Operations Data for Planning

The use of operations data is vital to the advancement of transportation planning as metropolitan planning organizations (MPOs) and State departments of transportation (DOTs) shift to a more performance-based planning process that requires system performance data to guide decisionmaking. The use of operations data in planning cuts across almost all planning activities, including:

• Setting outcome-based objectives
• Developing and tracking performance measures
• Identifying system performance needs and problems
• Analyzing and evaluating scenarios, programs, projects, or strategies
• Prioritizing and selecting programs and projects
• Monitoring and evaluating the impacts of implemented programs and projects

Examples of operations data include:

• Automated spot traffic data (e.g., loop, acoustic, radar traffic detectors) including volume, speed, occupancy and other data
• Travel time data (probe data)
• Incident logs
• Crash data
• Operational data (e.g., logs of messages displayed on variable message signs, 511 calls or alerts)
• Weather data^[1]

Transportation agencies in major metropolitan areas often have large amounts of archived or real-time data collected through road sensors and their central control systems, although there are significant limitations to the use of this data for performance monitoring.[2] The recent emergence of probe data is creating more opportunities for transportation operating agencies and MPOs to monitor and analyze travel times according to elements of the network and routes. There are now several commercial probe data systems available based on cell phone location, GPS equipped vehicles (usually fleets), and cell phones or other personal devices with GPS systems.

Operations data is needed for the analysis and simulation tools used to quantitatively evaluate and forecast the potential impacts of management and operations strategies on transportation system performance, safety, and other measures.

Data sharing between agencies is a critical way to cost-effectively gain a region-wide or system-wide perspective on transportation system performance. Collaborating on data collection requires that agencies agree to standard data definitions, levels of quality, and a consistent format. By sharing data, agencies in the region can "collect once, use often" and avoid duplicating effort.