Framework for Integrating and Assessing Highway Infrastructure Data Academic Article uri icon

abstract

  • © 2015 American Society of Civil Engineers. 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 and efficiency of information systems is becoming a main concern with respect to adding value to the users as compared with the amount produced and replicated. This paper presents a new framework for integrating highway infrastructure data and assessing the level of effective use of data in generating information and supporting management decisions from a holistic network viewpoint. In this study, social network theory is used as a principal component to interlink and map data with information and decisions, identify critical data and information in decision-making processes, and assess the overall performance of data and information usage. The application of this new framework is illustrated using real pavement management scenarios. A new performance measure called the Highway Infrastructure Data Integration (HIDI) index is proposed to evaluate the status of data utilization that may serve as an infrastructure data report card and help justify the return on investment for the continuous and growing data collection efforts of state highway agencies. This new index allows agencies to interlink data, information, and decisions and develop an active utilization plan for currently existing databases to place the right information in the hands of decision makers. In addition, the HIDI index will allow the enhancement of new data collection and knowledge generation plans to support key decisions that historically were not well supported with information and data. This new framework may be used as a benchmarking example for state highway agencies to make effective and reliable decisions through data-driven insights.

published proceedings

  • Journal of Management in Engineering

author list (cited authors)

  • Woldesenbet, A., Jeong, H. D., & Park, H.

citation count

  • 30

complete list of authors

  • Woldesenbet, Asregedew||Jeong, H David||Park, Heedae

publication date

  • May 2015