Business Intelligence Techniques for Transportation Agency Decision Making

Strategic approaches to the management and operation of transportation systems blend knowledge of agency goals, asset conditions, traffic and safety performance, and finance and budget constraints. This knowledge is based on data and many agencies are implementing consolidated data governance practices to improve data quality, to maximize the value of the data to the agency, and to better manage the data collection and analysis resources. The private sector also takes a strategic approach to management, one aspect of which is business intelligence which comprises the strategies and technologies used by enterprises for the data analysis of business information. Business intelligence technologies provide historical, current and predictive views of business operations; common functions include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics. Despite differences in how the private sector and transportation agencies operate (e.g., competition vs. collaboration, level of transparency in decision-making), it may be the case that various business intelligence practices and methods could be effectively incorporated by transportation agencies to improve activities such as trade-off analysis and enterprise resource planning. Of particular interest are techniques that would identify cultural, economic, and other trends and “black swan events” that will affect the transportation system. Incorporation of these techniques could lead to more strategic management of the transportation system and its operations to better address overall agency goals and objectives. Under NCHRP Project 03-128, “Business Intelligence Techniques for Transportation Agency Decision Making,” AEM Corporation was asked to catalog new techniques to extract actionable information from traditional and new data sources that transportation agencies can employ to enhance their decision-making processes. The research team (1) conducted a critical review of current and emerging business intelligence best practices, technologies, and data analytics applications in the private sector and identified those that are potentially most applicable to transportation agencies in nature, extent, and objectives; (2) identified common transportation agency decision-making processes and capabilities, in domains such as operational management, infrastructure management, investment management, and organizational and corporate management; (3) illustrated potential types of innovative data and information sources applicable to transportation agency and program management decisions; and (4) developed a guide cataloging business intelligence techniques and new data and information sources and the techniques to incorporate these techniques into an agency’s practices.


  • English


  • Status: Completed
  • Funding: $395940
  • Contract Numbers:

    Project 03-128

  • Sponsor Organizations:

    National Cooperative Highway Research Program

    Transportation Research Board
    500 Fifth Street, NW
    Washington, DC  United States  20001

    American Association of State Highway and Transportation Officials (AASHTO)

    444 North Capitol Street, NW
    Washington, DC  United States  20001

    Federal Highway Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Project Managers:

    McKenney, Christopher

  • Performing Organizations:

    Applied Engineering Management Corporation

    Herndon, VA  United States  20171
  • Principal Investigators:

    Shah, Vaishali

  • Start Date: 20180620
  • Expected Completion Date: 20231120
  • Actual Completion Date: 20230127
  • Source Data: RiP Project 41595

Subject/Index Terms

Filing Info

  • Accession Number: 01634644
  • Record Type: Research project
  • Source Agency: Transportation Research Board
  • Contract Numbers: Project 03-128
  • Files: TRB, RIP
  • Created Date: May 12 2017 10:22AM