Dynamic All-Red Extension at Signalized Intersection: Probabilistic Modeling and Algorithm

Dynamic All-Red Extension (DARE) has recently attracted research interest as a nontraditional intersection collision avoidance method. We propose a probabilistic model to predict red light running (RLR) hazard for dynamic all-red extension system. The RLR hazard, quantified by a predicted encroachment time, has contributory factors including the speed, distance and car-following status of the violator and the empirical distribution of the entry time of conflict traffic. An offline data analysis procedure is developed to set the parameters for RLR hazard prediction. An online two-dimensional normal model is developed to predict the vehicle’s stop-go maneuver based on speeds at advanced detectors. Additionally, unlike most prediction models which are designed to minimize mean errors, our model identifies two types of errors, namely the false alarm and missed report. The capability of distinguishing these two types of errors is crucial to the effectiveness of dynamic systems. To quantify the trade-off between these two types of errors in the system design, a system operating characteristics (SOC) function is then defined. Performance of the proposed model and its prediction algorithm is evaluated using data collected from a field intersection. At a false alarm rate of 5%, the algorithm reach a correct detection rate of over 65% to over 90% for various legs of the test intersection. Performance evaluation results showed that the proposed models and algorithms within the DARE framework can effectively detect the RLR hazards.

• Record URL:
  http://www.path.berkeley.edu/PATH/Publications/PDF/PWP/2011/PWP-2011-01.pdf
• 
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/20335
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/710863200
• Corporate Authors:

  University of California, Berkeley

  Berkeley, CA  United States  94720-1720

  University of California, Berkeley

  California PATH Program, Institute of Transportation Studies
  Richmond Field Station, 1357 South 46th Street
  Richmond, CA  United States  94804-4648

  California Department of Transportation

  1120 N Street
  Sacramento, CA  United States  95814
• Authors:
  • Zhang, Liping
  • Zhou, Kun
  • Zhang, Wei-Bin
  • Misener, James A
• Publication Date: 2011-1

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 37p
• Serial:
  • PATH Working Paper
  • Publisher: University of California, Irvine

Subject/Index Terms

• TRT Terms: Acceleration (Mechanics); Algorithms; Crash avoidance systems; Drivers; Errors; False alarms (Security); Following distance; Mathematical models; Red interval (Traffic signal cycle); Red light running; Speed
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory; I73: Traffic Control;

Filing Info

• Accession Number: 01338269
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Report/Paper Numbers: UCB-ITS-PWP-2011-01
• Contract Numbers: Caltrans task order 65A0363
• Files: CALTRANS, NTL, TRIS, STATEDOT
• Created Date: Apr 29 2011 7:36AM