Return on Investment in Transportation Asset Management Systems and Practices (2018)

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26 This chapter describes the purpose and context of the case studies developed by the research team. The case studies were performed to illustrate the realized benefits of TAM improvements and demonstrate applications of the ROI estimation framework detailed in Chapter 2. Three case studies were performed as part of the research. Each case study considered implementation of a TAM system in a different state DOT: 1. Implementation of a PMS in a western state, 2. Implementation of a BMS in an eastern state, and 3. Implementation of a MMS and maintenance levels of service (MLOS) approach in a southern state. The basic process followed for each case study is illustrated in Figure 3-1. Members of the research team prepared an initial questionnaire and developed an initial framework for per- forming ROI analysis. The team then met with agency stakeholders and collected available data. The team then performed the ROI analysis and detailed the results for agency and research panel review. Subsequent to receiving comments from the panel review, the ROI estimation frame- work was formalized for presentation in Chapter 2 of this report. The remaining sections of this chapter describe the results of each case study. Further details on user cost parameters used for the western state case study are detailed in Appendix D, and details on the analysis performed for the southern state case study are provided in Appendix E. In addition to the case studies, a pilot test was performed to validate the ROI guidance and calculation tool (ROI Tool). Unlike the case studies, which involved retrospective evaluations, the pilot involved a forward-looking (prospective) evaluation of a potential TAM investment. Details of the pilot are documented in Appendix F. Case Study 1: Western State Context The first case study was performed for a western state DOT that operates a highway system with more than 9,100 miles of road, 3,400 bridges, and numerous other assets. The agency uses a variety of management systems to support day-to-day management and investment prioritiza- tion for its assets, including: Deighton’s dTIMS CT for pavement management; AASHTOWare Bridge Management (BrM) software (formerly called Pontis); and SAP for maintenance man- agement. These systems, and the agency’s use of them, have evolved significantly over time. The agency’s early efforts in asset management started in 1994 with implementation of Pontis (now BrM). During the late 1990s, the agency began collecting pavement data and implementing dTIMS. Also at this time, the agency established a MLOS application for sampling conditions and helping to establish budgets for traffic and safety assets. C H A P T E R 3 Case Studies

Case Studies 27 In the early 2000s, the agency began to tailor its business processes to better incorporate con- sideration of asset management and better leverage the data and analyses produced by its man- agement systems. Also, the agency began implementation of SAP for supporting a wide range of business processes, including financial management and work order management. After SAP went into production, the agency incorporated the previously developed MLOS functionality into SAP. Also, the agency continued efforts to improve its linear referencing system (a foun- dational system for supporting asset management) and to implement improved systems and processes for other assets, such as signs and ITS assets. At the time of the case study, the agency had a relatively comprehensive set of asset manage- ment systems, though work was underway to develop improved systems for certain asset classes. Also, the agency had integrated asset management into several business processes, in particular the annual budgeting process. Analysis Scope The western state case study was focused on the agency’s investments in its PMS, and concen- trated on benefits achieved through 2012. This scope was selected because the initial investment in the PMS could be easily isolated from the agency’s other investments in asset management systems. Shortly after implementing the PMS, the agency began utilizing its results, also in a manner that could be isolated from the effects of other systems. An important change in policy that occurred following the implementation of the PMS was that, in 2002, the agency’s chief engineer required that regional engineers base at least 70% of their surface treatment projects on recommendations from the PMS. This percentage was increased to 80% in 2012. Also, in 2003 the agency implemented a policy that regions needed to spend at least 5% of their capital funds on preventive maintenance treatments, whereas previ- ously they had typically spent nearly all funds on more aggressive rehabilitation and reconstruc- tion treatments. The net effect of these changes was that, beginning in 2003, pavement engineers began making much more significant use of the system to develop project recommendations, and over time a shift occurred to increased emphasis on lower cost, less aggressive treatments to the extent that these were recommended by the PMS. Analysis Approach The western state case study analysis encompassed the following steps: Step 1: Calculating the costs of implementing and supporting PMS; Step 2: Approximating the effect of implementing the PMS by estimating the change in condi- tions that would have occurred had the PMS not been implemented and comparing that to what actually occurred during the analysis period; and Plan •Questionnaire development •Preliminary ROI framework Collect •Meet with stakeholders •Validate and organize Analyze •Application of quantitative methods •Analysis and documentation Figure 3-1. Case study analysis process.

28 Return on Investment in Transportation Asset Management Systems and Practices Step 3: Estimating the agency and user cost savings that resulted from improved conditions by estimating agency cost savings using the agency’s cost data and user cost savings using models extracted from HERS-ST. PMS Implementation Costs Table 3-1 summarizes the agency’s costs by year in current and constant 2012 dollars. The table includes an estimate of PMS implementation costs by year, including central office and regional staff costs (including overhead costs), software licensing costs, and software support. These values are provided in year-of-expenditure dollars (current dollars), then converted to constant dollar estimates (in 2012 dollars) based on the average inflation rate of 2.5% observed over the analysis period based on the Consumer Price Index (CPI). As indicated in the table, the initial cost of licensing the PMS and obtaining needed hardware was $454,800. The agency upgraded the software in 2004 and 2011. The total cost of the PMS, including licensing costs, software support, and consultant costs, was approximately $1.7 mil- lion. However, costs for staff time and data collection are much greater than this. The total cost over the analysis period including all cost components was $12.6 million, which is equivalent to $17.3 million in 2012 dollars. PMS Implementation Effects The basic effect of implementing the PMS was that beginning in 2003 the agency’s new poli- cies required pavement project programming to better reflect PMS recommendations, which forced a change in the types of projects programmed relative to prior years. Specifically, regional engineers began programming more preservation treatments (e.g., thinner asphalt overlays) and fewer rehabilitation and construction projects. Where applied correctly, such treatments have the potential to extend the life of the pavement and forestall the need for more costly work. The Year Staff Data Collection Hardware & Software Software Support Consulting Total Total (2012 $) 1999 $408,415 $268,637 $454,800 $20,000 $170,000 $1,321,852 $2,461,157 2000 416,585 282,069 0 12,126 0 710,780 1,259,183 2001 424,919 296,172 0 13,725 0 734,816 1,238,596 2002 433,417 310,981 0 13,900 0 758,298 1,216,153 2003 442,087 326,530 0 13,900 0 782,517 1,194,096 2004 450,931 342,857 35,000 14,750 0 843,538 1,224,750 2005 459,951 360,000 0 15,500 0 835,451 1,154,147 2006 469,151 374,667 0 16,800 0 860,618 1,131,222 2007 478,533 389,334 0 20,800 0 888,667 1,111,409 2008 488,106 404,000 0 28,560 0 920,666 1,095,555 2009 431,603 420,000 0 31,260 0 882,863 999,592 2010 440,237 436,000 0 34,200 0 910,437 980,791 2011 449,043 452,000 180,793 0 76,450 1,158,286 1,187,243 2012 458,021 468,000 0 0 83,150 1,009,171 1,009,171 Total $6,250,999 $5,131,247 $670,593 $235,521 $329,600 $12,617,960 $17,263,065 Table 3-1. PMS implementation costs by year, 1999–2012.

Case Studies 29 policy change to establish minimum funding levels for preventive maintenance further solidi- fied this change in direction. Note that this change impacted projects performed by contractors: before and after the change, the agency performed additional pavement maintenance work that was not specifically simulated in the PMS but was presumably reflected in the system’s deterio- ration models. The conditions that resulted from implementation of the PMS and related policies may be readily observed, but the conditions that would have resulted had the system not been imple- mented can only be estimated. To estimate the conditions that would have been observed had the PMS not been implemented, the research team first used agency documents on pavement decision trees, deterioration models, and treatment costs to simulate two scenarios illustrated in Figure 3-2: one in which treatments are performed as recommended by the PMS (termed actual practice); and one in which the set of treatments is restricted to allow only rehabilitation and reconstruction, consistent with business practices prior to implementation of the PMS and related policies (termed no PMS). The actual practice case was simulated with a constant dollar budget of $150 million, comparable to the level of funding during the analysis period. The budget for the no PMS case was varied to determine the budget required to achieve the same conditions as those observed for the actual practice case, yielding the result that $19 million more would be needed per year to achieve the same conditions had the PMS not been implemented. Agency staff indicated that, in the absence of the PMS and related programming policies, the agency would likely have spent approximately the same amount of money but then observed worse conditions over time. Thus, the next step of the analysis was to use historic PMS runs to estimate the effects of having an effective budget reduction of $19 million per year. A set of dTIMS runs performed by the agency from 2004 was used for this purpose. Figure 3-3 shows results for 2004–2012 for a high budget scenario, a low budget scenario, and actual con- ditions. The high budget scenario was run with a budget of $300 million per year and the low budget scenario was run with a budget of $143 million per year (close to actual spending). Both scenarios assumed 3.5% annual inflation. Construction inflation during this period was approxi- mately 5.1%, based on this state agency’s construction cost index. Thus, the high budget scenario represents an average annual budget of $345 million in constant 2012 dollars, and the low budget scenario represents an average annual budget of $165 million. Figure 3-3 shows model results that were used as a basis for estimating changes in conditions for the PMS implementation scenario. Based on comparisons of the actual and predicted results, the research team judged that the model results shown in Figure 3-3 provide a reasonable basis for estimating changes in conditions for alternative scenarios, provided these are expressed in terms of an effective change in annual budget. Table 3-2 details the calculation of percent of pavements in good/fair condition given an effective budget reduction of $19 million per year from not implementing its PMS, and developed by interpolating between the high and low budget results. As shown in the table, approximately 42.5% of this state DOT’s pavements would have been in good or fair condition in 2012 had the agency experienced an effective budget reduction of $19 million each year, a reduction of 1.5% relative to that actually observed. PMS Implementation Benefits The next step in the analysis was to convert the estimated effect of implementing the PMS and related programming policies (an improvement in the percentage of pavements in good/fair condition of 1.5%) to an agency and user cost benefit. The agency benefit of this change is that the remaining asset value, or residual value, of this agency’s pavement in 2012 was greater than it would have been without the PMS. The size of this benefit can be approximated by determining the cost of increasing the percentage of pavement in good/fair conditions by 1.5% as of the end

30 Return on Investment in Transportation Asset Management Systems and Practices 0 2 4 6 8 10 12 0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000 140,000,000 160,000,000 1 2 3 4 5 6 7 8 9 RS L in Y ea rs 0 2 4 6 8 10 12 RS L in Y ea rs Co st ($ ) Year Expenditures and RSL: Actual Practice 0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000 140,000,000 160,000,000 180,000,000 1 2 3 4 5 6 7 8 9 Co st ($ ) Year Expenditures and RSL: No PMS Preservation Rehab/Recon RSL Preservation Rehab/Recon RSL RSL = remaining service life Figure 3-2. Predicted costs and conditions for the actual practice and no PMS cases. of the analysis period. All things being equal, one would expect this value to be similar to the effective difference in agency investments over the analysis period ($171 million). This agency’s 2012 estimate for chip sealing pavement, the lowest cost treatment for improving pavement condition, was $7.5 per square yard, or $52,800 per lane-mile. Based on this estimate, the increased residual value from improving pavement condition by 1.5% is approximately $18.2 million each year (the cost of chip sealing 1.5% of this western state’s 23,024 lane-miles of pavement). The fact that the total estimated residual value ($18.2 million × 9, or $164 million) is comparable to the effective budget savings ($171 million) makes intuitive sense and provides some indication that the approximation is a reasonable one.

Case Studies 31 40.00 45.00 50.00 55.00 60.00 65.00 2004 2005 2006 2007 2008 2009 2010 2011 2012 Pe rc en t in G oo d/ Fa ir C on di ti on Low Budget High Budget Actual Figure 3-3. Percentage of pavements in good or fair condition for different scenarios. Description Units Value Notes Low Budget High Budget Total spending, 2004– 2012 2012 dollars $1,483 million $3,111 million Pavements in good/fair condition in 2012 % of pavements in good/fair condition 42.5% 56.9% Change in pavement condition per change in budget Change in % of good/fair pavements per $ million of spending from 2004–2012 0.009% Obtained by dividing difference in condition by difference in budget Effective change in budget from not implementing PMS 2012 dollars -$171 million Obtained by subtracting $19 million each year, 2004 through 2012 Effective change in condition from not implementing PMS % of pavements in good/fair condition -1.5% Obtained by multiplying 0.009% per million × $171 million Table 3-2. Predicted change in conditions in the absence of a PMS.

32 Return on Investment in Transportation Asset Management Systems and Practices In addition to the agency benefit described above, road users were anticipated to have experi- enced additional benefits from driving on roads in better condition than they would have been otherwise. To calculate this user benefit, 2012 data was used to calculate the average difference in the International Roughness Index (IRI) between pavements in good/fair condition and pave- ments in poor condition. This calculation showed that the average IRI for good/fair pavements was 93, versus 120 for poor condition pavements, a difference of 27 points. Models extracted from HERS-ST were used to calculate the impact on user costs of increasing IRI by 27 points from 93 to 120. Specific parameter values used for the analysis are documented in Appendix D. These parameter values were used to estimate increased user costs over a range of functional classifications and traffic levels. In summary, the analysis showed that increasing IRI from 93 to 120 increases costs for autos by approximately $0.01 per vehicle-mile, and increases cost for trucks by $0.03 per vehicle-mile. This state DOT estimates that vehicle-miles traveled (VMT) on its network total 76,945,000 daily for autos and 6,740,000 for trucks, so the total user benefit is approximately $5.3 million per year. This is calculated by converting the daily VMT to an annual value, multiplying this by the percentage of the network impacted (1.5%), and further multiplying this by the unit cost change ($0.01 for autos, $0.03 for trucks). Results Table 3-3 summarizes the overall results of the analysis. Annualized costs and benefits were calculated by treating the NPV as an annuity with a payout rate equal to the discount rate. As detailed in the table, the NPV of user benefits is $56.1 million and the NPV of the increased residual value is $182.4 million, bringing the total benefit to $238.5 million, or $9.54 million on an annualized basis. The NPV of agency costs of PMS implementation is $23.2 million. Based on these figures, the benefit-cost ratio (BCR) of PMS implementation is approximately 10.3, calculated by dividing the total benefit of $238.5 million by the total cost of $23.2 million. The figures shown for NPV were calculated based on a discount rate of 4%. It was not possible to calculate the IRR in this case without details on the time stream of benefits realized. However, if the investment is treated as a one-time investment of $23.2 million (NPV of costs) followed by an annual payout of $8.61 million (annualized benefits), then the investment in PMS would be deemed to have an annual return of 41%, calculated by dividing the annual benefit of $9.54 million by the NPV of costs of $23.2 million. The largest component of the benefit is the increased residual value of pavement as of the end of the analysis period; however, the investment in the PMS is demonstrated to be beneficial even if this benefit is set aside and the analysis is limited to user benefits. In the latter scenario, the BCR is 2.4 and the ROI is 10%. Value (2012 $ Million) Description Total NPV Annualized Agency costs 17.3 23.2 0.93 User benefits 47.7 56.1 2.24 Increased residual value 182.4 182.4 7.30 Total benefit 230.1 238.5 9.54 Net benefit 212.8 215.3 8.61 Table 3-3. Western state DOT case study analysis results.

Case Studies 33 This analysis is subject to a number of assumptions and qualifications, including the following: • The benefits of implementing the PMS largely stem from the agency’s policy-driven shift to an increased emphasis on preservation treatments as opposed to more expensive rehabilita- tion and reconstruction treatments. The analysis assumes that implementation of the PMS implies adoption of new programming policies in support of the PMS. It seems clear that data from the PMS helped facilitate this shift in programming decisions, but it is plausible that the agency may have programmed more preservation treatments from 2004–2012 than it had historically, even if the agency did not implement a PMS. • The analysis approach of calculating the effective budget increase from performing recommended preservation work was designed to leverage historic analyses performed by this state agency. In an ideal case, one would model the impacts of PMS implementation more explicitly, such as by simulating conditions and expenditures with two distinct pavement treatment strategies. • The analysis assumes that, in the absence of a PMS, no preservation work was being done. The agency did not require preservation work until adopting the new policy in 2003. However, it is possible that some preservation work may have been performed prior to the implementation of the PMS and the related policy. If preservation work was already being performed in the absence of a PMS, it would affect the calculation of the potential benefits of adopting the PMS. • The agency’s pavement deterioration models implicitly assume some amount of work is performed by maintenance forces; however, neither maintenance work nor pavement work accomplished through expansion projects is explicitly considered in the analysis. • Like other state DOTs, this state DOT must collect some amount of pavement data simply to support federal reporting requirements. No attempt was made in the analysis to distinguish between investments required for federal reporting and investments required for other purposes. • Agency staff reported significantly less staff travel time and expense resulting from PMS implementation, as pavement managers could often obtain necessary data through the PMS rather than through field visits. Staff were unable to accurately quantify these savings but esti- mated them at hundreds of thousands of dollars annually. These reported cost savings were not included in this analysis. • Although the PMS appears to be beneficial, overall pavement conditions worsened over the analysis period, and user costs went up. Case Study 2: Eastern State Context The second case study was performed for an eastern state DOT that is responsible for more than 163,000 lane-miles of pavement and more than 13,500 bridges. Like many other state DOTs, historically the agency focused on system expansion, committing up to half of its funding to new construction and expansion projects. As its road system has aged, however, the DOT has shifted its focus, increasing funding for maintenance and rehabilitation of existing roads and bridges. This DOT’s early asset management work focused on bridge management. The agency devel- oped a mainframe BMS in the mid-1980s that predated commercially available systems. The sys- tem helped this DOT to manage its bridge inventory, and provided functionality for analyzing bridge investment needs. During the late 1990s, the agency began improving its approach to main- tenance management, culminating in implementation of a new MMS in 2003. Subsequently, the agency upgraded its other asset management systems, implementing a new PMS in 2008 and new BMS, the Agile Assets BMS, in 2009. In parallel with implementing Agile Assets, the agency developed a new system for bridge inventory and inspection data collection. Thus, the BMS was used primarily for analysis rather than for supporting field data collection. Agency staff reported that the new BMS was

34 Return on Investment in Transportation Asset Management Systems and Practices immediately useful for helping make the case for increased funding for bridge preservation. Historically, the agency used its capital funds for bridges primarily to replace bridges classified as structurally deficient (SD). State funds totaling approximately $70 million per year were used for bridge maintenance. The agency used BMS analyses to make the case for increased funding, particularly for performing preservation projects such as deck overlays or bridge painting that could extend bridge life and forestall the need for replacement. Beginning in 2009, the agency began using $10 million per year of its federal bridge funds for bridge preservation. In 2011 the DOT received an additional one-time allocation of $100 million with flexibility for using this funding for preservation. Analysis Scope The eastern state case study focused on the agency’s 2009 investment in a new BMS, separate from the agency’s investment in a new inventory and inspection system. Agency staff credited the implementation of the BMS in 2009 for the decision to shift funding to bridge preservation, includ- ing $10 million annually of federal funds and approximately $100 million of additional state funds that was allocated on a one-time basis. In theory, focusing investments in bridge preservation can potentially save agency and user costs over the long term by allowing the bridge owner to perform work to extend the life of a bridge before it requires an even more costly replacement. Thus, the research team compared actual conditions and spending from 2009–2013 to predicted conditions and spending in an alternative scenario that capped funding for maintenance and preservation- related work at $70 million per year. The benefit of BMS implementation is approximated by the difference in agency and user costs for these two scenarios. Separately from the analysis for this case study, the agency evaluated the implementation of its internally-developed bridge inventory and inspection data collection system, and concluded that it yielded the following types of benefits: • Increased inspection efficiency, accuracy, and consistency of the bridge inspection process; • Elimination of paperwork, which allowed inspectors to spend more time on inspections; • Immediate availability of inspection reports, which allowed analysts to view reports sooner and react faster to bridges that need repairs; • Digital storage of reports, which allowed multiple people to view reports at the same time, improving quality control. Analysis Approach This case study analysis encompassed the following analyses: • Calculating the costs of implementing and supporting BMS; • Simulating two scenarios for the period from 2009–2013, using FHWA’s National Bridge Investment Analysis System (NBIAS): one scenario reproducing actual conditions, and a second scenario simulating situation in which preservation spending is limited to no more than $350 million over the 5-year period (or $70 million per year); and • Comparing the two scenarios to determine the agency and user benefits yielded by the BMS for the period 2009–2013. BMS Implementation Costs Table 3-4 summarizes the agency’s costs by year in current dollars and constant 2012 dollars. The table includes an estimate of BMS implementation costs by year, including licensing and configuration costs, maintenance costs, and software support costs. The support costs include

Case Studies 35 estimations based on an assumed number of support hours across multiple systems, and may be overstated for BMS. Staff costs are not included, but they are expected to be low given that the system is an analysis tool used primarily by the state’s central office staff on a part-time basis. These values are provided in year-of-expenditure dollars (current dollars) and then converted to constant dollar estimates (in 2012 dollars) based on the average inflation rate of 2.1% observed over the analysis period, based on the CPI. The costs appear to have lagged behind implementation dates by a year. Staff reported imple- menting the BMS in 2009, but the cost was shown as incurred in 2010. Thus, maintenance and support costs for 2014 are shown so that costs for a full 5-year period were captured despite this lag. Table 3-4 summarizes the costs by year in current and constant 2012 dollars. As shown in Table 3-4, the initial cost of licensing the BMS was approximately $2 million. Maintenance and support costs began 2 years after the BMS was implemented, with total main- tenance and support costs ranging from about $264,000 to about $384,000 per year. The total cost over the analysis period including all cost components was $2.86 million, which is equiva- lent to $2.92 million in constant 2012 dollars. Simulation of Agency Investments The research team used FHWA’s NBIAS Version 4.1 to simulate bridge conditions for two scenarios: actual spending and reduced preservation. NBIAS is FHWA’s system for analyzing national-level bridge needs. Using a modeling approach originally adapted from the Pontis BMS, NBIAS models deterioration and maintenance, repair, and rehabilitation (MR&R, also called preservation) at the element level. NBIAS also considers needs for functional improve- ments on a bridge. Improvements modeled include widening of existing lanes, raising a bridge, and strengthening a bridge. Bridge replacement may be triggered by an improvement need if multiple functional needs exist, if it is infeasible to address a need without replacing the bridge, or if replacement is the most cost-effective option. To run the scenarios for the case study, an NBIAS 4.1 database was created using FHWA defaults for all parameters and loaded with the state DOT’s 2008 NBI data, filtered to include only state-owned bridges. The system was then run for a 5-year period covering 2009–2013 using the dollar amounts that reflected the agency’s actual bridge spending. For the actual spending scenario, the agency’s unit costs in the system were adjusted such that simulated conditions approximated actual conditions. Then, to create the reduced preservation scenario, the weight on user costs relative to agency costs was increased such that preservation spending was limited to a total of $350 million over the 5-year period (an average of $70 million per year). Year Cost (Current $) Total (2012 $) Licensing & Configuration Maintenance Software Support Total 2009 0 0 0 0 0 2010 1,904,640 0 0 1,904,640 1,985,475 2011 0 0 0 0 0 2012 0 36,205 228,326 264,531 264,531 2013 0 60,707 247,114 307,821 301,490 2014 0 61,314 322,753 384,067 368,430 Total 1,904,640 158,226 798,193 2,861,059 2,919,926 Table 3-4. BMS implementation costs by year, 2009–2014.

36 Return on Investment in Transportation Asset Management Systems and Practices Preservation and functional improvement projects both yield a mix of agency and user ben- efits; however, the preservation work predominantly yields agency benefits, whereas the func- tional improvement projects tend to yield greater user benefits. Version 4.1 of the NBIAS does not allow for budgets by type of work, but adjusting the weight on user costs approximates this functionality. In both scenarios, the system simulated spending of $2 billion over 5 years, with budget levels starting at approximately $390 million per year and increasing to $580 million in the final year. In the actual spending scenario, MR&R spending was $436 million, versus $350 million in the reduced preservation scenario. Scenario Comparison The last step of the analysis was to compare the scenarios run in NBIAS. Figures 3-4 and 3-5 show how predicted conditions differ between the scenarios. Figure 3-4 shows the percentage of state-owned bridges classified as SD, both based on actual data and based on the two scenarios. The simulated actual spending scenario closely matches the actual data, and at the end of this scenario approximately 11% of state-owned bridges are classified as SD, a reduction from 14% in 2008. In the simulated reduced preservation scenario, the percentage of SD bridges rises to 21% in 2013, a difference of 11% relative to the simulated actual spending scenario. Figure 3-5 shows conditions in terms of Health Index, a measure of bridge condition that ranges from 0 (worst condition) to 100 (best condition). Figure 3-5 illustrates a small increase in the Health Index (improvement in conditions) for the simulated actual spending scenario and a reduction in this measure for the simulated reduced preservation scenario. For 2013, the predicted Health Index is 92.95 for the simulated actual spending scenario, and 90.42 for the simulated reduced preservation scenario, a difference of 2.5 points. The change in condition shown in the figures is an approximation of the increased residual value of agency investment in bridge preservation at the end of the actual spending scenario relative to the simulated reduced preservation scenario. Table 3-5 summarizes the calculation of increased residual value using the Health Index changes as the basis of the calculation. As shown in the table, if this value is multiplied by the replacement value of the agency’s bridge network, 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 2008 2009 2010 2011 2012 2013 % B ri dg es C la ss ifi ed a s SD Year Actual Simulated Actual Spending Simulated Reduced Preservation Figure 3-4. Actual and predicted percentage of bridges classified as SD by year.

Case Studies 37 the increased residual value is calculated as $283.9 million in constant 2012 dollars. The NPV of this total, normalized to 2012 using a discount rate of 4%, is $273.0 million. Another important measure predicted by the system is user benefit from the simulated work. User benefits predicted by the system total $1,767.2 million for the actual spending case and $1,868.4 for the reduced preservation case, a difference of $101.2 million. This reduction in user benefit predicted for the actual spending case results from diversion of funds to bridge preservation that could otherwise be used for functional improvements. The NBIAS model predicts higher user benefits for rehabilitation and replacement than for preservation work. Conversely, the NBIAS predicts higher agency benefits for preservation work than for reha- bilitation and replacement. The values in the NBIAS model are based on observed benefits, and also are the reason that shifting to preservation work from rehabilitation and replacement results in a decrease in user benefits and an increase in agency benefits (shown as an increase in residual value). 80.0 82.0 84.0 86.0 88.0 90.0 92.0 94.0 96.0 98.0 100.0 2008 2009 2010 2011 2012 2013 He al th In de x Year Simulated Actual Spending Simulated Reduced Preservation Figure 3-5. Predicted Health Index by year. Description Units Value Deck area for state-owned bridges Millions of square feet 92.66 Average replacement cost 2012 $ per square foot 121 Replacement value 2012 $ million 11,221 Health Index Change, Actual Spending – Reduced Preservation Health Index (0–100 scale) 2.5 Increased residual value 2012 $ million 283.9 NPV of increased residual value 2012 $ million 273.0 Table 3-5. Calculation of residual value.

38 Return on Investment in Transportation Asset Management Systems and Practices It is significant that the user benefits predicted by NBIAS are 1-year benefits, the benefits of performing recommended work in a given year relative to deferring work for a year. To obtain total user benefits obtained over time, it is important to sum the benefits over the life of a project. These cumulative benefits can be approximated over a short period by adding the benefits in a given year to the benefits for subsequent years. Table 3-6 details how the difference in total benefits of $101.2 million (in constant 2012 dollars) was distributed over time and converted into a cumulative value to obtain cumulative user benefits obtained over the analysis period of the case study. As shown in Table 3-6, once adjusted, the user benefits total approximately $202 million in constant 2012 dollars. The NPV of this total, normalized to 2012 using a dis- count rate of 4%, is approximately $201 million. Results Table 3-7 summarizes the overall results of the analysis. The figures shown for NPV were calculated based on a discount rate of 4%. The annualized costs and benefits were calculated by treating the NPV as an annuity with a payout rate equal to the discount rate. As detailed in the table, the NPV of benefits, including reduced user benefits and increased residual value of the bridge network, is $71.7 million ($2.87 million on an annualized basis). The NPV of agency costs of BMS implementation is $3.0 million. Based on these figures, the BCR of BMS implementation is approximately 24, calculated by dividing the NPV of total benefit of $71.7 million by the NPV of total cost of $3.0 million. With- out details on the time stream of benefits realized in this case, it was not possible to calculate the IRR; however, if the investment is treated as a one-time investment of $3.0 million (NPV of Year Approx. Percentage of Additional Preservation Funds Value (2012 $ Million) Change in User Cost NPV Total 1-Year Value Cumulative 2009 6.7 -6.7 -6.7 -7.6 2010 6.7 -6.7 -13.5 -14.6 2011 6.7 -6.7 -20.2 -21.1 2012 40.0 -40.5 -60.7 -60.7 2013 40.0 -40.5 -101.2 -97.3 Total 100.0 -101.2 -202.4 -201.3 Table 3-6. Calculation of cumulative change in user benefits. Description Value (2012 $ million) Total NPV Annualized Agency costs 2.9 3.0 0.12 User benefits -202.4 -201.3 -8.05 Increased residual value 283.9 273.0 10.92 Total benefit 81.5 71.7 2.87 Net benefit 78.6 68.7 2.75 Table 3-7. Eastern state DOT case study analysis results.

Case Studies 39 costs) followed by an annualized net benefit of $2.75 million, then the investment in BMS would be deemed to have an annual return of 92%, calculated by dividing the annualized net benefit of $2.75 million by the NPV of costs of $3.0 million. The largest component of the benefit is the increased residual value of bridges by the end of the analysis period. Absent this benefit the net effect of implementing the BMS is negative, under the assumption that the same amount of money would have been spent absent the BMS, but a portion of the spending would have been redirected from bridge preservation to replacement. This analysis is subject to a number of assumptions and qualifications, including the following: • The benefits of implementing the BMS largely stem from the agency’s increased emphasis on bridge preservation work, as opposed to more expensive bridge replacement. Agency staff largely attributed this shift to the implementation of the BMS, but strictly speaking it is not necessary to implement a BMS to begin programming bridge preservation work. Likewise, absent continuing investment in preservation, the analysis would not show further benefits of BMS implementation beyond that predicted here. On the other hand, if use of the BMS helped increase total investment, the approach used here may understate the benefits of implement- ing the BMS. • The results are extremely sensitive to the calculation of residual value. Of the different approaches, the approach used to multiply total replacement value by change in Health Index is most conservative (in this case, yielding the lowest estimate of increased value). Other approaches that yield much higher estimates of increased residual value include using the change in predicted SD bridges (multiplied by total replacement value) or the change in investment need. As a weighted average of bridge conditions, the Health Index tends to be less volatile than the percent SD. Given that SD status is binary (a bridge either is or is not SD), it is more difficult to predict percent SD than Health Index in NBIAS. • It is not known how well the projects simulated by NBIAS relate to actual projects pro- grammed by the state DOT. A more refined estimate of benefits could be obtained by running simulations using the projects that were actually performed. • The costs of staff time for implementing the BMS are not included. Incorporating these costs would reduce the predicted net benefit of system implementation. Case Study 3: Southern State Context The southern state DOT associated with this case study is responsible for a system of more than 14,500 miles (nearly 30,000 lane-miles) of roadway that includes asphalt, concrete, double bituminous surface treatment (DBST), and gravel surfaces. The system also includes nearly 140,000 mowable acres of roadside vegetation, 56 million linear feet of drainage, and numerous bridges. As of the fiscal year ending in 2014, the DOT’s maintenance budget was approximately $120 million. This budget addressed roadway, roadside, drainage, bridge, and traffic service maintenance activities in six districts statewide. In the mid-2000s, the DOT began implementing a new maintenance management approach, shifting from a focus on tracking maintenance activities to measuring the level of service (LOS) of different roadway features. The LOS rating system implemented by the DOT shows average asset conditions by district and road class using technical LOS measures (e.g., number of pot- holes per lane-mile) and assigned LOS letter grades or classes (A through F for each technical measure, with A as the best measure and F as a failing grade). LOS measurements began in 2007, and in 2010 the DOT implemented a new TAM system supporting this approach.

40 Return on Investment in Transportation Asset Management Systems and Practices The LOS approach and the new system were implemented to support a variety of performance- related tasks that included the following: • Tracking system condition and performance to develop needs-based estimates; • Prioritizing maintenance needs; • Providing an improved basis to support budget requests and allocate resources among activities and districts; • Showing the relationships between LOS and costs; and • Supporting communication and reporting. Using LOS ratings, the costs and budget can be allocated according to current LOS conditions and LOS targets, helping provide the agency an improved basis for allocating resources. To support the new TAM system, the agency annually collects data for approximately 2,400 random sample inspections of roadways and rights-of-way. The data are used to produce a report card called the Maintenance Summary and to help set future targets and establish budgets by maintenance activity category. Analysis Scope and Results Before implementing its TAM system, this DOT commissioned a study by a consultant to estimate the BCR of the new system. Because a detailed BCA had already been conducted on the system, the research team decided to illustrate a different ROI approach for this case study rather than try to reproduce or critique the prior analysis. In this case study, therefore, the research team used the time series approach for estimating benefits. The time series approach is infrequently found in the literature. This approach requires sufficient historic data on costs and investment effects, but potentially yields a result without supplemental simulation or other analyses such as those performed for the other case studies. The model was formulated to predict maintenance LOS by category and district as a function of spending by category and district, as well as based on the presence or absence of the new system, and based on temperature changes. The southern state case study included a time series analysis of maintenance expenditures and LOS using data reported in the DOT’s annual Maintenance Summary reports for 2007–2014. The research team applied statistical regression approaches to study the effects and benefits of investing in TAM for the DOT. Descriptive statistics, data visualization, and simple regression models were used to explore and understand the trends and relationships contained in the data. A multiple linear regression model was developed based on the deviation of current LOS condi- tions from the LOS targets reported in the Maintenance Summary reports. The available data did not allow the research team to estimate the dollar value of the benefits; however, the regression results provide evidence that implementation of the new TAM system resulted in more cost-effective management of LOS maintenance conditions, and the approach used for the case study may be applicable for other TAM investment analyses where sufficient data are available. Appendix E details the analysis.