Practical Challenges with Rapid Estimation of Incident-induced Delay for Incident Management

Spot speed and vehicle count measurement has been the most widely accepted performance monitoring method for traffic operations data collection by transportation agencies. Spot-speed based and cumulative-count based delay estimation methods are typically deployed by practitioners and researchers alike for rapid estimation of delays as a precursor to congestion mitigation. In this paper, these commonly used incident-induced delay estimation methodologies, that are based on queuing theory or shockwave analysis models, are reviewed and validated against microscopic simulation of a real-life incident. For the simulation model, traffic data was obtained through the local Traffic Management Center’s detection system and the incident timeline was constructed using incident logs. The comparison revealed challenges related to noisy data and the failure of spot-speed measurements to adequately capture heterogeneity in congested traffic, which rendered the methodologies impractical for field use. In the absence of any alternative method to accurately quantify delay within the constraints of field observational data, a regression model was developed using data from a non-exhaustive set of incident scenarios simulated using Vissim®, to help obtain rapid estimates of delays for incidents with varying characteristics occurring under varying base conditions. This regression model can aid in resource allocation for efficient incident management and identification of influence factors.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 22p

Subject/Index Terms

Filing Info

  • Accession Number: 01763960
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-03947
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:16AM