structure inspection involved five attributes: waterway adequacy, structural deficiency, joint deficiency, material deficiency, and shape. These evaluations will combine into an asset condition score. This information is used by headquarters asset management, along with deterioration rates determined through research, to predict what needs to be replaced or repaired, timelines for

forecasting condition changes, and the ability to look at corridors for larger bundled projects. Maintenance staff can also use this data to determine what culvert cleaning is necessary. As this program matures, Caltrans will be able to develop and use its own deterioration models and rates. Before 2022, surveying and paper-based inspection approaches were used for drainage structure inventory and inspection. However, inventory recently became integrated into an Esri ArcGIS Field Maps application, and field inspectors use Apple iPads to record their inspections using Esri Survey123. Caltrans has also started using a robotic-driven camera to travel through the culverts. The inspection data are checked by Caltrans staff in a QA/QC to ensure data completeness, and it is then placed in a database accessed by necessary parties. A statewide geodatabase includes the details for each culvert or drainage system along a corridor.

With the data collected, Caltrans has been able to create the GIS-based Culvert Inspection and Management System (CIMS), seen in Figure 4.5. This system includes an inventory of 254,815 culverts, with the projected total number of culverts across the state being over 300,000. Additionally, Caltrans uses varying dashboards to present that data from the system, such as those displaying culvert cleanings (Figure 4.6) and overall culvert conditions (Figure 4.7).

These systems allow Caltrans to prioritize maintenance activities, mitigate potential risks, and optimize the lifespan of critical drainage infrastructure.

TMS asset management is also considered mature. TMS inventory and inspections are conducted at the district level where the assets are identified, tracked by when those assets came online, and based on expected lifecycles, when those assets will deteriorate from good to poor condition. The district staff can also monitor the data being collected to determine if the TMS is functioning. If there seem to be malfunctions or more work than regular maintenance, then these TMS assets can be cataloged as chronic problems, monitored or inspected more closely, or replaced if needed. Otherwise, TMS assets are generally planned for replacement based on pre-defined life-cycle schedules and verified during project development, inspected based on life-cycle expectations, or perhaps spot-checked.

There are also dashboards for TMS, featuring their current conditions, assets down by element or district, and a 90% good condition goal, as set by SB1, seen in Figure 4.8 (green indicates “Good” condition, while red indicates “Poor” condition). The TMS system also includes a dashboard, Figure 4.9, for projected conditions based on lifecycles (green is an indication a district is above the target for assets in “Good” condition).

Caltrans notes that having a well-structured asset management data system enables the collection of all the data for cross-asset-type decision-making. Caltrans presents this in Figure 4.10, in their TAM Map Portal, which allows for the identification of concentrations of issues to consider for projects, as well as considerations across assets.

Caltrans Benefits, Challenges, and Future Plans

In discussing the benefits and challenges of ancillary asset management, Caltrans noted that systems such as theirs allow for thorough investment and trade-off considerations to determine where to focus the funding. Without the level of data they have access to, informed investment decisions can be difficult. They witness this first-hand with some of the assets where asset inventory and condition data are incomplete, outdated, or unverified, such as with bicycle and pedestrian infrastructure. To improve another asset class would take substantial resources, including time, funding, and staff. In the area of asset management, Caltrans notes there will always be a balance of priorities, consequences, and available funding in both investment decisions and decisions regarding which assets to manage and at what maturity level. The goal is to optimize investments for the good of the public and the department.

Caltrans also notes that challenges will often exist in data quality and availability. They are always looking to improve. Caltrans currently has contracts to use photogrammetric methods for feature extraction to inventory other highway infrastructure assets, such as sign panels. Additionally, through the biannual pavement condition survey, vans outfitted with various sensing and image capture equipment acquire the condition of all pavements. They are investigating the possibility of using this data to identify guardrail locations and types, rumble strips, and other ancillary assets through some form of feature extraction to build inventories. Caltrans’s objective is to improve its asset management program through the use of new technologies.

Caltrans Findings and Experiences

Caltrans believes in a proactive approach to managing ancillary assets, integrating advanced technologies, standardized protocols, and predictive modeling techniques to optimize asset performance and longevity. Moving forward, continued investment in asset inventorying, inspection methodologies, and life-cycle forecasting tools will be essential to enhance the sustainability and efficiency of California’s transportation infrastructure.

Caltrans believes the success in asset management starts with clearly defining data needs and doing so by working with all stakeholders that will use the collected data. Once the data are captured, practices need to be in place to ensure the data are high quality, and investment tools are needed to manage and use the data to improve the program. Much of the success in data management and use comes down to organization and well-developed data models or schema. If older data are to be included, Caltrans notes there is value in making sure data migrations are thorough and accurate. Caltrans notes that the key is in getting the data right, even if that means starting small with one asset. A well-executed approach with one asset sets the stage for adding others, and there is much that can be learned from other state DOT asset management programs.

Colorado Department of Transportation

Colorado Department of Transportation Overview of TAM

Colorado Department of Transportation (CDOT) manages a vast and diverse multimodal network. The system includes bridges stretching over large canyons and rivers, miles of roadways through rugged mountains, and complex tunnels. Various infrastructure elements are essential to the functionality of this network, including culverts, retaining walls, rockfall barriers, and advanced technologies like traffic signals, cameras, and wireless systems. CDOT uses a proactive approach to manage these assets, foster economic prosperity, and enhance the quality of life for Colorado residents. CDOT has undergone a transformative journey in managing its ancillary assets beyond the traditional focus on pavements and bridges. Key governing documents for the asset management program include CDOT Policy Directive (PD) 14 and PD 1609, which sets asset management requirements for inventories and performance targets.

Additionally, the 2022 CDOT TAMP is instrumental in achieving these objectives by assessing risks, costs, available resources, and avenues for innovation. Figure 4.11 illustrates the assets considered in CDOT’s TAMP.

In May 2020, CDOT adopted a 10-year plan that identifies transportation strategic investments across the state, ranging from long-deferred resurfacing projects to large and complex projects. The integration of this 10-year plan with the TAM Program is the focus of several initiatives, including integrating the plan with asset forecasting. Wherever possible, projects within the 10-year plan will be incorporated in CDOT’s Asset Investment Management System model to account for any forecasted condition improvements, as well as financial plan figures.

CDOT Ancillary Asset Considerations

CDOT expanded its asset management journey in 2012, previously focusing mostly on pavements and bridges. Over time, the department expanded its scope to include various ancillary assets such as buildings, culverts, and various types of walls. The decision to manage these ancillary assets was driven by various factors, including existing funding, criticality, public feedback, and the need for comprehensive maintenance strategies. In one case, rest areas were added as a new class due to a study finding this asset class was not funded sufficiently, and there were public complaints. CDOT determined about $6 million per year was needed to properly maintain the rest areas and increased the asset management budget by this amount to incorporate rest areas as a managed asset. CDOT now manages 12 different asset classes, including pavements and bridges, and each class has its own asset manager at the headquarters level. Some classes also have asset managers at the region/district level.

The ancillary assets managed include buildings, culverts, fleet/road equipment, geohazards, ITS, rest areas, traffic signals, tunnels, walls (bridge walls, retaining walls, and noise walls), and MLOS, as seen in Figure 4.11. This totals ten formal ancillary asset programs, with MLOS being a very broad class. MLOS comprises activities such as snow removal, striping, litter removal, signs, and delineators.

CDOT Discussion of Maturity for Ancillary Assets

As can be inferred from the performance measures in Figure 4.11, the ancillary assets vary in maturity. Some are inventoried and evaluated based on expected lifecycles, while others are

inspected with regular frequency. CDOT requires that all assets have a target, a model, and a rule on how to invest, though some of the models are experimental. While pavements and bridges represent the most advanced and well-established programs, other assets are at different stages of development. For instance, rest areas have only recently been added as an asset class, but the inventory is already mature thanks to an Esri-based application to facilitate proper inspections. Meanwhile, the CDOT bridge team includes advanced professional inspectors who fulfill the requirements of the National Bridge Inventory in addition to fulfilling CDOT’s needs.

While maturity depends on asset class, most of the asset classes will be at least inventoried or evaluated every year. This does not mean each asset in the class is evaluated; just some portion of the class population is evaluated. Bridges, for example, are on a cycle where each bridge is inspected every 2–3 years, and pavements are on a yearly cycle. CDOT noted that there is a difference between maturity and being advanced. For example, CDOT, in evaluating its tunnels, found that the performance metric was not working as expected. To them, recognizing this without holding on to the metric is a certain maturity. Now, they are looking to other tunnel-evaluating state DOTs for guidance on developing a new metric.

Finally, maturity varies within the MLOS class itself. The MLOS program maintains asset measures that enable the program to determine how much funding is needed to maintain current condition grades. There are a variety of assets mentioned in MLOS in CDOT’s TAMP, including signs, striping, and delineators. For many of these, their assessment is largely inventory and condition grading, but some are more mature, such as striping. CDOT evaluates striping with measures like retro-reflectivity. Signs are another example of an asset evaluated by the CDOT safety branch. While more mature than simply inventorying, signs are managed less formally than asset classes in CDOT’s asset management program, which requires forecasting and other analyses.

CDOT Performance Measures, Metrics, Targets, and Programming for Ancillary Assets

CDOT employs a range of performance measures and metrics to evaluate ancillary assets’ condition and performance. However, standardization and cross-asset analysis remain challenges, particularly for assets like tunnels, where forecasting and risk assessment metrics are not standardized in the industry. CDOT continuously refines its approach to align with AASHTO’s TAM maturity scale, aiming for proficient and best practice levels across all asset classes. The performance measures and targets are seen in Figure 4.12.

The data collected on ancillary assets informs various programming and decision-making processes in CDOT. From scheduling maintenance activities to budget allocation, asset data plays a crucial role in prioritizing investments and optimizing resource utilization. The department’s investment decisions are guided by cost-benefit analyses generated by tools like dTIMS (Deighton Total Infrastructure Management System), which forecasts condition improvements over time based on different funding scenarios. Many states use dTIMS for pavement decisions, but CDOT uses it for 11 asset classes. CDOT also uses various other technologies and tools, including Esri-based apps, SAP (System Analysis Program Development software), and other software, to collect data, forecast conditions, and recommend projects for varying assets.

Using dTIMS, CDOT can show current asset conditions (Figure 4.13) or provide cost-benefit analysis scenarios. Investing a certain level of funding into an asset class then translates to an increase or decrease in the level of condition for assets in that class (Figure 4.14). Comparing budget and forecasting shows options for evaluating investments over time. CDOTs GIS group can also map the assets and treatments (Figure 4.15).

CDOT’s asset management team can compare the dTIMS forecasts for the various asset classes to perform a level of cross-asset analysis. This is entirely dependent on the quality of the data available for a particular asset class. One of the challenges is weighing each of the asset types against the other. When CDOT used this approach directly, the modeling tool recommended investing more in the “buildings” asset class because buildings are cheap to repair, and there are good benefits to doing so.

Because the cross-asset recommendations of the model did not necessarily align with department priorities, CDOT compares model forecasts for each asset class to one another, outside of the model’s cross-asset analysis. By comparing the forecast of one asset class to another, officials can see that the performance improvement from investing in one area is more beneficial than investing in another area. By viewing recommended treatments on a map, as seen in Figure 4.16, the department sometimes analyzes which treatments can be bundled.

CDOT Benefits, Challenges, and Future Plans

CDOT has observed several advantages from its ancillary asset management programs, including improved planning, better resource allocation, and enhanced asset condition monitoring.

Additionally, all asset classes in the program receive funding consideration each year. In the past, geohazards were an example of an asset that, at times, did not receive resources when needed. The programmatic approach gives CDOT the ability to plan projects based on funding that is determined multiple years in advance. Knowing future budgets also gives CDOT the ability to set aside funds to plan for emergencies and inspections. Maintaining official policies, such as requiring each asset class to maintain an inventory, means CDOT will have the data needed for proper planning. For example, wall assets were at one point inventoried to about 30%; once walls matured as a managed asset class, inventories grew to 100%, and CDOT had robust data for decision-making.

However, challenges persist, such as prioritizing asset funding amid competing priorities, balancing funding between asset classes appropriately, and having adequate resources for analyses. Additionally, a challenge could emerge if additional asset classes are added to the program, which could split the current asset management budget further. Even without adding new asset classes, determining the appropriate budget split is challenging because once a class starts to get a base level of funding annually, it is difficult to cut back as asset managers become accustomed to working with that funding level. Another noted challenge was the lack of standards

in performance metrics and models nationwide for smaller asset classes like tunnels. The case of the CDOT tunnel metric was one where a standard industry metric could have assisted the program.

Despite these challenges, CDOT remains committed to maintaining its ancillary asset management programs. The asset management team is regularly approached with requests to add new asset classes to the overall program, such as permanent water quality features, electric charging stations, and buses. CDOT has documented requirements and created a form for staff to fill out if they would like to create new asset classes. The Colorado Transportation Commission and an executive oversight committee must evaluate the classes for approval.

CDOT Findings and Experiences

CDOT’s experience exemplifies the evolution of ancillary asset management in a state transportation department. Through strategic planning, technological innovation, and ongoing refinement, CDOT has transformed its approach to managing ancillary assets, laying the foundation for enhanced infrastructure resilience, improved service delivery, and greater efficiency in resource utilization. As other states embark on similar journeys, they can draw valuable insights and lessons from CDOT’s experiences to drive their own asset management initiatives forward.

CDOT emphasizes the importance of executive buy-in (especially related to funding), policy alignment, and ongoing commitment to asset management initiatives. The department advises states embarking on similar programs to establish clear processes, enshrine requirements in policy, and continuously evaluate and update their asset management models. Furthermore, CDOT underscores the need for comprehensive planning and resource allocation to sustain asset management efforts in the long term. CDOT has found its executive committee and its continuous drive for improvement to be fundamental to the success of the CDOT asset management program.

Maryland Department of Transportation and State Highway Administration

Maryland Department of Transportation State Highway Administration Overview of TAM

The Maryland Department of Transportation (MDOT) is comprised of six transportation modes (Figure 4.17). Together, these modes play an important role in managing Maryland’s transportation infrastructure of interstates, numbered highways, tunnels, bridges, bus networks, light and heavy rail, commuter rail services, and flagship facilities such as the Helen Delich Bentley Port of Baltimore and Baltimore/Washington International Thurgood Marshall Airport.

Given the complexity of assets in the portfolios of each transportation mode, MDOT designated seven “key asset classes” that represent the most critical to the transportation system and MDOT operations. Those assets are pavement, structures, tunnels, rail, facilities, vehicle fleet and equipment, and major information technology systems.

As each transportation mode operates and evolves, additional critical and ancillary assets are designated and managed through asset management offices and programs based on the mode-specific needs and services.

The Maryland State Highway Administration (SHA) oversees a diverse array of asset classes essential for delivering services to the state’s residents, businesses, and travelers. Figure 4.18 presents a summary of the SHA Asset Portfolio. In total, SHA manages 15 asset classes, inclusive

of more than 75 asset types. SHA employs a decentralized approach to managing assets, i.e., specialized asset class engineering offices manage the life-cycle decisions for their classes and types. The SHA Asset Management Office (AMO) facilitates the Enterprise Asset Management Program (EAM program), informing the business operations and life-cycle management strategies of the 15-asset class portfolio.

In addition to managing the EAM program, SHA delivers the Maryland TAMP on behalf of MDOT and in partnership with the Maryland Transportation Authority, Maryland Aviation Administration, and 12 federal, county, and municipal partner owners of NHS bridges and pavement in Maryland. SHA uses the Transportation Performance Management (TPM)-based planning and programming template of the TAMP to inform the development and implementation of asset class and portfolio management plans.

MDOT SHA Ancillary Asset Considerations

Ancillary assets, such as sidewalks and stormwater systems, play a crucial role in supporting a complete and safe transportation system. Ancillary assets are infrastructure components that complement the primary transportation network. Other ancillary assets include traffic barriers, traffic control devices, geotechnical, and other non-roadway elements. SHA manages its ancillary assets through the EAM program. The 13 ancillary asset classes are managed through asset-specific life-cycle management plans and, where possible, an asset management plan that documents a long-range performance-based plan for the programmatic needs of the asset class portfolio.

The EAM program aims to streamline asset management practices across diverse asset classes, including ancillary assets while supporting the individual needs of asset planning, engineering, and operations teams across the DOT. Standardizing processes promotes consistency and efficiency in asset management practices. While most roadway assets have formalized inspection protocols, some of SHA’s ancillary assets are in the process of standardizing inspection, condition, and criticality rating procedures. In many cases, inspections are triggered by customer requests or are situational, such as addressing a safety concern. Establishing formalized inspection protocols for ancillary assets is crucial for preventive maintenance and risk mitigation.

Technology plays a vital role in enhancing the management of ancillary assets. The implementation of geospatial systems and GIS service layers allows for the efficient cataloging and monitoring of ancillary assets. Mobile field devices equipped with enterprise maintenance management software, provide recommendations and asset maintenance and inspection records, enabling field personnel to address asset needs promptly and systematically. By prioritizing standardization, preventive and proactive maintenance, and technological integration, SHA can enhance the resilience, safety, and sustainability of ancillary infrastructure, ultimately improving the overall transportation experience.

SHA recognizes the challenge of balancing reactive maintenance requests with proactive asset management strategies. By adopting a proactive approach to asset management and leveraging data-driven insights, SHA aims to minimize reactive responses and optimize asset performance over time.

MDOT SHA Discussion of Maturity for Ancillary Assets

Managing ancillary assets with decentralized engineering and maintenance responsibilities and individualized risk-based inspection programs is challenging. Standardizing PBPP and enterprise risk management practices for ancillary assets is a key EAM program priority.

SHA employs condition forecasting methodologies for certain ancillary assets, such as pavement markings and sign panels, based on expected time intervals and past performance. Formalized inspection protocols are in place for critical assets like earth and retaining structures, with performance metrics used to flag assets for repair or replacement.

While individual ancillary asset classes may lack cross-asset decision-making capabilities, the annual 10-year asset SGR financial analysis for all 15 asset classes provides SHA with valuable insight into the needs of the asset portfolio and the growing financial gap causing an increase in the asset SGR backlog. This analysis considers the expertise of each asset class in predicting needs over the next decade, encompassing inspection, maintenance, rehabilitation, and potential replacement activities. This fiscally unconstrained, but expertly informed financial analysis facilitates programming decisions and prioritization of investments across the asset portfolio. The SGR financial analysis is a critical tool that aids ancillary asset classes in securing the funding needed to implement their life-cycle management practices and optimize investment over time.

Effective management of all assets, core and ancillary, is essential for maintaining safe, reliable, and sustainable transportation infrastructure. By addressing challenges, employing mature forecasting techniques, and integrating ancillary assets into strategic planning processes, SHA enhances the resilience and performance of its transportation network, ultimately benefiting the communities it serves.

MDOT SHA Performance Measures, Metrics, Targets, and Programming for Ancillary Assets

SHA uses a routine (one- to five-year) inspection cycle based on criticality for asset classes that are not on a time-based replacement cycle. For each class, there are established inspection protocols and evaluation metrics. During inspections, condition and asset characteristics are recorded to quantify asset performance across the portfolio and identify the treatment or replacement needs of assets.

SHA is refining a model that prioritizes meeting asset needs through the classification of asset criticality and condition (or performance) characteristics in a risk profile for each asset. Risk-based prioritization empowers staff to make informed decisions in the management of assets and provides the information needed to calibrate programs to achieve performance goals while mitigating risks. SHA is in the process of centralizing this information across multiple assets as part of a new Enterprise Asset Management System (EAMS) deployment, where the necessary data will be analyzed for optimizing asset management practices and investments at the enterprise level.

MDOT SHA Benefits, Challenges, and Future Plans

SHA continually refines the management of assets and system performance. The creation of the AMO in 2021 centralized functions for enterprise decisions, providing a more holistic view of the asset portfolio. This centralized approach allows for better coordination, resource allocation, and decision-making across all asset classes, including ancillary assets. While the office originated in the operations business unit, it was reorganized in 2024 to the Chief Financial Officer business unit. By aligning with the financial realm, the AMO enhances transparency regarding the financial needs of all asset classes, including planning, engineering, and operating needs. This enables better advocacy for ancillary assets, ensuring they receive adequate funding and support to implement the lowest life-cycle cost management strategies.

The EAM program is implementing a bundled approach to meeting asset needs. By analyzing the needs of all asset units in a corridor or project limits, the EAM program can prioritize projects for competitive infrastructure grant programs that return the highest possible benefit/cost return. The asset-agnostic approach allows for a focus on multi-objective decision analysis, prioritizing projects based on their overall impact and benefit to the transportation network. This promotes efficient allocation of resources to address critical needs across all asset classes.

The coming deployment of an EAMS will further streamline data collection, maintenance planning, and PBPP for ancillary assets. This system provides real-time insights into asset conditions, allowing for strategic maintenance and resource allocation. Future system functionality will support formalized risk-based asset management processes for all asset classes.

MDOT SHA Findings and Experiences

The SHA AMO was created to implement EAM program solutions. For example, the office is piloting a system-wide collection of roadside assets to support efficient asset inventory and condition tracking. Enterprise asset data collection proves to be more cost-effective and efficient than individual efforts and ensures all asset data are accessible through a single system and geospatially linked to the Maryland linear referencing system, One Maryland One Centerline.

The SHA EAMS facilitates a strategic approach to asset management that adjusts to the diverse needs of all asset classes. Rather than focusing solely on one asset class at a time, the program prioritizes projects that benefit the highest-risk needs of all assets across the transportation network. Effective communication of the financial needs of ancillary assets is crucial to secure the necessary funding. The AMO articulates the financial requirements in a unified manner to garner support for critical, core, and ancillary assets.

The systematic methods used by SHA to manage the asset portfolio underscore the importance of centralized asset management, financial transparency, collaborative asset data management, and strategic planning. By leveraging these advantages and lessons learned, SHA continues to enhance the resilience, safety, and efficiency of its transportation infrastructure, ultimately benefiting the communities it serves.

Minnesota Department of Transportation

Minnesota Department of Transportation Overview of TAM

Minnesota Department of Transportation (MnDOT) owns and operates 14,000 miles of state highway, including a variety of additional critical assets in its rights-of-way. MnDOT’s day-to-day operations are primarily overseen by one of its eight regional districts. These districts manage various tasks such as highway construction projects, maintenance activities, and managing highway right-of-way issues. Given the significance of Minnesota’s transportation network in bolstering the state’s economic competitiveness and overall quality of life, MnDOT has been dedicated to maintaining optimal asset performance through well-founded investment strategies.

Long before federal regulations mandated state DOTs to formulate risk-based TAMPs for pavements and bridges on the NHS, MnDOT had already embarked on its asset management journey. Initially, MnDOT surpassed the basic requirements for its inaugural TAMP by incorporating both NHS and non-NHS pavements and bridges, along with additional asset categories. Starting with a pilot study conducted by the FHWA in 2014, MnDOT’s first TAMP covered six asset classes. Subsequently, its 2022 TAMP expanded to encompass 12 asset classes. This decision to broaden the scope is evident in the objectives outlined in MnDOT’s 2022 TAMP submission.

MnDOT Ancillary Asset Considerations

The assets in MnDOT’s 2019 TAMP include:

• Required Assets
  • – Pavements
  • – Bridges (including Bridge Culverts)
• Other Assets
  • – Buildings
  • – Highway Culverts
  • – Deep Stormwater Tunnels
  • – ITS
  • – Noise Walls
  • – Overhead Sign Structures
  • – Pedestrian Infrastructure
  • – Traffic Signals
  • – Lighting
  • – High Mast Light Towers

The ancillary assets are listed as “Other Assets” in MnDOT’s TAMP. Many state DOTs manage ancillary assets outside of the TAMP. MnDOT began including ancillary assets, or “Other Assets,” in its TAMP in 2014, its first. MnDOT requested the addition of assets beyond pavements and bridges and was allowed to make the additions. The assets included in the 2014 TAMP were bridges, pavements, hydraulic structures, culverts, overhead sign structures, high mast lights, and deep stormwater tunnels. These assets were selected due to their high risk and with input from MnDOT’s Asset Management Steering Committee. In 2018, five more assets of traffic signals and lighting, ITS, noise walls, buildings, and pedestrian infrastructure were added.

There is often a question from other state DOTs about the liability of including the “Other Assets” in the TAMP. MnDOT has not had any issues with including the ancillary assets.

MnDOT has several asset management systems, but the majority of the assets are included in the Transportation Asset Management System (TAMS) through asset management software. TAMS serves as a foundation for collecting and housing the data from the current list of 16 asset classes. Data are collected through light detection and ranging (LiDAR), construction as-builts, or internal staff. Not all assets managed by MnDOT are included in the TAMP, such as ERS. The 2020 AMSIP included 78 asset classes in the DOT. MnDOT has determined how to manage all these assets with prioritized assets in tiers. Then, they had to determine how to collect the data to support the management of the assets. MnDOT held a risk workshop to understand the risks behind all the assets and the data needed prior to the inclusion of the assets in the TAMP.

Around the same time, in 2020, Statute 174.03 Subdivision 12 was enacted, which required MnDOT to inventory specific assets across the state. Fortunately, MnDOT had already begun inventories with several of these, such as pedestrian, geotechnical, and bicycle asset categories. This legislation just continued the momentum of managing ancillary assets for MnDOT.

MnDOT Discussion of Maturity for Ancillary Asset

Table 4.2 represents the maturity assessments of MnDOT’s ancillary assets.

MnDOT conducts a review of its TAMP assets every four years. Overall, MnDOT feels they are at the Structured level of maturity and approaching the Proficient level of maturity for the assets in the TAMP. Many of the assets in MnDOT’s TAMP have detailed performance metrics for condition assessments. Where inspection criteria and performance metrics are not developed, MnDOT bases the management of the asset on life-cycle projections.

Table 4.2. MnDOT ancillary asset maturity.

MnDOT Performance Measures, Metrics, Targets, and Programming for Ancillary Assets

For MnDOT, a number of assets have inspection-based condition information, but some ancillary assets are based on service life. The assets are organized by tiers based on risk and criticality of the asset, as seen in Figure 4.19.

The Minnesota State Highway Investment Plan is a 20-year capital investment plan for MnDOT and their assets. Asset conditions can be projected based on investment options that include performance measures and targets. Having this data allows for setting performance targets within the 20-year highway investment plan and for the development of performance levels and outcomes. MnDOT has a review process to determine budgets for allocating capital highway funds to asset management. The data used is available to the districts for transparency.

MnDOT uses TAMS for inspection forms, inventory, and various modules to review performance. TAMS also includes maintenance management, so when maintenance is issued a work order and performs work, they then record the information in TAMS. MnDOT has their maintenance crews capture data via TAMS. These crews use, enter, and extract data from the TAM system, and they use inspection and condition forms for efficiency. Mobile applications with geo-referenced information from the field have also assisted in data collection and accuracy. TAMS includes a decision matrix for determining the treatment options and next assets for repair. An example inspection inventory is displayed in Figure 4.20. The GIS module for TAMS presents the condition assessment and suggested treatments, as seen in Figure 4.21.

Also, MnDOT uses dashboards from asset management software (Figure 4.22) or spreadsheet software (Figure 4.23) to display information. These tools can be used to depict life-cycle costs by district, with projected performance and investment needs.

MnDOT is Developing a corridor risk tool to map from different data and plans. This tool would pull infrastructure data from the TAMP, risk and resilience data, vulnerability data, and other layers.

MnDOT Benefits, Challenges, and Future Plans

MnDOT notes keeping asset inventory and condition data current is a challenge, but being data-rich has created opportunities to obtain additional funding and improve planning that is based on data-driven decision-making. MnDOT mentioned resource constraints and the challenge of prioritizing efforts across so many different asset classes.

However, MnDOT feels its approach set a great foundation, and it could have moved toward its AMSIP sooner than it did.

Moving forward, MnDOT wants to improve cross-asset decision-making and trade-off analysis. MnDOT is also planning to work more closely with local municipalities to share asset data and coordinate planning.

MnDOT Findings and Experiences

MnDOT finds having their ancillary assets in the TAMP is useful and has no negative impacts, but there should be buy-in and committee-based feedback. They have worked to learn from other DOTs, which has helped them create best practices. MnDOT believes in focusing on higher-risk assets and understanding how the associated data will be used, and they like their enterprise approach rather than approaching one area at a time.

MnDOT’s main goal is to continuously improve in their management of its assets.

Tennessee Department of Transportation

Tennessee Department of Transportation Overview of TAM

The Tennessee Department of Transportation (TDOT) oversees infrastructure along interstates and state routes across Tennessee, ensuring reliability and mobility for its customers. The transportation system encompasses over 95,000 miles of roadways, more than 20,000 bridges, 79 airports, 120 miles of Class I railroads, two short-line railways, 949 miles of navigable waters, and two passenger ferries. Although Tennessee’s transportation network includes all modes—rail, air, water, and road—the final TAMP concentrates on two primary roadway assets: over 14,000 miles of pavement and more than 8,000 bridges. TDOT achieves its mission through the collaboration of central bureaus and its four regions. The roadway network serves a diverse group of stakeholders, including state residents, travelers, trucking companies, military installations, and more. Currently, asset management resides in operations, but there have been discussions about reorganization.

TDOT Ancillary Asset Considerations

While not formally within the TDOT TAMP, TDOT does maintain the following ancillary assets, with at least an inventory:

• Hydraulic structures (e.g., culverts, drainage systems), including cross drains, side drains, ditches, catch basins, etc.
• Overhead signs and structures.
• Sidewalks for ADA compliance and bicycle infrastructure for multimodal concerns.

• ITS and communications infrastructure.
• Traffic barriers (e.g., guardrail).
• ERS, including retaining walls, noise walls, and mechanically stabilized earth walls.
• Building facilities.
• Pavement markings and pavement markers.

Condition assessments depend on the asset class and there is not a consistent formal policy followed by the entire state. For example, there are regional sign shops for each of the four regions that handle sign maintenance. One division uses an access database to track the maintenance and repair of signs; this does not occur in other regions. The same region, for guardrails, created a database that will be statewide in the near future. The database tracks damage and contracts to repair guardrails, how long it takes to repair, and by whom.

TDOT did develop a new inspection process for hydraulic structures in 2021. TDOT had been doing an asset inventory and random sampling for the list of assets above, except for hydraulic structures. In 2021, TDOT procured a contractor to use LiDAR to do statewide inventory extraction but could not do this for hydraulic structures. TDOT instead used ArcGIS Survey123 with around 80 staff to do inspections. The goal is to inspect all hydraulic structures every 6 years. About ⅓ of the structures have been inventoried since 2021. They are working to inventory all structure assets as well. TDOT has developed reports to calculate condition scores, but they do not have life-cycle analysis data for deterioration forecasting of assets, though they are working toward that to become more mature in their asset management. Since 2021, the inspection of hydraulic structures has become more formalized, and a policy is in place, but it is not as formal as bridge inspections (federally mandated). While there is standardization of how to collect and store data for bridges, there is no standardization for hydraulic structures. TDOT has found challenges in using the information in their database, but they are developing new formats to make the data useful to other divisions at TDOT.

Hydraulic structures became a focus for asset management because TDOT did not have a lot of information on these structures, there was leadership interest, and the cost to maintain hydraulic structures requires a high level of investment. Using a tiered approach, if a structure requires a significant investment or damage to the asset could cause damage to a roadway, these are higher priorities.

One need at TDOT is to organize inventories, as some areas of inventory are handled across multiple divisions. Also, there are a lot of assets that are still being maintained in a reactive mode or complete replacement mode. For example, there is a practice to restripe all pavement markings every two years for painted markings and every three years for adhesive striping. In the future, TDOT is looking to do some reflectivity testing for condition assessment and forecasting. TDOT is considering using a LiDAR contract to collect pavement marking data, which would help to develop deterioration models. TDOT is trying to be more proactive as some areas of markings may last longer than three years, and some may need to be replaced sooner than three years.

Another potential use of the LiDAR contract may be for signs to assess retro-reflectivity. TDOT piloted a project using AI and crowdsourcing to assess the conditions of signs, and the LiDAR vendor did a side-by-side assessment. While LiDAR is not an accurate measure of retro-reflectivity, the AI process gave a good, fair, and poor assessment. TDOT is now looking at machine learning to improve the model and AI approach. TDOT is also still assessing signs at night to give a good, fair, and poor assessment and would like to move to an improved practice.

TDOT Discussion of Maturity for Ancillary Assets

Maturity varies across TDOT’s different assets. TDOT has used Maintenance Quality Assurance (MQA) inspections, where segments of corridors are assessed including an inspection of

all assets in the segment. TDOT transferred to a performance-based model, but this was too complex to align with the MQA scoring. TDOT needed to return to a Pass/Fail assessment because they are moving to outsource maintenance corridors, and there needs to be a straightforward evaluation of the condition.

Top-tier assets are all inventoried, and their conditions are evaluated to determine how to invest in them to perform at the required level. Lower-tier assets are starting to be invested in and being inventoried. TDOT’s Maintenance Management System does not currently include an asset management application to compile data and provide actionable information to determine the level of maintenance needed for assets. An older system in use since 2005 is no longer supported, and TDOT plans to move to the new system in the next couple of years.

TDOT Performance Measures, Metrics, Targets, and Programming for Ancillary Assets

TDOT is only formally preparing condition forecasts for the asset management of pavements and bridges. However, TDOT’s geohazards database is the ancillary asset data that is the most mature for forecasting conditions. TDOT is working towards forecasting conditions with additional ancillary assets (e.g., hydraulic structures).

TDOT’s TAMP focuses on bridges and pavements, but their goal is to write standalone documents on ancillary assets, how they are used, record their current conditions, and determine their priorities. This will help find the gaps to address in the future.

Performance targets are developed for most assets at TDOT based on the items of safety, preservation, and aesthetics. For evaluations, safety is A level, preservation is B level, and aesthetics is at a C level. TDOT has several systems for reviewing such data. First is the TDOT Roadway Asset Map (TRAM). This is presented in Figure 4.24 and Figure 4.25, which show the GIS-mapped assets collected from Survey123.

In selecting a specific asset, a user can link to the LiDAR data that TDOT collects in an associated application, which also provides a street view and the LiDAR point cloud for taking measurements. This is seen in Figure 4.26.

TDOT’s data and condition reporting can also be used for dashboards (Figure 4.27 and Figure 4.28).

TDOT Benefits, Challenges, and Future Plans

TDOT notes that resources and standardization are challenges in implementing a broader ancillary asset management project. Another challenge for TDOT is change; between reorganization, moving to corridor maintenance, and bringing on new systems and technologies for asset management, TDOT is experiencing significant changes.

That said, TDOT notes that the availability of asset management data to drive decision-making is beneficial and efficient.

TDOT Findings and Experiences

One of the items that TDOT noted as a lesson learned was when collecting large amounts of data, such as that collected by LiDAR, there needs to be a plan for data storage and management. In some cases, paying for data storage services is the best approach.

Case Example Summary

These case examples illustrate varying approaches to managing ancillary assets. The participating DOTs all had unique tools and approaches that added value to their asset management programs, and all noted that the data collected was immensely valuable. Their approaches to managing ancillary assets also varied, yet they were all effective.

Caltrans manages eight ancillary assets included in their TAMP as “Supplementary Assets.” These include drainage pump plants, lighting, office buildings, overhead sign structures, safety roadside rest areas, complete streets, transportation-related facilities, and weigh-in-motion scales. Caltrans’ programs for managing these assets are mature and they use a variety of metrics and measures to evaluate the conditions of these assets. While some may be inventoried with a service life, others are inspected to determine conditions or perhaps even have conditions projected. Caltrans uses a host of GIS and data analysis tools for all of their asset management. They note that data collection, data quality, and data maintenance can be challenging, but the benefit of systems like theirs is the ability to consider investments and tradeoffs across the portfolio of Caltrans’ assets. They also note a desire for more guidance on condition prediction models.

CDOT manages ten ancillary assets included in their TAMP, including buildings, culverts, fleet/road equipment, geohazards, ITS, rest areas, traffic signals, tunnels, walls (bridge walls, retaining walls, and noise walls), and MLOS. MLOS is a very broad and unique class comprising activities such as snow removal, striping, litter removal, signs, striping, and delineators. CDOT’s asset management is also mature, but this varies by class since they have been added over time. Most of the asset classes will have some percentage at least inventoried or evaluated every year. CDOT uses a range of metrics and performance measures to consider conditions and make condition projections. They note a desire for more guidance on projection models, similar to Caltrans. Also, like Caltrans, CDOT has a range of data analysis tools. The CDOT’s decision to manage these ancillary assets was driven by various factors, including existing funding, criticality, public feedback, and the need for comprehensive maintenance strategies. Balancing funding was a challenge noted by CDOT, but the benefit of managing ancillary assets comes in the transparency of doing so.

MDOT SHA includes 15 asset classes in their managed portfolio of assets. SHA is also mature in their asset management. SHA employs condition forecasting methodologies for certain ancillary assets, such as pavement markings and sign panels, based on expected time intervals and past performance. Formalized inspection protocols are in place for critical assets like earth and retaining structures, with performance metrics used to flag assets for repair or replacement. These approaches in management consider criticality and risk. SHA continually refines the management of assets and system performance. The creation of the AMO in 2021 centralized functions for enterprise decisions, providing a more holistic view of the asset portfolio. However, SHA also noted that change often includes minor challenges to work through.

MnDOT includes buildings, highway culverts, deep stormwater tunnels, ITS, noise walls, overhead sign structures, pedestrian infrastructure, traffic signals, lighting, and high mast light towers in their TAMP as “Other Assets.” MnDOT believes that the management of many of these assets is approaching the proficiency level. Like others, they use a range of metrics and performance measures to evaluate their assets, with some being inspection-based and others managed according to service life. MnDOT has many forms, tools, and data dashboards to facilitate efficient data entry, data management, and data analysis. MnDOT notes keeping asset inventory and condition data current is a challenge, but being data-rich has created opportunities to obtain additional funding and improve planning that is based on data-driven decision-making.

Finally, TDOT manages hydraulic structures (e.g., culverts, drainage systems), including cross drains, side drains, ditches, catch basins, etc., overhead signs and structures, sidewalks for ADA compliance, and bicycle infrastructure for multimodal concerns, ITS and communications infrastructure, traffic barriers (e.g., guardrail), ERS, including retaining walls, noise walls, and mechanically stabilized earth walls, building facilities, and pavement markings and pavement markers. TDOT does not include any ancillary assets in their TAMP. TDOT notes that the maturity of the management of these ancillary assets varies from inventorying to inspection assessments. The TRAM is a unique tool where TDOT has combined multiple datasets, including assess to LiDAR data. Though TDOT notes that managing large datasets can be a challenge and should be done strategically, they also note that the availability of asset management data to drive decision-making is greatly beneficial and efficient.