Framework for Integrating and Assessing Highway Infrastructure Data

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.

[1]  Samuel Leinhardt,et al.  Social Networks: A Developing Paradigm , 1977 .

[2]  Liaquat Hossain,et al.  Communications and coordination in construction projects , 2009 .

[3]  Matthew R. Hallowell,et al.  Measuring and modelling safety communication in small work crews in the US using social network analysis , 2013 .

[4]  Chen Chen,et al.  Asset Management Data Collection For Supporting Decision Processes , 2005 .

[5]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[6]  Edward McCormack,et al.  FREIGHT DATA FROM INTELLIGENT TRANSPORTATION SYSTEM DEVICES , 2003 .

[7]  Paul Chinowsky,et al.  Project Organizations as Social Networks , 2010 .

[8]  J. A. Barnes.  Class and Committees in a Norwegian Island Parish , 1954 .

[9]  P. Bonacich.  Factoring and weighting approaches to status scores and clique identification , 1972 .

[10]  Keith R. Herrmann.  Visualizing Your Business: Let Graphics Tell the Story , 2001 .

[11]  Stephen Pryke,et al.  Analysing construction project coalitions: exploring the application of social network analysis , 2004 .

[12]  Eddy M. Rojas,et al.  Social Network Analysis of Collaborative Ventures for Overseas Construction Projects , 2011 .

[13]  Hyung Min Kim.  Comparing Measures of National Power , 2010 .

[14]  Paul Chinowsky,et al.  Networks in engineering: an emerging approach to project organization studies , 2012 .

[15]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[16]  Diane M. Strong,et al.  Information quality benchmarks: product and service performance , 2002, CACM.

[17]  Neal Hawkins,et al.  Use of Transportation Asset Management Principles in State Highway Agencies , 2013 .

[18]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[19]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[20]  J. Mitchell,et al.  Social Networks in Urban Situations: Analyses of Personal Relationships in Central African Towns. , 1970 .

[21]  Diane M. Strong,et al.  Knowing-Why About Data Processes and Data Quality , 2004 .

[22]  Ina Finke,et al.  SELaKT - Social Network Analysis as a Method for Expert Localisation and Sustainable Knowledge Transfer , 2004, J. Univers. Comput. Sci..

[23]  William S. Cleveland,et al.  Data science: An action plan for expanding the technical areas of the field of statistics , 2001, Stat. Anal. Data Min..

[24]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..

[25]  Caw Gilliland.  MANAGING CHANGE IN STATE DEPARTMENTS OF TRANSPORTATION. SCAN 5 OF 8: INNOVATIONS IN WORK FORCE STRATEGIES , 2001 .