The National Academies

NCHRP 03-128 [Anticipated]

Business Intelligence for Transportation System Management and Operations and Agency Decision Making

  Project Data
Source: AASHTO Highway Subcommittee on Transportation Systems Management and Operations
Funds: $360,000
Staff Responsibility: B. Ray Derr
Fiscal Year: 2018

This project has been tentatively selected and a project statement (request for proposals) is expected in July 2017. The project statement will be available on this world wide web site. The problem statement below will be the starting point for a panel of experts to develop the project statement.

Business Intelligence (BI), as applied in the private sector, is an umbrella term that refers to the analysis of disparate raw data using data mining techniques, on-line analytical processing, querying, and reporting/visualization to make better business decisions. BI techniques are well suited to Transportation System Management and Operations (TSM&O) as it is a data-oriented function that attempts to optimize the multimodal operational performance of the highway and transportation networks through operational strategies as well as project-based transportation system improvements. TSM&O is driven by diverse data and information from an expanding number of public and private sector sources, such as:
  • Data about traffic operations, environmental conditions, and infrastructure assets;
  • Driver and shipper trips and demand patterns;
  • Internal organizational resources and capabilities; and
  • Budget considerations including financing needs.
Transportation agencies’ TSM&O groups must integrate these data; generate relevant and timely information, knowledge, and intelligence; and incorporate the outcomes almost continuously into various agency and transportation system management decision-making processes. Some examples decisions that could be supported through applying BI data analysis and visualizations include:
  • Determining active operational management and response strategies—including those involving traffic engineering and operations and highway maintenance,
  • Establishing lifecycle financial investment strategies for operational programs and associated operations and ITS infrastructure systems based on data driven performance measures, and
  • Addressing human resource allocation and asset management as well as corporate financial and budget management.
Applying BI practices using integrated transportation operations and asset management information has the potential to improve system performance management through enhanced decision making relative to agency goals and objectives. The traditional focus has been on active operation of the transportation system based on situational awareness of traffic and environmental conditions by providing guidance on immediately viable tactics to mitigate recurring congestion and non-recurring disruptions. However, managing transportation investments also requires asset and operational performance data and information that are dynamically integrated with financial data and budget information. In both operational management and investment management contexts, Business Intelligence practices and methods could be more effectively incorporated by transportation agencies, which could enable enhanced trade-off analysis and more effective enterprise resource planning.

Research is needed on ways to use BI in identifying the value of investment in multi-modal operational improvements and strategies, especially with respect to programs for major investments and state of good repair. Research is also needed on how BI practices can lead to a higher level of confidence in the effectiveness of TSM&O programs among policy makers and to reduce (and perhaps eventually eliminate) the need to have parallel tracks for funding consideration.

The objective of this research is to develop a report defining a BI framework of TSM&O program management decision-making process types and example applications that encompass operational management, infrastructure management, investment management, as well as organizational and corporate management. The research will assess current processes and analysis methods that are foundations for how core decision-making processes occur: (1) within TSM&O programs, (2) in conjunction with associated and encompassing agency functions, and (3) in an integrated and supportive fashion by data and information associated with on-going operations. Significantly, the research will identify and consider how effective and emerging BI practices, models, or approaches from other industries can be applied to TSM&O and transportation agency program management processes. Particular attention will be paid to BI practices that reduce the cost of providing services or improves the quality and effectiveness of decision-making capabilities.

Project tasks are expected to: (1) Scan and synthesize current Business Intelligence best practices, technologies, and data analytics applications in the private sector and other industries and identify those that are potentially most applicable to TSM&O in nature, extent, and objectives; (2) Leverage TSM&O Capability Maturity Modeling (CMM) and other recent efforts to define and assess BI related business and decision-making processes within transportation agencies; (3) Incorporate findings of various private sector BI efforts that are recognizing the rapidly expanding sources, types, and overall quantity of relevant data; (4) Assess and define effective and emerging BI processes, methods, practices, tools, approaches and organizational models for the management and integration of these data into TSM&O decision-making processes. The development of case examples and a literature review can be used. The processes, methods, and performance management strategies should be developed so as to better demonstrate to policy makers the effectiveness of TSM&O programs; (5) Define a BI framework of TSM&O program management decision-making process types and example applications that encompass operational management, infrastructure management, investment management, organizational and corporate management, and others; and (6) Correlate data and data management characteristics, use cases, and needs to the framework.

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