Transit Asset Condition Reporting (2011)

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18 Detailed case studies of two of the largest 50 transit agencies, the Massachusetts Bay Transportation Authority (MBTA) and the New York City Transit Authority (NYCT), were under- taken as part of this synthesis. The MBTA SGR project is an analytical approach for identifying capital reinvestment needs and setting investment priorities. The FTA cited the MBTA SGR project as the most comprehensive approach being used in the U.S. transit industry. NYCT is the largest transit authority in the country. In the last 25 years it has undergone a remarkable change from being in a state of disrepair to a much higher state of repair. The tool used to track and monitor this transformation is its Asset Con- dition Databases, which is covered in the second case study. CASE STUDY: ASSET CONDITION DATA COLLECTION AND TRACKING AT THE MASSACHUSETTS BAY TRANSPORTATION AUTHORITY Background The MBTA is the country’s fifth largest transit authority and carries approximately 1.3 million passengers daily. It is fully multi-modal, providing heavy rail, light rail, bus, trolley bus, bus rapid transit, commuter rail, demand response ADA (Americans with Disabilities Act), and ferry services. It pro- vides transit services to eastern Massachusetts and commuter rail service that extends to Rhode Island. It has a 5-year capi- tal plan of approximately $3.8 billion and an annual operating budget of $1.6 billion. In 1995, the MBTA devoted a substantial portion of its cap- ital program to expansion or enhancement to the current sys- tem. Several large projects were undertaken simultaneously including: • A new bus rapid transit line with a one-mile tunnel, • Three new commuter rail lines including stations and rolling stock, and • The rebuilding of a number of existing stations as part of an ADA program. Owing to the level of expansion activity, the funding available for investments in the existing infrastructure had shrunk to about half of the overall capital program. The other half was devoted to expansion. This approach did not appear to provide adequate funding for maintaining the existing system. It was deemed likely that a backlog of SGR investments was being created. Purpose In 1997, aware of the likely imbalance caused by expansion activity, the MBTA commissioned a study to determine the condition of its asset base and to develop an interactive SGR database. The goal was to: • Assess and monitor the true condition of the Authority’s assets. • Define in monetary terms the SGR backlog for the agency overall, by asset class, and on an asset by asset- specific basis. • Estimate the funding necessary to return the system to a state of good repair over a defined period and to main- tain it thereafter. • Articulate the case for additional funding. • Advocate for a permanent switch in the priorities of the capital program from expansion to investment in the existing infrastructure. • Select projects to be included in the Capital Investment Program based on the priority ranking provided by the system. SGR System/Database The asset tracking/condition assessment system that was developed is an interactive database that tracks assets, their useful life and condition, and calculates replacement values over time. The SGR database helps the MBTA assess the implications of various planning scenarios (i.e., for specific dollar amounts or an unlimited amount) and time periods (5-year or 20-year). The design of the database uses an age-based definition of SGR that involves funding renewal and replacement actions at specific years during an asset’s life. Assets are: • Renewed at critical midlife ages (e.g., engine replace- ments 6 years, roof replacements 15 years). • Replaced at the end of their useful life (e.g., buses 15 years, bridges 50 years). CHAPTER FIVE CASE STUDIES

19 The SGR database uses age as the major measure of condi- tion. The default values can be changed if a specific asset (or class) needs to be retired earlier (or later) than expected. These exceptions are based on a management evaluation of the asset’s condition. The MBTA does not have any specific rules for making these exceptions. The exceptions do require a detailed analysis of why a different service life should be used. This analysis, which typically would be prepared by operations management, is discussed and reviewed by senior management. The individual asset data in the database includes the fol- lowing information: • Count of each asset. • “Condition” measures (age, service life). • Project “action” costs – Replacement/renewal costs – Cash flow in years in which expenditures are made. • Data for ranking measures – Ages as percent of service life – Operational impact—yes/no for whether asset is essential to operations – Cost-effectiveness—cost of action per rider impacted. • Mode (e.g., subway, light rail, bus, and commuter rail). • Service area (e.g., Red Line or Green Line). • Asset type (e.g., rolling stock, station). The SGR scenario analysis is a sequential programming process that looks at SGR required actions by year. The pro- gramming steps for each year are: • Identify candidate projects, either replacement/renewal actions that come due in the analysis year or delayed projects from prior years. • Score and rank projects using the ranking measures. • Fund safety-critical projects regardless of their ranking measures. • Fund the remaining projects in priority rank order until the cost of the next project is greater than funds remaining. • Mark unfunded projects as candidates for next year. In discussions with the MBTA, senior management emphasized its strong position that “safety is priority one.” Safety projects should be and are funded when needed, regardless of the output from the SGR database or any crite- ria ranked therein. The definition of safety-critical projects includes two gen- eral types of projects. The first are projects that the MBTA must implement by legal mandate such as federal, state, or local laws or court decisions. The second concerns projects that involve assets for which failure would produce cata- strophic results. These involve a small number of projects related to signaling and communications. Such projects and asset types were selected based on a consensus of MBTA senior management and have not been changed significantly over the use of the database. The results are provided in graphic and table format, and can be provided at the system level, by asset type (e.g., track, rolling stock), service mode (e.g., commuter rail), and ser- vice mode area (e.g., Blue Line). These output features were included to make it easy to prepare focused and consistent presentations of the results. It was expected that there would be ongoing communication with decision makers about the MBTA’s progress toward meeting SGR for the entire system as well as for specific asset types, service modes, and service mode areas. The funding scenarios can be set in two dimensions: 1. Funding Levels. The scenario can assess the implica- tions of unconstrained funding or specific funding by year (e.g., $4 million in 2010, $4.1 million in 2011). 2. Asset Categories. The scenario can assess the implica- tions for all assets or for an asset category (e.g., power, rolling stock). Management at the MBTA reported that it would useful if the scenarios generated by the SGR database identified the cost benefits of funding or not funding specific projects to help inform agency managers and stakeholders. The original software is now being modified to include a component that will identify the reduction of corrective maintenance costs resulting from the funding of one new investment over another. Impact/Use The study and database took 2 years to complete. In 2000, the SGR database and its output were first used by the MBTA in its capital planning and programming activities. The output from the SGR database has been used effec- tively by the MBTA. After the database was completed, the MBTA made a concerted effort to persuade elected and appointed officials of the need to emphasize the funding of investments in the system’s current infrastructure over the funding of expansion services. This effort was successful. The funding of SGR invest- ments increased from about 50% to almost 80% within 5 years. In a subsequent development, the state has agreed to help fund mandated expansion projects. Recently (2009), a study of the system was undertaken at the request of Governor Deval Patrick. In this study, the use of the SGR database output was used to illustrate the impact of investment in the system’s infrastructure.

CASE STUDY: UPDATING THE DEFINITION OF BEING IN A “STATE OF GOOD REPAIR AT NYC TRANSIT” Background NYCT is the country’s largest transit authority. It is multi- modal and operates heavy rail (subway), bus, express bus, bus rapid transit service, and demand response ADA services. NYCT provides transit services to the five boroughs that com- prise New York City as well as bus service on Long Island. NYCT carries 5.1 million daily subway passengers and 2.3 million daily bus passengers. It has a 5-year capital pro- gram of $12.8 billion and approximate annual operating expenses of $6.2 billion. In 1982, NYCT’s system was in a state of disrepair. In response, the MTA undertook a series of 5-year capital plans to bring the system back into SGR. The MTA is now in its fifth 5-year capital plan and has made significant strides in restor- ing the agency’s assets to SGR. SGR Database As part of the SGR initiative, NYCT developed a database to help quantify and track its asset base. The information system contains details about each of the agency’s operating equip- ment and support assets. The database was also designed to help NYCT prioritize its capital investment needs. At the base of the asset management system are individual spreadsheets for each asset type. The amount of detail pro- vided varies by asset type, but generally includes for each asset: • Age • Manufacturer and model • Asset type • Mode (e.g., subway, bus) • Service area (e.g., the 1, 2, or 3 Lines, higher level “A-Division”) • Past SGR capital investment information. This detailed level of information enables NYCT to iden- tify specific assets that are in need of capital investment. This contributes significantly to both 5-year capital plan develop- ment and 20-year needs planning. For most asset types, this information is presented on a single summary sheet that shows the numbers of the asset in question, the average age of the asset, age distribution, and condition information. In addition, the basis for any backlog in SGR investments is provided such as condition assessment, age, or performance. More detailed information about assets is tracked within the database than is done at the MBTA. For example, sta- 20 tions are broken down into 11 separate components: plat- forms, platform edges, mezzanines, stairs, ventilators, wind screens, canopies, elevators, escalators, ADA access, and automatic fare collection system/equipment. In comparison, the MBTA’s database tracks three station components. The NYCT information can be combined and displayed by asset class type (e.g., rolling stock), mode (e.g., subway, bus), and service area (e.g., the 1, 2, or 3 Lines or at a higher level such as the “A-Division”). Although maintenance is continually done on these assets, the condition information in the base asset spreadsheets is updated on a rolling, 5-year basis. This is based on field assessments that are undertaken with a higher degree of credibility than are undertaken by the MBTA. However, the NYCT database does not have some features that make the MBTA database an effective strategic planning and programming tool, including: • The cost of asset replacement is integrated in the MBTA database. At NYCT, the costs are contained in a distinct and separate database. • Estimates of current SGR backlog can be made automat- ically in the MBTA database based on pre-determined condition settings and measures of SGR. This cannot be done automatically in the NYCT database. Instead, spe- cial manual querying and processing is required to pro- duce these estimates. This involves a much higher level of human interaction. • Scenarios can be run automatically in the MBTA data- base using different funding levels. The MBTA database has a programming logic that makes “funding decisions” based on the weighting of several project factors. This cannot be done in the NYCT database. In summary, the asset management system is a functional and detailed accounting of NYCT’s asset base and an assess- ment of its condition. NYCT senior managers report that it is fully integrated into the planning and funding efforts of the agency. They believe that the rolling, 5-year updating of the database helps provide substantive input into the 5-year Capital Plan. SGR Definition: The Old and the New At the beginning of the NYCT’s SGR initiative, the agency developed and applied investment “state” definitions to the assets tracked in the database. These definitions initially pre- scribed investment priorities, but also defined how outputs and asset conditions would be tracked and measured. For the first 20 years of the SGR initiative, when an asset reached SGR (through capital investment or was in SGR prior to the capital program), it was defined to remain in SGR

21 because it was assumed that the asset would receive proper maintenance. This assumption was made regardless of an actual condition or age. As these SGR-defined assets reached the end of their service life and were replaced, the costs of replacement were considered as “normal replacement” invest- ments, not as SGR investments. This approach of counting an asset as SGR asset regard- less of its actual condition led to an inaccurate measurement of the percentage of SGR achieved. It also unintentionally led to an investment process that supported investments based on definitions applied to the assets (normal replace- ment or not-at-SGR) rather than a more complete assess- ment of what was in a state of good repair and what was not. To address this issue, NYCT initiated a new condition- based approach. In this approach, an asset’s condition is deter- mined using one of the following three metrics most appropriate to its asset class: 1. Asset condition (ranked 1 to 4). 2. Asset age versus the presumed useful life of the asset. 3. Actual asset performance as compared with standards identified by the agency. This approach is sensitive to the actual condition of NYCT’s assets. It permits an asset that reaches SGR to lose its SGR sta- tus as it ages and its condition declines. Station Component Program In conjunction with this more refined approach to asset clas- sification, a NYCT also adopted a more focused approach for its station investment program. Initially, the agency rehabili- tated stations from top to bottom and replaced all station com- ponents without regard to their actual condition. After the station was rebuilt, it was then declared to be in SGR. NYCT believed that this approach misallocated resources and was unsustainable over the long run. It limited the number of sta- tions that could be rehabilitated within a given funding level and precluded other stations with repair needs from benefiting from SGR investment. NYCT developed a new approach to provide more benefits to more stations and customers. Station components would be repaired individually or in cost-effective groupings depending on their individual condition across a large number of stations. No longer would improvements be focused on a small number of stations. This component based and targeted approach was made possible by a survey of more than 11,000 station compo- nents systemwide. The new approach also included renewal projects. Where appropriate, components are bundled together with other cap- ital improvements into station “renewal” projects instead of station “rehabilitation” projects. The revised NYCT brings more improvements to the sys- tem and the public in more places at a quicker pace. It also makes better use of funding because it avoids unnecessary investment in components not in need of repair. Results As a consequence of the new approaches adopted by NYCT, the reported condition of various asset classes changed. Although several key assets (cars, track, and switches) remained stable at 100% in SGR, the reported condition of other asset classes changed, reflecting a more detailed and realistic measure of SGR. Three examples of the impact of this are: 1. Stations. Overall this category went from 53% in SGR to 67% because of the new definition and the new sta- tion component assessment regime. Funds allocated to stations were better focused on components in poor condition. 2. Power. The reduction in SGR status from 95% to 62% for this asset class reflected a more detailed assess- ment of the condition of the power system compo- nents. Previously, only substations were counted; now the components of a substation and the power distri- bution network are included in the assessment. 3. Buses. The SGR for both buses and their support facili- ties dropped because assets formerly considered replacement actions now are considered SGR actions. The SGR for buses dropped from 100% to 87%, whereas support facilities dropped to 66% from 90%. SUMMARY OF CASE STUDIES The two case studies demonstrate that focused attention to transit asset management can improve the funding of SGR projects. At the MBTA in Boston, the funding of SGR invest- ments increased from about 50% to almost 80% within 5 years. The Commonwealth of Massachusetts has agreed to help fund mandated expansion projects. In 1982, NYCT’s system was in a state of disrepair. In response, the MTA undertook a series of 5-year capital plans to bring the system back into SGR. The MTA is now in its fifth 5-year capital plan and has made significant strides in restoring the agency’s assets to SGR. The two case studies highlight two desirable features of an “ideal” transit asset management database system. The MBTA database is a good case study of an effective strategic planning and programming tool. The MBTA database can assess the impacts of different funding scenarios on the state of repair of

a transit system. These scenarios can be run “automatically” because the database contains: • Pre-determined condition settings and measures of SGR; • Costs of asset renewal and replacement; and • A programming logic that makes “funding decisions” based on the weighting of several project factors. 22 NYCT’s database is a good example of a detailed database. Assets such as stations are broken down into very detailed components that each have a service life and can be renewed. This level of detail provides the opportunity to consider the programming of specific renewals (e.g., replace escalators and roofs) rather than consideration of simpler actions at a higher level of asset aggregation (e.g., rehabilitation of a station).