ارزیابی سرمایه گذاری در بخش دارایی بزرگراه
Abstract
1- Introduction
2- Data and tools
3- Data envelopment analysis (DEA)
4- Investment scenarios and benchmarking of individual asset
5- Cross-asset investment benchmarking
6- Conclusions and discussions
References
Abstract
Highway agencies have been using many of the elements of asset management with the support of various decision-making tools. To determine the most effective investment strategy with scarce resources, the integration, and hence better utilization, of existing tools and practices across asset classes is generally lacking. This paper applies data envelopment analysis (DEA) to benchmark different highway investment scenarios using existing data or data readily available through existing models. Three asset types, pavements, bridges, and traffic signage, are investigated. Asset investment analysis results from the Highway Economic Requirements System State Version (HERS-ST) application, the PONTIS bridge management system software, and purpose-built traffic signage spreadsheet are obtained to capture the changes of performance measures under various budget scenarios and are further used as the inputs for the DEA process to benchmark investment scenarios for each individual asset. Subsequently, the performance measures and budget levels are assembled in the Asset Manager-NT software, whose results are input into DEA to benchmark cross-assets resource allocation scenarios. Planning for the management of highway network is addressed via case studies in a systematic manner that recognizes the tradeoffs among different funding periods and objectives such as preserving existing investments, safety, roughness and user costs. This study has established a preliminary implementable framework of highway asset management by linking DEA approach and current widely used decision-making tools for more efficient investments within and cross assets, and better understand of the tradeoffs, costs and consequences of various asset management decisions.
Introduction
At its core, asset management is about using limited transportation dollars in the most cost-effective way possible (AASHTO, 2011; Adey, 2017; McNeil et al., 2008; Taggart et al., 2014). A fundamental challenge in managing transportation infrastructure assets is to determine how to allocate scarce resources among disparate asset categories (roads, bridges, safety, mobility, etc.) and types of needs (replacement, rehabilitation, routine maintenance, etc.) (Bai et al., 2011, 2015; Cambridge Systematics, Inc., 2006; Dehghanisanij et al., 2010; Fwa and Farhan, 2012; Li et al., 2012; Li and Sinha, 2004; Mrawira and Amador, 2009; Pagano et al., 2005; Wu et al., 2012). Allocating resources between these areas is a complex problem requiring consideration of multiple objectives and constraints. To achieve the best results at both the individual asset system and the overall system levels, given a budget, five types of approaches are traditionally used for fund appropriation among competing highway asset components (Fwa and Farhan, 2012): (1) appropriation of funds based on historical allocations to the individual asset with minor adjustments to allow for special requirements (OECD, 2001); (2) formulabased appropriation, whose funds are allocated according to a predetermined formula based on engineering judgment or past experience consisting of selected parameters from the various assets (such as empirical regression models); (3) asset value-based appropriation, which implicitly assumes that the maintenance needs of each asset is proportional to its asset value (Jani, 2007; Sirirangsi et al., 2003); (4) maintenance needs-based appropriation, which allocates the available funds in proportion to the maintenance needs of each individual asset, but does not address optimality for the overall asset system (Flintsch and Bryant Jr., 2006); and (5) performance-based appropriation, which ties fund appropriation with the desired performance level of each asset component (Cowe et al., 2006; Gharaibeh et al., 1999, 2006). All approaches suffer limitations as they do not achieve optimality at both individual and overall system levels simultaneously.