The objective of this project is 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 project will be based largely on identifying promising business intelligence practices from the private sector and exploring their utility using transportation agency and program management scenarios. These scenarios should cover a broad range of agency management levels and functions, including system operations.
The draft final deliverables are expected in October 2019.
Task 1. Carry out project management activities, including holding a web-enabled kick-off meeting, preparing and submitting monthly and quarterly progress reports, and communicating with the research team and the NCHRP.
Task 2. Conduct a comprehensive review of literature pertaining to industry standards and best practices in the adoption and use of business intelligence (BI) and business analytics (BA) outside of transportation. Supplement the literature review with interviews of innovators and leaders from many industries.
Task 3. Conduct a comprehensive review of transportation literature to identify common agency decision-making processes and capabilities in multiple domains. Supplement the literature review with interviews of selected individuals at various levels of decision-making.
Task 4.Develop a catalog of BI strategies, techniques, and tools and summarize sources for traditional and non-traditional data. Organize the types and levels of agency decision making along with the respective data needs. Create a suite of decision-making scenarios with good value BI options by matching between the two lists.
Task 5. Develop and interim report and meet with the NCHRP to review it. The interim report will include: (1) introduction, (2) research objectives and approach, (3) findings from Tasks 2 through 4, (4) definition of BI adoption scenarios and pathway requirements, (5) activities planned and timeline for remaining tasks, (6) annotated outline for the Plan for Implementation of Research products, (7) annotated outline for the Final Report, and (8) bibliography.
Task 6. Conduct a half-day workshop bringing together individuals at the executive and program levels from multimodal agencies. Preparatory material, including a list of potential attendees, will be submitted at an appropriate time prior to the workshop.
Task 7. Apply the findings from the workshop to (1) refine the BI adoption pathways for the decision-making scenarios and (2) develop case studies for high-value BI adoption.
Task 8. Develop the final work products, including the final report, executive summary, customizable briefing of research results, implementation plan technical memorandum, and research recommendations technical memorandum. Following review by the NCHRP, revise the final work products.
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 that 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 v. 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, in turn, could lead to more strategic management of the transportation system and its operations to better address overall agency goals and objectives.