Highway Infrastructure Data and Information Integration & Assessment Framework: A Data- Driven Decision-Making Approach

State highway agencies invest a large amount of resources in collecting, storing and managing various types of data ranging from roadway inventory to pavement condition data during the life cycle of a highway infrastructure project. Despite this huge investment, the current level of data use is limited and is raising concerns whether the growing amount of data adds value to users and offers meaningful return on data collection efforts. This study presents a holistic approach that can systematically integrate and bridge data and information with decisions through incorporation of a unique and proactive performance assessment technique to improve the utilization of a growing amount of data in transportation agencies. With a focus on enhancing the active utilization of data and measuring level of data use, this research delivers i) Integrated Data Quality Assessment Framework, ii) Three-tiered Hierarchical Data-Information-Decision-making Framework and iii) Highway Infrastructure Data Integration (HIDI) index, new data and information performance assessment tool. The study presents an integrated requirement analysis to identify the satisfaction level of various highway decision-makers in current data use and determine the quality requirements of highway data in an integrated and objective manner through the application of fault tree analysis. A three-tiered hierarchical framework is presented to understand the relationship between data and information and identify their use in supporting highway infrastructure decision-making processes. As part of this framework, key players in decisionmaking processes are identified and quantified through the application of a social network theory. A new index called, HIDI is also developed to evaluate the status of data utilization that may serve as Highway Infrastructure Data Report Card and help justify the return on investment on the continuous and growing data collection efforts. This research study will allow agencies to interlink data, information and decisions and to develop active utilization plans of currently existing databases to place the right information in the hands of decision-makers. It will enhance the development of new data collection scheme to support key decisions that, historically, were not well-supported with information and data. The study uses pavement management data as a primary data set to illustrate the application of the framework along with preconstruction service data as a case study and validation data set. This new framework may be used as a benchmarking example for SHAs to make effective and reliable decisions through data-driven insights.