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:
  • Freeway operations
  • Performance measurement
  • Reliability (Statistics)
  • Traffic congestion
  • Peak hour traffic
  • Sensitivity analysis
  • Algorithms
  • Traffic delays
  • Traffic incidents
  • Traffic volume
  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.
  More +
• Format:
  PDF
• Funding:
  14-8691
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29500/dot_29500_DS1.pdf
• File Type:
  [PDF - 2.51 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:c02978f6611e11f382e5054bd0f536e11082ebc9f1c76b2f3b94d072b5f22eeb1559a45847a43e7f8eb696ee23b767f90501a9e93babfa645da44d3bfe2754c1