Use available datasets that can eventually capture the benefits of improved or ideal data, instead of waiting for a silver bullet dataset.
National experience using archived traffic detector data for monitoring highway performance.
- Do not wait idly for a "silver bullet" data set or collection technique. Change in transportation is usually evolutionary rather than revolutionary, and agencies may find that what seemed like an ideal data source also has problems. Of course, agencies must become comfortable with available data resources and their features and limitations. In a limited number of instances, available data may be so poor as to not be considered for performance monitoring. Data of such poor quality should be obvious to even the casual observer.
- Use available data resources within an analysis framework that can eventually capture the benefits of improved or ideal data. An example of this practice comes from the Florida Department of Transportation (DOT). In their mobility performance measures program, the Florida DOT has designated the reliability of highway travel as a key mobility measure for their State highway system. Ideally, a travel reliability measure would be formulated from a continuous (e.g., 24 hours a day, 365 days per year) data collection program over all highways. However, like most states, the Florida DOT does not have such a continuous data collection program, even in major cities. Instead, the state is planning to collect data for their reliability measure through a combination of archived data and additional floating car data collection.
- Use non-ideal data sets to become familiar with using performance measures. Another advantage of embarking on a performance-monitoring program even without the ideal data set is that agencies grow accustomed to reporting and using measures in their day-to-day management activities and decision-making. These functions are ultimately what performance measurement should be achieving. By starting now, agencies learn how to best use performance measures for their own uses.
- Use speed/travel time modeling and estimation techniques when link travel time data are not readily available. Many performance-monitoring programs rely on speed or travel time-based performance measures. As such, link travel time data form the basis for performance monitoring as well as numerous other advanced transportation applications (such as traveler information, dynamic routing, etc.). Because link travel time data are not readily available or cheaply collected for most highway links, many performance-monitoring programs have relied on speed/travel time modeling and estimation techniques.
- Remember that travel time modeling and estimation techniques will always be necessary. Some analysts have suggested or implied that if one cannot directly measure link travel times, then travel time-based performance measures are not feasible. Other analysts predict a future in which link travel times will be ubiquitous and travel time modeling or estimation will be unnecessary. The inherent nature of a performance-based planning process requires that travel time-based performance measures be estimated for future planning scenarios. Travel time modeling and estimation techniques will always be necessary (even with widespread availability of collected link travel times), particularly in a performance-based planning process. One of the challenges will be to ensure that estimation techniques produce roughly compatible travel time estimates as those from direct measurement.
Author: Shawn Turner, Rich Margiotta, and Tim Lomax
Published By: U.S. Department of Transportation, Federal Highway Administration
Source Date: 10/1/2004
EDL Number: 14059
Other Reference Number: FHWA-HOP-05-003URL: http://ops.fhwa.dot.gov/publications/lessons_learned/index.htm#toc
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