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Corporate investment decision: a review of literature.
- What are the major determinants of corporate physical investment decisions?
2. Theoretical Review
2.1. neoclassical theory of investment.
- No uncertainty exists in the market;
- The enterprises are operating in full perfect competition;
- There exists a maximum employment rate in an economy;
- There is an efficient financial market that can offer loans to the industrial sector at given interest rates;
- Corporate firms are able to maximize the net present value of present and future cash flows.
2.2. Accelerator Theory of Investment
2.3. q theory of investment, 2.4. the internal funds theory of investment, 3. material and methods, 4. conclusions and implications, author contributions, data availability statement, conflicts of interest, declaration.
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Share and Cite
Farooq, U.; Tabash, M.I.; Al-Naimi, A.A.; Drachal, K. Corporate Investment Decision: A Review of Literature. J. Risk Financial Manag. 2022 , 15 , 611. https://doi.org/10.3390/jrfm15120611
Farooq U, Tabash MI, Al-Naimi AA, Drachal K. Corporate Investment Decision: A Review of Literature. Journal of Risk and Financial Management . 2022; 15(12):611. https://doi.org/10.3390/jrfm15120611
Farooq, Umar, Mosab I. Tabash, Ahmad A. Al-Naimi, and Krzysztof Drachal. 2022. "Corporate Investment Decision: A Review of Literature" Journal of Risk and Financial Management 15, no. 12: 611. https://doi.org/10.3390/jrfm15120611
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Return on Investment in Transportation Asset Management Systems and Practices (2018)
Chapter: appendix a - literature review.
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.
111 A P P E N D I X A Literature Review This appendix details findings of a literature review conducted in 2015 to review literature related to calculating ROI of TAM investments. Appendix B includes an annotated bibliography detailing the materials that were reviewed. REVIEW APPROACH The review yielded a number of documents describing the benefits of TAM systems and procedures in qualitative terms and providing guidance to agencies seeking to implement them. Table A-1 includes examples of this guidance. Table A-1. Qualitative Terms and Guidance for TAM. Source Summary American Association of State Highway and Transportation Oï¬cials (AASHTO), Transportation Asset Management Guide: Volume 1 (1) This guidance helps agencies understand how they might beneï¬t from TAM in four areas: (1) setting policy goals and objectives, (2) planning and programming, (3) program delivery, and (4) information and analysis. AASHTO, Transportation Asset Management Guide: A Focus on Implementation (2) This guidance describes practical steps for implementing a TAM plan, including speciï¬c processes and tools for service planning, life cycle management and asset preservation, and TAM integration. It provides an extensive discussion of key considerations in implementation and how TAM investments can improve agency decisions and the quality of information available to all stakeholders. AASHTO, Pavement Management Guide: 2nd Edition (3) This guide helps agencies address the issues and challenges associated with managing pavements eï¬ectively, including assessing funding needs for pavement preservation and rehabilitation, and setting attainable pavement-related performance goals. The Guide discusses the use of pavement management at the project, network, and strategic levels and the beneï¬ts of using pavement management to support agency decisions. (Continued on next page)
112 Return on Investment in Transportation Asset Management Systems and Practices While these references provide extensive information on the benefits of TAM in qualitative terms, there is relatively little information on how to quantify the benefits of TAM systems and procedures and measure the return on investments in them. Thus, the research team performed a literature review focused on documents published during the last decade on estimating the return on investments in systems and procedures supporting TAM. In conducting this literature review, the following resources were used: Publications of FHWA and other federal agencies; Publications of state departments of transportation (DOT) and other state and regional agencies, as appropriate; University Transportation Centers of Excellence publications and graduate dissertations related to maintenance and asset management; Transportation Research Board (TRB) publications, including the Transportation Research Record, Transportation Research Circulars, and reports and syntheses prepared for NCHRP and the Strategic Highway Research Program (SHRP); TRB Transportation Research Information Services (TRIS) online database; WorldCat online database; International agencies and associations known to be conducting work relevant to the objectives of this project; Web searches on common search engines, including Google, Bing, and Yahoo. LITERATURE REVIEW The research team identified over 35 studies relevant to estimating the ROI of asset management implementation. These studies are summarized in an annotated bibliography in Appendix B. The team characterized the methods and measures used in the studies and identified common themes to facilitate the development of an ROI framework described in Chapter 2. In the following sections, Table A-1 (Continued). NCHRP Synthesis 397: Bridge Management Systems for Transportation Agency Decision Making (4) This synthesis describes methods for measuring bridge condition and performance, identifying bridge needs, establishing funding levels, comparing bridge needs with needs in other areas (pavement, safety, etc.), allocating funds, and prioritizing projects. It discusses the role of automated bridge management systems in planning, programming, resource allocation, and budgeting. Federal Transit Administration (FTA), Asset Management Guide (5) This guide provides a framework for managing transit assets and a description of its beneï¬ts to transit agencies. The beneï¬ts include improved customer service, improved productivity and reduced costs, optimized resource allocation, and improved stakeholder communications.
Appendix A 113 the recent literature on the quantification of transportation asset management is summarized according to: Quantitative Measures Used o Agency Data Collection, Processing, and Analysis Cost Savings o Reduced Expenditures for Asset Maintenance, Rehabilitation, and Replacement o Asset Condition o User Benefits Basis for Quantitative Analysis o Time Series o Controlled Field Experiments o Simulation o Breakeven Analysis The emphasis of this summary is on methods and measures, so most studies are mentioned multiple times depending on the approaches used in the studies. QUANTITATIVE MEASURES USED Agency Data Collection, Processing, and Analysis Cost Savings Demonstrating that the cost of a proposed TAM action will be more than offset by future savings in agency costs for data collection, processing, and analysis provides a strong basis for advocating the action. Such cost comparisons typically rely on an understanding of how data processes will operate with and without the TAM action, rather than simulation models or time series analyses. However, most TAM actions do more than just reduce data costs; they can provide improved data quality, better predictions of asset condition, better tools for project selection, and improved capabilities for communicating with stakeholders. Several of the documents covered in this review reported savings in agency costs for data collection, processing, and analysis: De la Garza (6) studied implementation of IP-S2 mobile mapping technology for highway asset condition assessment. Staff time requirements for data collection, processing, and analysis using IP-S2 mapping at high speed and low speed were compared with traditional methods. Babinski (7) studied the development and use of geographic information systems (GIS) by King County in Washington State. The study covered GIS produced outputs for the Waste Water Treatment Division, other divisions of the Department of Natural Resources and Parks, Department of Transportation, Department of Assessment, and several other smaller departments. Based on a survey of 175 GIS professionals and users, the study estimated the number of output units that would be produced and the cost per output unit with and without GIS. NCHRP Report 800 (8) describes a case study in which reductions in staff time requirements for adding agency-wide geospatial capabilities for program development were estimated. Schiffer (9) explored options for implementing a barcode inventory system to track fixed assets for the Arizona DOT. Based on a pilot implementation, the report estimated reductions in
114 Return on Investment in Transportation Asset Management Systems and Practices person-hours required for physical inventory collection, acquisition cost for hardware and software, and annual maintenance expense. In a study for Michigan DOT, Dye (10) examined remote technologies available for use in collecting data about attributes of 27 asset classes. Alternative technologies were compared in terms of data collection cost per mile. Yen (11) examined seven options for vehicle-mounted mobile Light Detection and Ranging (LiDAR) deployment in Washington State. For each option, as well as current data collection processes, detailed estimates of data collection costs were developed for three current programs: Roadside Feature Inventory Program, bridge clearance measurement, and ADA feature inventory. Hoekstra (12) studied the implementation of a statewide multi-level linear referencing system (MLLRS) for Iowa DOT. Quantified benefits were developed for GIS staff, operational staff, and administrative staff for other departments. Babinski (7) used the idea of consumer surplus to extend the results of their examination of agency data costs. Their survey of GIS professionals and users provided not only information on cost savings per unit but also increases in the number of output units that would be produced. Cost savings for current outputs were calculated as the number of units currently produced and the difference between the with-GIS and without-GIS cost per unit. Benefits due to increased output were calculated as one-half times the product of the difference in the number of units produced and the difference in cost per unit. Reduced Expenditures for Asset Maintenance, Rehabilitation, and Replacement One of the key expected benefits of implementing TAM systems and improved practices is an overall reduction in agency expenditures for asset maintenance, rehabilitation, and replacement. Several of the documents covered in this review provided estimates of savings in agency expenditures for asset maintenance, rehabilitation, and replacement due to new TAM systems or processes: Smadi (13) estimated agency cost savings for the implementation of a pavement management system in Iowa. Agency costs and pavement condition were compared for âWith PMSâ and âWithout PMSâ cases. The âWithout PMSâ case consisted of actual expenditures and pavement conditions for a five-year period prior to the implementation of the PMS. The âWith PMSâ case covered the same time period, but with expenditures and pavement conditions predicted based on optimization criteria from the PMS. In particular, the âWith PMSâ case was run to achieve the same pavement conditions observed for the âWithout PMSâ case, so the difference between the two cases would be the difference in agency costs necessary to achieve this pavement condition. The analysis found a difference of about $1 million per year for the Iowa Interstate system. The results from Smadiâs 2004 study were used by FHWA in a regulatory impact analysis (RIA) of a proposed rule that would establish a process for the development of state transportation asset plans and define minimum standards for pavement and bridge management systems (14). The RIA assumed that, under the proposed rule, all states, DC, and Puerto Rico could save $1 million per year on average starting in the third year after implementation. With this assumption, the benefit-cost ratio of the proposed rules was 9.3 with a discount rate of 7% and 10.5 with a discount rate of 3%. Note that as of the time of the review, the proposed rule was still under development, and over a dozen of the 58
Appendix A 115 comments received by FHWA on the proposed rule, including those from AASHTO, questioned the cost and benefit assumptions in the RIA. Commonly cited issues in the comment are that the costs assumed by FHWA appear low, and that many agencies have already implemented an asset management approach, and thus have already gained the benefits projected by FHWA. Pickering (15) describes a generic whole life cost (WLC) model for highway technology assets and identifies benefits of adopting the WLC approach. WLC is presented as a systematic approach for balancing initial and ongoing capital expenditure with the costs of operation and in-service support for the lifetime of the asset under consideration. The paper notes that WLC model results can be used as a decision support tool for designing and evaluating new systems, correcting in-service problems, guiding maintenance and replacement strategies, and forecasting future asset costs and system performance. In a PMS project proposal for the Virginia DOT, Harrison (16) estimated cost savings due to increased pavement life as 0.25% of Virginiaâs $280 million annual budget for asphalt paving, based on national research showing savings from using a PMS to develop and implement a preventive maintenance program. In addition to data collection costs noted above for MLLRS, Hoekstra (12) estimated agency cost savings due to reduced level of risk for litigation, construction cost reductions, and reduced maintenance costs. These savings were estimated by applying percentages to associated agency budget categories. The percentages used were based on Caltrans experience for comparable costs. Ye (17) estimated agency cost savings for a winter maintenance decision support system (MDSS). The MDSS was used to simulate conditions under traditional practices and under practices recommended by the MDSS itself. The simulation was conducted for a nine mile Interstate highway segment in New Hampshire, using weather data from seven consecutive winter seasons. In addition to highway user travel-time and accident cost savings (discussed below), the MDSS simulations were used to estimate agency cost savings due to reduced salt usage. Veneziano (18) describes the development of an online toolkit for benefit-cost analysis of specific winter maintenance practices, equipment, and operations. Based on the study by Ye, the toolkit estimates agency cost savings from a maintenance decision support system as 15% of materials cost. For AVL/GPS, agency cost savings are estimated as 5% of labor and equipment costs associated with vehicle operations and 10% of paperwork costs (based on a Kansas study). For a road weather information system, agency cost savings are 40% of material costs, based on an Idaho study. Vasquez (19) estimated the net present value of future agency costs for three pavement management options for the city of Toole, Utah: âno pavement managementâ (applying complete base and pavement replacement after the pavement fails), âpartial pavement managementâ (applying preventive treatment eight years after newly constructed), and âfull pavement managementâ (applying preventive treatment eight years after newly constructed and a rehabilitation treatment four years later). Krugler (20, 21) explored simulation, optimization, and decision analysis methodologies for examining the benefits of two specific asset management goals, namely minimizing the cost of right-of-way acquisition and speeding construction completion time on the construction of new highway facilities. Simulation, optimization, and decision analysis techniques were
116 Return on Investment in Transportation Asset Management Systems and Practices each investigated as potential methods in Phase 1 of this research. In Phase 2, the authors developed simulation and optimization tools for TxDOT right-of-way sections and budget decision makers. These tools were designed to provide decision support as optimal strategies for use of early right-of-way acquisition methods are considered at project, district, and state levels. In New Zealand, research on âOptimised Decision Making for Smaller Authoritiesâ (22) cites an example using pavement management on a very small network (Central Otago, with 580 km). Though one of the most cost-effective networks in New Zealand, both in terms of cost per kilometer and cost per vehicle, Central Otago had concerns about uncertainty of pavement life and lack of confidence to program 30 years ahead, a looming wave of rehab needs, and upcoming funding scarcity, so they sought to find additional means to save. They launched an asset management initiative to gather data on the network, and then used it to gain a detailed model of their needs. The model found that they could actually spend 28% less than what they had proposed to invest prior to undertaking the optimization model. Setting different levels of service for different roads hierarchies in conjunction with this pavement performance model allowed the agency to sustain the desired condition levels at a reduced cost. Asset Condition Another measure of the impact of new TAM systems and practices is the change in asset condition after adoption of new TAM actions. Several of the studies reviewed used changes in pavement condition ratings to quantify the benefits of TAM: Hendren (23) documents a peer exchange on asset management in planning and operations. The report provides asset condition data from before and after the TAM implementation, including (1) remaining service life for pavements from Michigan DOT, (2) pavement condition from Jackson County Missouri, and (3) pavement and bridge condition from Ohio DOT. McNeil  (24) conducted simulation runs using Vermontâs PMS for a âWith PMSâ case (in which maintenance and rehabilitation actions were selected based on PMS optimization strategy) and a âWithout PMSâ case (in which M&R actions were selected based on a âworst-firstâ strategy). Results for the two cases were compared by year and highway system in terms of traffic-weighted average pavement condition. Results were also compared by year and highway system in terms of the percent of travel by pavement condition category (i.e., very poor, poor, fair, and good). McNeil [2008, 2011] (24, 25) conducted a similar analysis using FHWAâs HERS-ST model. As in the Vermont case study, a âWithout HERS-STâ case was simulated in which highway section treatments were selected solely based on present serviceability rating (PSR) by highway class. The âWith HERS-STâ case was simulated in which the HERS-ST prediction and benefit-cost analysis procedures were used to select highway section treatments. The two cases were compared in terms of traffic-weighted pavement condition by five-year funding period and highway class and in terms of percent of travel by pavement condition category1. 1 As discussed in the next section, user benefits (reductions in travel time, vehicle operating costs, and crashes) were also estimated for the âWithout HERS-STâ and âWith HERS-STâ cases.
Appendix A 117 All of the above examples involve changes in pavement condition. Similar quantitative approaches could in theory be applied to changes in bridge condition, though in practice no such examples were identified in the review. User Beneï¬ts Asset management has the potential to produce benefits by lower user costs, such as those associated with excess travel time, vehicle operating costs, or crashes. A few of the reviewed studies produced estimates of these types of user benefits: McNeil [2008, 2011] (24, 25) produced estimates of user benefits for their comparison of âWith HERS-STâ and âWithout HERS-STâ cases. The estimates of user benefits included travel-time and vehicle operating cost savings (for autos and trucks); reductions in crashes, injuries, fatalities, and crash costs. The estimates were obtained directly from outputs of HERS-ST. Ye (17) produced estimates of cost savings due to reductions in travel time and crashes associated with the MDSS. To estimate both reductions in crashes and delays, adjustment factors to crash rates and speeds as a function of road conditions were used. For example, for deep slushy vs. dry road conditions, crash rates were 75% higher and speeds were 16% lower. In the analytical toolkit described in Veneziano (18), total user benefits associated with travel-time and crash cost savings are estimated as a percentage of material costs. The default percentage is 15%. Also, safety-related benefits of automatic vehicle location (AVL) and geographic information systems (GIS) due to reductions in snow removal time and more timely responses to emergencies are estimated as a 5% reduction in all crash types (based on a Kansas study). Safety benefits of a road weather information system are estimated as a 3% to 17% reduction in all crash types (based on a study from Finland). Hoekstra (12) estimated benefits of MLLRS due to safety improvements that would result due to efficiencies in identifying safety-related information. These savings were estimated by applying percentages (that vary from 0.07% for a baseline system to 0.10% depending on options) to the associated agency budget category. The percentages were developed based on Caltrans experience. Rail Safety and Standards Board (26) estimated benefits of remote condition monitoring options, including safety, service, and industry cost savings. Deighton (27) analyzed alternative strategies for maintaining and rehabilitating the UDOT highway network over a 20-year analysis period. The strategies studied included Do Nothing, Maintenance Only, Reconstruction Only, Current Model, as well as other strategies that altered the timing of pavement preservation and rehabilitation activities, pavement condition levels, and funding levels. In addition to agency costs, the report estimated for each strategy the safety costs, vehicle operating costs (including fuel and wear and tear on vehicles and tires), and delay costs associated with preservation and rehabilitation activities. BASIS FOR QUANTITATIVE ANALYSIS The studies included in the literature review identified four different methods to quantify the benefits of TAM actions. These methods include both modeled and measured data. The research team will likely need to consider a combination of these methods for the ROI framework,
118 Return on Investment in Transportation Asset Management Systems and Practices depending on whether the asset management action being evaluated has already been implemented or is a prospective implementation. The quantitative methods found in the literature review included the use of: Time series data on system-wide conditions before and after implementation of the actions Controlled field experiments to measure cost savings and differences in asset conditions with and without the actions Simulation of conditions with and without the actions Breakeven analysis in which benefits required to offset the cost of the actions are related to the scale of the system affected by the actions. Time Series A time series approach allows an agency to document the benefits associated with ROI actions that have already been implemented. This is the most direct approach, but it cannot be applied to a prospective investment unless the results of a time series analysis for another agency are used as a benefit transfer. Despite the apparent simplicity and intuitiveness of a time series approach, quantifying the benefits of TAM actions using data on conditions before and after the implementation of TAM actions poses a number of challenges: Changes over time in agency expenditures for maintenance and rehabilitation greatly affect system conditions and it is difficult to separate the effects of TAM actions from M&R expenditures. This difficulty is compounded by the fact that information developed through TAM actions is often used to support needs for increases or shifts in expenditure levels. Other variables such as weather and traffic levels also affect system conditions. In addition to the TAM actions and expenditure levels, changes in other aspects of an agencyâs business processes can affect system conditions. It may be possible to control for the problems noted above through construction of a model that accounts for the effects of changes in expenditure levels, other agency business processes, weather, and traffic. The review attempted to identify examples of time series analyses of asset management benefits, but no such examples were identified in the literature. Controlled Field Experiments The literature review found several studies in which controlled field experiments were performed to quantify the benefits of TAM actions. While this approach could not be applied to the ROI of a prior or prospective investment, the results of controlled experiments could be used for benefit transfers. Lawrie (28) presents a case study in which point machines on a busy mainline rail section were instrumented and the Intelligent Asset Management system was used for data acquisition, analysis, and decision support. The benefits of the system were measured using a before and after analysis of mean time between service affecting faults on the rail line. Also, agency data collection, processing, and analysis cost savings were estimated using controlled field experiments by De la Garza (6) for the IP- S2 mobile mapping system, by Schiffer (9) for a barcode inventory system, and by Yen (11) for mobile LiDAR.
Appendix A 119 Nokes (29) presents a quantitative analysis of the benefits from Heavy Vehicle Simulator (HVS) testing of pavement rehabilitation alternatives to validate expected performance of innovative mixes and designs before using them in rehabilitating a high-traffic urban interstate freeway in the Los Angeles area. Five rehabilitation alternatives were identified for the case study and total life cycle costs were calculated for each alternative based on methods and guidance in the Caltrans Life-Cycle Cost Analysis Procedures Manual and its accompanying RealCost software. The total life cycle costs were the same for the âWithout HVS testâ and âWith HVS testâ cases. However, the probabilities of implementing a given rehabilitation alternative were different depending on whether the HVS test was conducted. That is, the innovative rehabilitation alternatives, which had lower life cycle costs, were less likely to be selected without the HVS test. For the âWith HVSâ and âWithout HVSâ cases, expected costs were calculated by multiplying the total life cycle cost of each alternative by its probability of implementation and summing the result over all alternatives. The benefits of HVS testing were the difference between expected costs for the two cases. Simulation The use of simulation models provides the opportunity to estimate the benefits of TAM actions under controlled circumstances. For example, âWith TAMâ and âWithout TAMâ scenarios can be designed so that both produce similar system-wide measures of asset conditions, in which case the relative merit of the two scenarios can be assessed in terms of agency costs required. Conversely, the scenarios can be given the same budgets, in which case their relative merit can be assessed in terms of system condition and user costs. Ye (17) presents the results of a benefit-cost study of a winter maintenance decision support system (MDSS). The MDSS evaluated was developed under a pooled fund study led by South Dakota. The MDSS is an integrated software application that provides users with real-time road treatment guidance for each maintenance route, addressing the fundamental questions of what, how much, and when according to the forecast road weather conditions, the resources available, and local rules of practice (the methods that a transportation agency uses in treating its roadways). Benefit-cost analyses were conducted for two scenarios: one in which the same amounts of resources were used as in the base case and one in which the same level of service was achieved as in the base case. In conducting the benefit-cost analyses, the MDSS itself was used to simulate conditions for the base case and the alternative scenarios. For the base case, existing maintenance practices were assumed. For the alternative scenarios, maintenance practices selected by the MDSS were assumed. Quantitative estimates of benefits included reductions in salt usage, crashes, and delays. McNeil (24) used the simulation capabilities of HERS-ST to estimate the benefits of using TAM tools based on economic efficiency as opposed to a âworst-firstâ strategy in selecting pavement rehabilitation actions. The benefits of using HERS-ST are quantified using net present value, benefit-cost ratio, and other performance measures such as average pavement condition, travel time, safety, and maintenance costs. The âworst-firstâ strategy is run first, using pavement condition triggers to select highway sections to be resurfaced or reconstructed. Expenditures from the âworst- firstâ run are then used as budget constraints in a HERS-ST run in which improvements are selected based on economic efficiency. The methodology is demonstrated for three case studies using data from New Mexico, Kentucky, and Delaware. One caution in using simulation models to assess their own usefulness as TAM tools is that the decision criteria they use to select asset treatments are the same criteria that are being used to assess the performance of the tools relative to current practices. Even if the prediction models and
120 Return on Investment in Transportation Asset Management Systems and Practices prioritization procedures are significantly flawed, the tools will score better than current practice because the flawed elements are used to keep score. This is not a problem if using prediction models that have been independently verified, but this caution should be kept in mind in using unverified simulation models to assess their own usefulness. Bernhardt and McNeil (30) used a simulation model to illustrate the effects of variability in the assessment of pavement condition on life cycle user and agency costs. The model was run for 1,000 pavement segments, over which reported pavement conditions (measured in PCI) were assumed to differ from actual pavement conditions. User costs were calculated based on actual PCI while agency actions and costs were based on reported PCI. Four scenarios were simulated: (1) reported PCI was 5% higher than actual PCI, (2) reported PCI was 5% lower than actual PCI, (3) the difference between reported and actual PCI was a uniformly distributed random variable ranging from -5% to +5% of actual PCI, and (4) the difference between reported and actual PCI was a uniformly distributed random variable ranging from -2.5% to +2.5%. Agency costs and user costs for each of the four scenarios were compared with a base case in which reported and actual PCI were assumed to be the same. Breakeven Analysis Breakeven analysis is a useful alternative in situations where most of the benefits of a TAM action cannot easily be quantified by the methods discussed above. In breakeven analysis, the amount of benefits that would be required to cover the costs of a TAM action are calculated and related to the size of the system that the action would affect. Two examples of breakeven analysis (one for pavement and one for bridges) are provided in the RIA conducted by FHWA (14) for the proposed rule on developing asset management plans (and establishing minimum standards for pavement and bridge management systems). However, as noted above, a number of stakeholders provided comments to FHWA on the draft rule and RIA, and these comments are not incorporated in this review. In performing the analysis for the RIA, FHWA first calculated the cost of the rule to states. It then estimated benefits to road users of improving pavements from poor to good (or better) condition based on savings in vehicle maintenance and repair, fuel consumption, and tire wear. The number of road miles of pavements in poor condition that would have to be improved to good condition (or better) to offset the cost to states of the proposed rule was then calculated and related to the total number of pavement miles currently in poor condition. The other breakeven analysis performed by FHWA determined the number of year-long bridge postings that would have to be avoided to offset the costs of the proposed rule. Bridge posting costs were estimated based on average truck volumes, detour lengths, travel time per mile, and vehicle operating cost per mile. Another example of a breakeven analysis is provided in NCHRP Report 800 (8), which presents a case study for adding agency-wide geospatial capabilities for program development. The options studied provided both efficiency benefits (measured in terms of reduced staff time requirements) and increased effectiveness (due to better information to support program development and project prioritization, as well as better communication with customers and political officials). The effectiveness benefits were not explicitly quantified. Rather, the report noted that net costs (costs minus efficiency benefits) were only about 0.06% of the agencyâs pavement and bridge programs. The effectiveness benefits were clearly worth far more than the net costs given the opportunity they represented to spend the available funds more wisely.
Appendix A 121 REFERENCES 1. AASHTO. âTransportation Asset Management Guide: Volume I,â prepared for NCHRP Project 20-24(11) by Cambridge Systematics, Parsons Brinckerhoff Quade & Douglas, Inc., Ray Jorgenson Associates, Inc. and Paul D. Thompson, November 2002. 2. AASHTO. âTransportation Asset Management GuideâA Focus on Implementation,â prepared for NCHRP Project 8-69 by AECOM, Spy Pond Partners, and Paul D. Thompson, 2011. 3. AASHTO. âPavement Management Guide,â 2nd Edition, 2012. 4. Markow, M., and Hyman, W. âNCHRP Synthesis 397: Bridge Management Systems for Transportation Agency Decision Making,â Transportation Research Board, 2009. 5. Federal Transit Administration. âAsset Management Guide,â prepared by Parsons Brinckerhoff, 2012. 6. De la Garza, J. M., Howerton, C. G., and Sideris, D. âA Study of Implementation of IP-S2 Mobile Mapping Technology for Highway Asset Condition Assessment,â Virginia Tech. 7. Babinski, G., Fumia, D., Reynolds, T., Singh, P., Scott, T., and Zerbe, R. âAn Analysis of Benefits from Use of Geographic Information Systems by King County Washington,â Richard Zerbe and Associates, March 2012. 8. Spy Pond Partners. âNCHRP Report 800: Successful Practices in GIS-Based Asset Management,â Transportation Research Board, 2015. 9. Schiffer, A. âAutomated Asset Inventory System,â Arizona DOT, April 2006. 10. Dye Management Group, Inc. âMonitoring Highway Assets with Remote Technology,â Michigan Department of Transportation, July 2014. 11. Yen, K. S., Ravani, B., and Lasky, T. âLiDAR for Data Efficiency,â Washington State Department of Transportation, September 2011. 12. Hoekstra, R., and Breyer, J. âMulti-Level Linear Referencing System (MLLRS) Cost/Benefit Value Analysis Study,â requested by AASHTO, May 2011. 13. Smadi, O. âQuantifying the Benefits of Pavement Management,â a paper from the 6th International Conference on Managing Pavements, 2004. 14. Federal Highway Administration. âAn Analysis of the Economic and Non-Economic Costs and Benefits of Implementing MAP-21 Asset Management Plans and Related Provisions,â 2015. 15. Pickering, W. âUnderstanding the Whole Life Costs of Technology Projects in the Highway Market,â Smart Moving Conference, ITS Strategies and Potentials Session, 2007. 16. Harrison, F., and Mack, J. âPavement Management System (PMS) Project Proposal,â Virginia Department of Transportation, September 2005. 17. Ye, Z., Strong, C., Shi, X., Conger, S., and Huft, D. âBenefit-Cost Analysis of Maintenance Decision Support System,â Transportation Research Record: Journal of the Transportation Research Board, No. 2107, Transportation Research Board, 2009. 18. Veneziano, D., Fay, L., Ye, Z., Williams, D., and Shi, X. âDevelopment of a Toolkit for Cost-Benefit Analysis of Specific Winter Maintenance Practices, Equipment and
122 Return on Investment in Transportation Asset Management Systems and Practices Operations: Final Report,â prepared for the Wisconsin Department of Transportation and the Clear Roads Program, November 2010. 19. Vasquez, C., Heaslip, K., and Louisell, W. âA Practice Proven Pavement Management System for Local Governments,â submitted for presentation and publication at the 89th annual meeting of the Transportation Research Board, Washington, D.C., 2010. 20. Krugler, P., Chang-Albitres, C., Pickett, K., Smith, R., Hicks, I., Feldman, R., Butenko, S, Kang, D., and Guikema, S. âAsset Management Literature Review and Potential Applications of Simulation, Optimization, and Decision Analysis Techniques for Right-Of- Way and Transportation Planning and Programming,â requested by FHWA, April 2007. 21. Krugler, P., Chang-Albitres, C., Feldman, R., Butenko, S, Kang, D., and Seyedshohadaie, R. âDevelopment of Decision-Making Support Tools for Early Right-Of-Way Acquisitions,â requested by FHWA, January 2010. 22. Muir, J. âOptimised Decision Making for Smaller Authorities,â Central Otago District Council, presented at LGIM Forum, 2015. 23. Hendren, P. âAsset Management in Planning and Operations: A Peer Exchange,â Transportation Research Circular, No. E-C076, Transportation Research Board, 2005. 24. McNeil, S., and Mizusawa, D. âMeasuring the Benefits of Implementing Asset Management Systems and Tools,â Midwest Regional University Transportation Center, University of Wisconsin, Madison, 2008. 25. McNeil, S., Mizusawa, D., Rahimian, S., and Bittner, J. âAssessing and Interpreting the Benefits Derived from Implementing and Using Asset Management Systems,â Project 06- 06, Phase 2, Midwest Regional University Transportation Center, June 2011. 26. Rail Safety and Standards Board, âDetailed Overview of Selected RCM AreasâRCM Toolkit,â prepared by Asset Management Consulting Limited, London, 2012. 27. Deighton Associates Limited, âGood Roads Cost Less: 2006 Study Update,â prepared for Utah Department of Transportation, 2006. 28. Lawrie, M. âIntelligent Asset Management,â Thales Transportation Systems GmbH, November 2013. 29. Nokes, W., du Plessis, L., Mahdavi, M., Burmas, N., Holland, T.J., and Harvey, J. âTools and Case Studies for Evaluating Benefits of Pavement Research,â 8th International Conference on Managing Pavement Assets. 30. Bernhardt, S., and McNeil, S. âImpacts of Condition Assessment Variability on Life Cycle Costs,â ASCE, 2006. Additional Resource for information on HERS-ST Parameters FHWA. âHERS-ST v2.0: Highway Economic Requirements System-State Version Technical Report,â U.S. Department of Transportation, Washington, D.C., 2002.
TRB's National Cooperative Highway Research Program (NCHRP) Research Report 866: Return on Investment in Transportation Asset Management Systems and Practices explores how transportation agencies manage their transportation assets, and provides guidance for evaluating the return on investment for adopting or expanding transportation asset management systems in an agency.
As the term is most generally used, transportation asset management (TAM) entails the activities a transportation agency undertakes to develop and maintain the system of facilities and equipment—physical assets such as pavements, bridges, signs, signals, and the like—for which it is responsible. Based on the research team’s work and the experiences of these agencies and others, the researchers describe a methodology that an agency may use to assess their own experience and to plan their investments in TAM system development or acquisition.
A spreadsheet accompanies the research report helps agencies evaluate the return-on-investment of TAM systems.The tool allows users to summarize data from various simulation tools. The calculator also includes factors and procedures from the Highway Economic Requirements System State Version (HERS-ST) to estimate user benefits for pavement projects. It does not estimate user benefits for bridge projects.
This software is offered as is, without warranty or promise of support of any kind either expressed or implied. Under no circumstance will the National Academy of Sciences, Engineering, and Medicine or the Transportation Research Board (collectively "TRB") be liable for any loss or damage caused by the installation or operation of this product. TRB makes no representation or warranty of any kind, expressed or implied, in fact or in law, including without limitation, the warranty of merchantability or the warranty of fitness for a particular purpose, and shall not in any case be liable for any consequential or special damages.
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