Provide ITS data at different aggregation levels as well as unaggregated data to satisfy diverse user needs.
National experience with ITS data archiving, sharing and usage.
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United States

A Literature and Best Practices Scan: ITS Data Management and Archiving


To manage and operate ITS technologies to exceed customer expectations, data is needed on the structure, status, use and behavior of the entire transportation system. With the advent of Archived Data User Service (ADUS), the National ITS Architecture officially embraced the concept of saving (retaining and archiving) real-time, operational data for other non-real-time applications, such as transportation planning, safety analyses, and research. There are three major aspects to fulfilling data needs: understanding what data is needed by the full range of stakeholders; creating/ maintaining the mechanisms to gather/ store the data; and providing convenient, timely and affordable access to the data. The report provides examples of who can profit from data, what types of data they would like and what benefits can accrue if these users obtain the quality of data they seek. This report, performed for the Wisconsin Department of Transportation, overviews the current data collection and management environment for improved data archiving. Among the lessons learned are the need to improve data quality, the need to provide ITS data at different aggregation levels, and the need to provide easy and low-cost access to ITS data.

Lessons Learned

Many stakeholders including planning, operations, and research staff, use different levels of aggregation. They are interested in performing similar analyses at different aggregation levels. One solution to the need for different levels of aggregation might be to save the basic aggregated data for current needs and use innovative archiving capabilities to provide advanced data users with access to raw, unaggregated data. There does not appear to be a least common denominator of data aggregation that is significantly favored, other than simply raw, unaggregated data.

Take into account the various users' needs:

  • Transportation planners require the most aggregated data. They require data aggregated for periods of 15 minutes or more. They often require data for various segments and time periods.
  • Traffic management operators often require data that are less aggregated (aggregation levels of 1, 5, or 15 minutes) than the transportation planners. The incident detection algorithm development requires data at intervals of less than 1 minute while typical applications evaluating highway capacity or ramp metering strategies may utilize data at the 5-minute aggregation level.
  • Transportation researchers typically desire the least aggregated data of all stakeholders. They will often use data in the least aggregated form available (i.e. data over a 20-second period, or individual probe vehicle data). Advanced data users such as transportation researchers generally have a significant amount of computing power and data manipulation tools at their disposal to assist them in analyzing large data sets.

For example, the research performed at the Institute of Transportation Studies (ITS) at the University of California at Berkeley has used unaggregated ITS data supplied by loop detectors to evaluate traffic features of freeway bottlenecks on two freeways in Toronto, Ontario, Canada. Speed, occupancy, and volume data were collected every 30 seconds along the Queen Elizabeth Way and the same data were collected every 20 seconds along the Gardiner Expressway.

Identifying which aggregation levels are necessary for different applications is indispensable. Different uses of ITS data require different levels of detail. Design and operational applications commonly require detailed data for shorter roadway sections and time intervals. Planning applications require the most aggregated data and commonly require historical data over extended sections of roadway and periods of time. Aggregating data at the right level is important because it helps transportation planners, traffic management operators, and transportation researchers to more effectively conduct the analyses and answer their questions of interest.

A Literature and Best Practices Scan: ITS Data Management and Archiving

A Literature and Best Practices Scan: ITS Data Management and Archiving
Publication Sort Date
Henry X. Liu, Rachel He, Yang Tao, and Bin Ran
University of Wisconsin

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Application Areas

Focus Areas Taxonomy: