Transit Asset Condition Reporting (2011)

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23 The literature review; a survey of the largest transit agencies, with an 82% response rate; and case studies yielded some key findings regarding the state of the practice in transit asset man- agement and the limitations in current methods. The results of the synthesis also suggested additional research to improve transit asset management practices. Good transit asset management can provide critical support in two key areas: 1. Establishing the level of need for infrastructure investments. A comprehensive analysis of infrastruc- ture needs can produce an estimate of the funding needed to address (1) ongoing asset replacement and rehabilitation needs, and (2) past unfunded infrastruc- ture needs (often termed backlog needs). This funding estimate and supporting documentation can provide a compelling argument and support for increased funding. 2. Programming of cost-effective investments. A sys- tematic approach that is based on good quality data and clear organizational objectives can help prioritize the programming of investment projects when available funding is constrained and not sufficient to support the implementation of all needed projects. The use of this approach will help maximize the effectiveness of local, state, and federal funding investments. The synthesis revealed the following key findings about the state of the practice at the largest transit agencies: • Most large agencies have asset tracking databases that are frequently updated and include all assets. The primary data sources vary but include financial records (fixed asset ledgers), asset inspections, main- tenance management systems, or some combination of these sources. There are variations in how the data are stored including the use of off-the-shelf, financial information or asset management databases, and spe- cial databases developed internally or by outside con- sultants. Most agencies use designated in-house staff to support and update the databases for most responding agencies. • Many transit agencies maintain separate equipment ros- ters that are independent from the mainstream planning, programming, and budgeting processes. This is done for internal maintenance management and to meet federal requirements for adequate control of grant-funded assets. Often, there is limited consideration given as to how the inventory and condition data might be integrated to sup- port short-term and strategic planning and investment policy. As a result, human interaction is needed to adapt or process the data for these activities. • Most large transit agencies determine asset condition through a combination of age and inspection results. Many agencies assess the condition of selected asset categories such as bridges based on inspections while relying on age for other asset categories. Most transit agencies are not using decay curves for assessing cur- rent State of Good Repair (SGR) or projecting future investment needs. Decay curves depict the relationship among asset condition, useful life, and maintenance practices and were initially developed by the Chicago Transit Authority. They are key elements of FTA’s Transit Economic Requirements Model (TERM). • The assessment of SGR needs has benefited many transit agencies. Most large transit agencies use inventory and condition assessment data to estimate both current and future SGR backlogs and investment needs. The agen- cies stated that their asset condition systems were used to change capital funding priorities to improve their SGR. The two case studies demonstrated that focused attention to transit asset management can improve the funding of SGR projects. • The large transit agencies do not use asset condition data to set investment priorities for capital programming. Most large transit agencies use the transit asset condition data as another qualitative factor to be considered in the determination of investment priorities and devel- opment of capital programs. This was even true for the Massachusetts Bay Transportation Authority (MBTA) application, which has the ability to prioritize the funding of specific asset renewal or replacement projects in con- strained funding environments. The current methods used by the large transit agencies sur- veyed are at an elementary level. The key issues with the meth- ods are: • The appropriate measurement of SGR using age and/or condition. • The limited estimation of benefits or consequences of alternative investments. • The absence of scenario testing for different funding levels. CHAPTER SIX CONCLUSIONS

The synthesis revealed that there has been significant dis- cussion of the appropriate measurement of SGR using age and/or condition among large transit agencies. In concept, most managers at large transit agencies believe that condi- tion is the best measure of SGR because it recognizes that the need to replace an asset in practice is related not only to age, but to other factors such as intensity of use (e.g., miles), level of preventive maintenance, and climate. However, condition measurement often incorporates on-site inspec- tions and evaluations by expert engineers—a costly ongoing expense for many transit agencies faced with tight funding. Condition measurements are most helpful for making detailed, short-term investment decisions that involve invest- ment actions for specific assets in an asset class with common characteristics (e.g., how many 14-year old buses should be programmed this year in view of other investment needs?). Age is viewed as a simple and less desirable (compared with condition) measure of SGR because it does not recognize the other factors that contribute to the physical declines of dif- ferent assets. The use of age implies it is reasonable to apply one service life for an asset type (e.g., conventional buses) in all situations. Age data have the advantages over condi- tion data of being easier to collect and maintain and to explain to decision makers. Age data may be a reasonable way to make appeals for more SGR funding because of these advantages. These advan- tages may also apply to long-term planning activities that, by nature, are willing to use more simple models of condition. Fundamentally, the tradeoffs of using age data versus condition data involve the degree of variation (variance) in replacement ages based on analysis of condition data. For example, if the analysis of condition data suggests that most buses should be replaced between 12 and 14 years, then using an age-based service life of 13 years is reasonable and saves the added costs of condition inspections. However, if this vari- ance is wider, for example 12 to 18 years, then using condition data is preferable and warrants the added costs of condition inspections. Unfortunately, the synthesis did not identify significant efforts to address the age versus condition issue. The contin- ued development of decay curves for FTA’s TERM model and other agency applications may add more insight to this issue. The estimation of the benefits (or consequences) of invest- ment decisions is seldom performed in current practice. Gen- erally, the benefits are estimated as the degree to which the SGR has been achieved (e.g., 70% of assets are in SGR, the average rating is 3.2). However, these SGR measures are really surrogates for the potential impacts of SGR investments that have real meaning to decision makers and the general public—impacts such as reduced operating costs, improved reliability, and increased safety. 24 Unfortunately, these impacts have been difficult to estimate and are not explicitly considered by most large transit agen- cies. Most agencies rely on the expert judgments of transit managers and engineers and assume that these experts weigh these factors as they define the times when assets should be replaced. The Illinois Department of Transportation effort is an exam- ple of how the impact on operating costs could be determined. The Illinois model estimated the life-cycle costs of different types of buses, including operating, maintenance, and capi- tal costs. The resulting total cost curves were used to deter- mine the service lives that minimized total costs. The data from these curves could also be used to estimate the added cost impacts of deferring bus replacements beyond these ser- vice lives. Finally, the ability to perform scenario testing is limited at most transit agencies. Most large transit agencies use inven- tory and condition assessment data to estimate the funding needed to eliminate current SGR backlogs. They also esti- mate future funding needed to maintain SGR. Both of these approaches are useful for arguing for additional funding to reach “ideal” operating environments. However, decision makers and the general public are skep- tical of these “ideal-based” funding estimates. Often, it is believed that it is not possible to provide this ideal level of funding. Instead, there is an interest in determining the impacts of lower levels of funding. Often, the discussion begins with determining the level of funding needed to halt the decline in the SGR for a transit agency. It then advances to questions about the benefits of increasing funding beyond this “SGR steady-state” funding. A methodology is needed to prioritize the funding of spe- cific asset renewal or replacement projects when these scenar- ios involve funding that is less than what is needed to bring all assets to SGR and maintain them at SGR. Only the application at the MBTA in Boston has this ability to test different fund- ing environments because it has a programming logic that can prioritize the funding of specific asset renewal or replacement projects in constrained funding environments. Although this work is widely known, managers from the large transit agen- cies expressed interest in learning more about the develop- ment, use, benefits, and limitations of this prioritizing tool. Additional research on the effective design and use of asset databases is suggested. The research might focus on the fol- lowing issues: • The structure and level of detail in effective databases. Research to define the elements of a good asset condition inventory database addressing issues such as database structure, function, data requirements, assets covered, frequency and method of updates, analytical capabilities, and helpful output reports. The potential of sharing good

25 asset inventory database software or specific database analysis modules might also be investigated. • The effective use of age and condition-based assessments of SGR for different asset types. Research to examine the degree of variation (variance) in replacement ages based on analysis of condition data. For some asset types, it may be determined that age is an appropriate measure. For other asset types, it may be determined that the added cost of condition measurements and inspections is warranted. • The estimation of the benefits (or consequences) of investment decisions. Research to examine analytical methods for estimating the potential impacts of SGR investments that have real meaning to decision makers and the general public—impacts such as reduced operat- ing costs, improved reliability, and increased safety. • The use of prioritization decision tools for examining the impacts of different SGR funding levels. Research to examine the effective design and use of such tools and their benefits and limitations.