Data-driven freeway performance evaluation framework for project prioritization and decision making.

Details

• Creators:
  Liu, Xiaoyue Cathy ; Chen, Zhuo
• Corporate Creators:
  University of Utah. Department of Civil and Environmental Engineering
• Corporate Contributors:
  United States. Federal Highway Administration
• Subject/TRT Terms:
  Algorithms Decision Making Evaluation Freeway Operations Peak Hour Traffic Performance Measurement Reliability (Statistics) Sensitivity Analysis Traffic Congestion Traffic Delays Traffic Incidents Traffic Volume Weather Conditions

  More +
• Publication/ Report Number:
  UT- 15.05
• Resource Type:
  Tech Report
• Geographical Coverage:
  Utah
• Corporate Publisher:
  Utah. Dept. of Transportation. Research Division
• Abstract:
  This report describes methods that potentially can be incorporated into the performance monitoring and planning
  
  processes for freeway performance evaluation and decision making. Reliability analysis is conducted on the selected
  
  I-15 corridor by employing congestion frequency as the performance measure and hot spots during peak hours are
  
  identified through sensitivity analysis. A data-driven algorithm combining spatiotemporal analysis and shockwave
  
  theory is developed and applied to historical traffic data and incident records to determine the secondary incidents.
  
  The results show that the occurrence of secondary incidents is highly related to weather and roadway conditions.
  
  Incident-induced delay is further quantified through spatiotemporal pattern recognition. The average delay induced
  
  by incidents aligns well with the incidents’ severity and impact. There were several hot spots suffering from higher
  
  delays and are explored in further details. A statistical mechanism is developed to determine the adverse weather
  
  impact on travel. Using the weather records in 2013 and mapping with the PeMS traffic database, the volume and
  
  delay under normal condition are estimated and compared with the condition under adverse weather. The analysis of
  
  different roadway conditions reveals that the general parabolic pattern of speed and volume disappear under severe
  
  adverse weather condition. The mechanism is able to identify the causes for reduced volume under a variety of
  
  scenarios through empirical data, either due to roadway capacity reduction or travel demand reduction.
  
  
• Format:
  PDF
• Funding:
  14-8691
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:c02978f6611e11f382e5054bd0f536e11082ebc9f1c76b2f3b94d072b5f22eeb1559a45847a43e7f8eb696ee23b767f90501a9e93babfa645da44d3bfe2754c1
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29500/dot_29500_DS1.pdf
• File Type:
  [PDF - 2.51 MB ]