The Mobility Monitoring Program (http://mobility.tamu.edu/mmp/) provides valuable insights with respect to using archived traffic detector data for monitoring highway performance (e.g., traffic congestion and travel reliability). The Mobility Monitoring Program was initiated in 2000 using archived freeway detector data from 10 cities. By 2004, the Program had grown to include nearly 30 cities with about 3,000 miles of freeway. Over the first four years of the Program, the project team gained valuable experience in the course of gathering archived data from State and local agencies. These experiences were captured in the report "Lessons Learned: Monitoring Highway Congestion and Reliability Using Archived Traffic Detector Data." The lessons documented in this report focus on three general areas: analytical methods, data quality, and institutional issues. They are useful to the Federal Highway Administration (FHWA) as it expands the national congestion monitoring program and to State and local agencies as they develop their congestion monitoring capabilities.
Public agencies need to make decisions based on limited or less-than-ideal information. Performance monitoring and the associated decisions stemming from transportation system performance data should be viewed in this context. Some analysts believe that a wide gap remains between a multi-modal, system-wide performance measurement system and the available data to support it. Some agencies may be taking a "wait-and-see" attitude in regards to using archived data from traffic management centers. Other agencies may be hoping that probe vehicle data from cell phones or vehicle monitoring systems will solve the data gap for performance monitoring. Some agencies may rely only on their data and not trust data collected by another agency. Yet numerous practitioners around the country have been using available data resources to make informed decisions about system 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.
Although a public agency's existing data set for performance measures may not be ideal, the agency should learn how to work with this data instead of idly waiting for a better data set to become available. In this same capacity, travel time modeling and estimation techniques should be used when better data are not available. An agency can make use of multiple sets of non-ideal data to make informed transportation decisions, and consequently gain experience for and be more productive when more ideal data become available.
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