A Robust Bottleneck Identification Method Using Noisy and Inconsistent Fixed-Point Detector Data

Bottleneck identification locates problematic segments on a freeway corridor and meanwhile provides information about the cause and characteristics of the congestion. It is a critical step in mitigating the urban congestion problem. Due to the wide availability of traffic surveillance data, researchers have been designing bottleneck identification algorithms based on archived traffic flow data. Those algorithms include rule-based, contour-map-based and simulation-based methods. However, these existing methods require traffic data with high accuracy and consistency, which may not always be the case in reality. In this paper, a new bottleneck identification method based on coordinate transformation on fundamental diagram is proposed. The algorithm is designed for fix-location detector data and can tolerate noise and inconsistency. Three loop detector datasets were collected at the city of Madison and the city of Milwaukee, WI, USA. The three datasets have different levels of data quality so that the effectiveness and robustness of the proposed algorithm can be tested. Meanwhile, a novel evaluation strategy for bottleneck identification in the absence of ground truth data was first introduced in this paper. Using this strategy, the proposed algorithm is compared with Chen’s method. The evaluation results indicate superior effectiveness and robustness of the proposed algorithm comparing to earlier methods.

Language

  • English

Media Info

  • Media Type: DVD
  • Monograph Title: TRB 89th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

Filing Info

  • Accession Number: 01152419
  • Record Type: Publication
  • Report/Paper Numbers: 10-1374
  • Files: TRIS, TRB
  • Created Date: Jan 25 2010 10:37AM