Proposed Data-Driven Performance Measures for Comparing and Ranking Traffic Bottlenecks

To justify investments towards improved traffic operations, engineers and policy-makers need scientific and accurate methods of congestion measurement. However, status-quo methods are limited and/or outdated. Peak-hour analyses are becoming outdated as a sole source of traffic assessment, because they fail to account for changing conditions throughout the year. There has been a movement towards “reliability” modeling, which attempts to capture these annual effects. But due to significant input data and calibration requirements, the reliability models suffer from practicality issues. Next, there have been recent improvements in data-driven intelligent transportation system (ITS) technologies, which identify congestion in real time. However, there is room for improvement in the robustness of performance measures derived from these technologies. Finally, some engineers have compared and ranked congested locations (i.e., bottlenecks) on the basis of experience and judgment. Despite their cost-effectiveness, judgment-based qualitative assessments will lack credibility unless backed by quantitative results. In a recent Federal Highway Administration study, congestion measurement was a primary area of emphasis. This paper discusses project-specific software development, which produced new and innovative performance measures for congestion measurement. It will present concepts and evidence to imply superiority of the proposed new measures. This paper is intended to serve as a preview of a future full journal paper; which will rank ten or more real-world bottlenecks according to new and old performance measures, to demonstrate impacts of the new measures. It is hoped that the new performance measures will be adopted by states and/or commercial products, for a new level of robustness in congestion measurement.

Language

  • English

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Filing Info

  • Accession Number: 01606192
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jun 15 2016 9:52AM