Transit Asset Condition Reporting (2011)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

This synthesis examines and documents the current state of the practice in transit asset man- agement at large transit agencies. The objective of a good transit asset management system is to achieve and maintain a “State of Good Repair” (SGR), where all transit assets (e.g., vehicles, stations, and power systems) are replaced when needed. Achieving and maintaining SGR is a matter of urgent national concern. A 2008 FTA report, Transit State of Good Repair—Beginning the Dialogue, estimates that 25% of public transit assets are in marginal or poor condition. In addition, overall conditions have been declining because current infrastructure funding is inadequate and addresses only 60% to 80% of what is required for ongoing replacement needs and for the elimination of the backlog of past unfunded replacement needs (often termed backlog needs). The underinvestment in public transit infrastructure has significant consequences. Operating costs are higher because of the increased costs of maintaining assets that are performing beyond their useful lives. Service reliability suffers as more buses and rail cars breakdown in service. The quality and appearance of passenger amenities declines as stations and shelters age and escalators experience frequent failures. Safety becomes a greater public concern when aging assets fail at critical times, as recent accidents in Boston and Chicago have demonstrated. Ulti- mately, these consequences make transit service less attractive and result in lower use of tran- sit services. This synthesis found that the large transit agencies are concerned about the consequences of underinvestment, but use asset management systems that are elementary and limited. Most agencies have systems that track all assets and are frequently updated; however, these systems have limited ability to estimate the consequences of not making asset replacements when needed. The systems also lack the ability to test the impacts and consequences of different fund- ing scenarios. These limitations hamper the transit agencies in their efforts to develop compelling argu- ments for increased funding. They also do not provide the needed information that would help prioritize the programming of investment projects when available funding is not sufficient to provide implementation of all needed projects. This synthesis presents an overview of published literature on transit asset management sys- tems, a survey of the 50 largest transit agencies, and in-depth case studies of two transit agen- cies that have focused attention on transit asset management. An initial challenge in this work was the definition of a good transit asset management sys- tem. For this synthesis, attention was focused on the use of technical modeling approaches for: • Estimating funding needed to address ongoing and backlog replacement and rehabilita- tion needs, and • Setting priorities for the funding of SGR projects when funding is not sufficient to provide implementation of all needed projects. SUMMARY TRANSIT ASSET CONDITION REPORTING

The literature review identified a small number of publications that described approaches for estimating funding needs or setting funding priorities in constrained funding environments. Most of the literature focused on the need for SGR analysis, funding, general processes, and frameworks for conducting the analyses. The small number of relevant publications documented efforts to estimate transit capital needs at three different aggregation levels: nationally, statewide, and locally. The technical approaches described in these reports varied in several respects. • Types of Capital Asset Costs Included. Some applications only considered replacement costs as capital costs. Others included significant mid-life renewals as capital costs. One approach examined life-cycle costs that included operating and maintenance costs. • Measure of SGR. Some applications defined assets as being in a state of good repair if they were replaced before the end of their defined useful life (e.g., 12 years for buses, 25 years for rail cars, and 50 years for stations). Others used asset condition ratings (decay curves) that were adopted from an approach developed by the Chicago Transit Authority in the 1990s. Therefore, two identical assets may be scheduled for replacement at different ages based on the intensities of their use and their respective levels of maintenance. • Scenario Testing. All applications provided estimates of the capital funding needed to bring the assets to, and maintain the assets at, a state of good repair. Several applications estimated the capital costs of reaching SGR, but also maintaining (or improving) service performance in terms of passenger crowding and travel speeds as population and travel congestion increases. One application also included the ability to prioritize the funding of specific asset renewal or replacement projects in constrained funding environments where the available funding is less than what is needed to bring all assets to SGR. General findings from the survey highlighted differences and commonalities in SGR approaches and results among the nation’s 50 largest transit agencies. The transit agencies sur- veyed are primarily multi-modal transit agencies that typically operate heavy, light, or com- muter rail services, and conventional bus service. The survey focused on these agencies because it was expected that they would likely have the most advanced asset management systems owing to the complexity of their operations. The survey was sent to all 50 large transit agencies, and it generated a response rate of 82%, or 41 respondents. Virtually all respondents indicated that they maintain comprehensive inven- tories of assets that are updated regularly. A high proportion of respondents also indicated that these data are used for capital planning or development of investment strategies. Detailed survey responses received from transit agencies revealed the following about the collection and analysis of the data at these agencies: • The primary sources of the data vary among the transit agencies. Common sources include financial records (fixed asset ledgers), asset inspections, maintenance manage- ment systems, or some combination of these sources. • Although all data are maintained electronically, there are variations in how the data are stored. The most common storage packages are off-the-shelf, financial information or asset management databases, and special databases developed internally or by outside consultants. • Two of every three respondents indicated that their agencies use rolling programming cycles (e.g., 2009–2013, 2010–2014). This means that most of the large transit agencies need to make capital needs forecasts every year. • Most responding agencies determine asset condition through a combination of age and inspection results. This may mean that agencies assess the condition of selected asset cat- egories based on inspections (e.g., bridges) while relying on age for other asset categories (e.g., buses). 2

The responding transit agencies indicated that they made good use of the asset tracking and condition data. Most agencies stated that they use the age and condition data to make an assess- ment of their infrastructure needs and to support appeals for more funding. The majority of the responding agencies reported that these efforts produce good results and that their asset condi- tion systems were used to change capital funding priorities to improve their SGR. The responses suggest that the transit agencies use the transit assent condition data as another qualitative factor to be considered in the determination of investment priorities and develop- ment of capital programs. None of the responding agencies provided examples of how the data were used quantitatively to set investment priorities. Two case studies of the Massachusetts Bay Transportation Authority (MBTA) and the New York City Transit (NYCT) demonstrate that focused attention to transit asset management can improve the funding of SGR projects. At the MBTA, the funding of SGR investments increased from about 50% to almost 80% within 5 years. 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 a state of good repair. The MTA is now in its fifth 5-year capital plan and has made significant strides in restoring the agency’s assets. The use of an extensive asset inventory with condition ratings was critical to this success. 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 sce- narios 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. NYCT’s database is a good example of a detailed database. Assets such as stations are bro- ken 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 the programming of simpler actions at a higher level of asset aggregation (e.g., rehabilitation of a station). Based on the literature review, surveys, and case studies a number of suggestions are made to improve the design and use of the asset databases. The suggestions address the structure of the databases, improved analysis techniques, and use of SGR-based tools for funding prioriti- zation, and are outlined in chapter six, Conclusions. 3