The Map-21 authorized, and the FAST Act reauthorized FTA to develop a rule for the state of good repair program. This rule establishes a system to monitor performance, manage transit assets, increase safety and reliability, and estimate performance measures (WSDOT, 2016). Therefore, transit agencies need to develop a TAMP process per MAP-21 and FAST Act requirements to achieve a state of good repair. FTA also developed TAPT tool for transit
agencies to support the TAMP process. This TAPT tool includes four spreadsheet models which help transit agencies to predict the future conditions of their transit assets and help prioritize rehabilitation and replacement needs. The FTA’s TERM Lite can be used along with TAPT or
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without TAPT to support analysis of different investment scenarios. Furthermore, many agencies developed their decision support tools and an asset management system which can be used to support TAMP processes (Robert, William; Reeder, Virginia; Lauren, Katherine, 2014).
2.4.1. FTA’s transit economic requirements model (TERM Lite)
The FTA developed Transit Economic Requirements Model (TERM Lite) tool in 1995 to estimate transit capital needs and spent about $5 million in development and update until 2013. The TERM model measures asset condition on a 5-point scale and considers a revenue vehicle to be in a state of good repair if the condition of the vehicle reaches or above the condition rating of 2.5 (FTA, 2013; Zarembski, 2013). It estimates the state of good repair backlog, determines the capital funding levels required to achieve the state of good repair, analyze the impact of
projected future investment on capital performance, and prioritize long-term investment (Cevallos, 2016). By using TERM, the transit agencies can forecast the trend of asset
maintenance, replacement, and rehabilitation costs for the next 20-year period as well as the FTA can estimate the capital needs and develop various reports. The TERM model uses information obtained from NTD database. The asset age and physical condition for each asset category are considered as the predictor for determining the condition (Cevallos, 2016).
The TERM model can predict a current and future asset condition based on a five-point rating system as shown in Table 7 (FTA, 2010a). It uses the numerical method to rate transit asset condition based on a scale of 5.0 for excellent, 4.0 for good, 3.0 for adequate, 2.0 for marginal, and 1.0 for poor for evaluating a transit asset condition based on their age, replacement or rehabilitation history, and other factors. If the rating of the asset is at or above the condition rating of 2.5, TERM model considers it a state of good repair. Similarly, if the condition value of
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all transit assets is 2.5 or higher in a transit agency, it will be considered in a state of good repair (FTA, 2010a).
Table 7. TERM Condition Ratings
Condition Ratings Description
Excellent 5.0 to 4.8 New or like new asset
Good 4.7 to 4.0 Asset showing minimal signs of wear Adequate 3.9 to 3.0 Asset has reached mid-life
Marginal 2.9 to 2.0 Asset reaching or just past its useful life Poor 1.9 to 1.0 Asset past its useful life
Source: Adapted from FTA. 2010. National State of Good Repair Assessment. Report to Congress, U.S. Department of Transportation, Washington, D.C.: Federal Transit Administration.
2.4.2. Other analytical tools for state of good repair
Along with the TERM tool, the FTA also developed four analytical tools for transit agencies to support the TAMP process. These tools are (1) prioritization modeling tool, (2) vehicle modeling tool, (3) age-based modeling tool, and (4) condition-based modeling tool. They are described below.
1. Prioritization Modeling Tool
This tool prioritizes a series of asset rehabilitation or replacement funds and simulates the funds for ten years (Cohen & Barr, 2012). This tool provides a set of recommendations for the investment plan based on the allocated budget and prioritization index (PI) results. Even though the tool provides the straightforward approach for allocating funds for replacement and
rehabilitation based on PI, in practice higher-ranked projects with available budgets may need to be rescoped, and smaller projects need to be combined. Also, there might be a limitation of maximum and minimum spending by asset category to get a reliable solution (Cohen & Barr, 2012).
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2. Vehicle Modeling Tool
The vehicle modeling tool estimates the cost minimizing point that a bus or rail vehicle should be replaced and predicts the annual costs and prioritizes replacement of transit vehicles based on age (Cohen & Barr, 2012). It considers energy or fuel costs, rehabilitation costs, and delay costs for calculating the need for replacement or rehabilitation. Transit agencies should use this tool multiple times as the calculations are fleet specific. Therefore, the transit agencies need to develop various models for different vehicle types (Cohen & Barr, 2012).
3. Age-Based Modeling Tool
The age-based modeling tool assesses deteriorations on transit asset other than a transit vehicle over time and forecasts the annual costs of the transit agency as well as user costs of the transit asset (Cohen & Barr, 2012). It also prioritizes asset replacement based on a function of age. This tool calculates asset replacement cost and predicts when the asset will fail if it is not replaced. It is intended to use for different asset types other than vehicles. Therefore, the transit agencies should use this tool multiple times for different asset types. The age-based model may not be preferable in some complicated situation where age might be a poor predictor of an asset. However, the age-based model requires comparatively less data than the other models (Cohen & Barr, 2012).
4. Condition-Based Modeling Tool
The condition-based modeling tool uses on non-vehicle assets that deteriorate as a function of condition (Cohen & Barr, 2012). It predicts the annualized user costs of the assets to the transit agency. Using this tool, the rehabilitation or replacement actions are performed on the transit asset based on priority, and condition. This tool is intended to use for specific multiple non-vehicle assets, therefore transit agencies need to run it multiple times for multiple asset
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types. Guideway, facilities, systems, and stations are modeled using the tool. The condition- based model is preferable in a complex situation where the condition is a good predictor rather its age (Cohen & Barr, 2012).