BACKGROUND

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.

OBJECTIVE

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.