Consider using private sector probe-based data to develop and monitor congestion and reliability measures required by MAP-21.

Lessons from the National Capital Region Transportation Planning Board's experience with probe-based traffic data from the I-95 Corridor Coalition.

August 1, 2012
Washington,District of Columbia,United States

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Lesson Learned

In the past three years, staff of the National Capital Region Transportation Planning Board (TPB), the officially designated MPO for Washington, D.C., Suburban Maryland, and Northern Virginia have taken advantage of emerging private sector probe-based traffic data provided by the I-95 Corridor Coalition Vehicle Probe Project to develop a number of congestion and reliability performance measures, tailored for the National Capital Region but which also can be explored on a national scale. The Vehicle Probe Project, a flagship project of the I-95 Corridor Coalition, was launched on July 1, 2008. As an affiliate member of the coalition, the TPB was one of the earliest users of the historical data archived by this project and has applied the data in a number of planning activities. To a significant extent, the Vehicle Probe Project data has been acting as a "game changer" in the TPB's planning programs and processes, and it will undoubtedly help to support the TPB to meet the MAP-21 requirements on monitoring and reporting congestion and reliability of the highway system.

The following lessons learned are worth noting for transportation professionals and will help to pave the path forward in developing high quality performance measures to meet MAP-21 requirements:
  • Consider using private sector probe-based traffic data to advance congestion and reliability monitoring efforts. The private sector traffic data has greatly advanced monitoring efforts, thanks largely to the unprecedented coverage and details of the new data.
  • Prepare hierarchically structured performance measures, instead of a menu of measures, based on federal requirements, the region’s tradition, available data, and public acceptance. Such a hierarchical framework could promote the inter-relationship among different measures and facilitate the selection of a subset of appropriate measures for different applications and requests.
  • Understand that there is no "magic" performance measure to capture all the aspects of congestion and reliability while maintaining a high-level of public acceptance. Statistically and mathematically, such a measure could be developed (like some indicators in economics), but the learning curve of that measure could be very long and difficulties could arise in the interpretation and communication of the measure.
  • Develop better ways to explain travel time reliability measures to the public. Feedback from participants of the Regional Transportation Priorities Plan (RTPP) public outreach activities indicate that the regional average planning time index (phrased as "Extra time needed for on-time arrival") is not intuitive, even when presented along with innovative graphs. It might be more straightforward to explain the 95th reliable travel time for a specific corridor. The overarching reliability measure – standard deviation of travel times – is not suitable for public communication. Future efforts are needed to either explain regional Planning Time Index in a more straightforward manner or identify alternative reliability measures (e.g., Failure Rate or Percentage of On-Time Arrival) for public outreach.
  • Allocate sufficient time and effort to obtaining the key congestion measure of delay. A significant amount of time and effort have to be allocated to match the speed and volume data. Agencies should consider this factor before making a decision to use delay as the primary congestion measure.
  • Develop a standard data processing practice in calculating performance measures. Even with the same definition, the value of a performance measure can vary significantly depending on many details in the calculating process. A standard procedure is vital to ensure consistency in comparing different measures or the same measure overtime.
  • Recognize that technical, professional oriented performance measures have their place. While not all the performance measures are suitable for public communication (e.g. standard deviation and coefficient of variation), keeping them for professional communication and applications such as benefit-and-cost analysis has been effective.
To meet the MAP-21's new requirements in monitoring and reporting highway congestion and system reliability in a performance-based planning environment, transportation agencies have to establish necessary data elements and commonly agreed congestion and reliability performance measures. Based on the National Capital Region MPO's experience in applying a private sector probe-based traffic data in its planning programs and processes, this paper demonstrated the effectiveness and potentials of such data, which can be provided by several competitive data vendors, in making up the gaps that exist between the new requirements and many transportation agencies' state of practice. The paper also identified an opportunity to establish a standardized national congestion and reliability monitoring program, which could also be tied into the existing Highway Performance Monitoring System (HPMS). To achieve this, both standardized data and commonly agreed performance measures are necessities. This paper proposed a hierarchical framework of performance measures that can inform and facilitate performance measure identifications. This paper could also be a reference to other transportation agencies and entities in identifying appropriate performance measures that can satisfy their specific needs. Some observations and lessons provided in this paper could be useful in future performance measures research.

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Using New Data Sources to Meet MAP-21 Requirements for Performance-Based Planning

Author: Wenjing Pu and Andrew J. Meese

Published By: Transportation Research Board

Source Date: August 1, 2012

URL: http://www.mwcog.org/uploads/committee-documents/ZF1cVldb20130212105822.pdf

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Liz Greer


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Lesson ID: 2013-00656