Twenty-four hour peaking relationship to level of service and other measures of effectiveness.

Details

• Creators:
  Moses, Ren
• Corporate Creators:
  FAMU-FSU College of Engineering (Tallahassee, Fla.). Dept. of Civil Engineering
• Corporate Contributors:
  United States. Federal Highway Administration
• Subject/TRT Terms:
  • Peak hour traffic
  • Traffic data
  • Traffic volume
  • Level of service
  • Measures of effectiveness
  • Traffic congestion
  • Transportation planning
  • Urban areas
  • Traffic surveillance
• Publication/ Report Number:
  BDK83 977-01
• Resource Type:
  Tech Report
• Geographical Coverage:
  Florida
• Corporate Publisher:
  Florida. Dept. of Transportation
• Abstract:
  Transportation planners and traffic engineers are increasingly interested in traffic analysis tools that analyze ; demand profiles and performance that go beyond analysis of the traditional peak hours and extend the analysis to ; other hours of the day. The primary objective of this research was to utilize historical traffic data from telemetered ; traffic monitoring sites (TTMS) to analyze 24-hour peaking relationship to various performance measures. Data ; from 26 TTMS sites located in large urbanized areas showed that the 99th percentile hourly volume was close to ; 2,000 vehicles per hour per lane on limited access facilities, i.e., freeways, toll roads, and HOV lanes. The 99th ; percentile hourly volume did not reach 1,000 vehicles per hour per lane on divided and undivided arterial roads. ; Congestion levels in a 24-hour period were analyzed using methodology contained in the 2012 Urban Mobility ; Report by Texas A&M Transportation Institute in which speed reduction factor (SRF) is calculated by dividing the ; average combined peak period speed by the free-flow speed. The results of congestion level analysis using ; permanent count stations data showed that on limited access facilities, severe congestion occurs in only 4 hours of ; the day, moderate congestion in 10 hours of the day, and relatively free flowing conditions in 10 hours of the day. ; For divided and undivided arterial roads, severe congestion occurs in 5 hours of the day, moderate congestion in ; 11 hours and relatively free flowing operations in 8 hours of the day. The results of the linear models for the peak ; volumes developed from the hourly data analyzed by lane showed that area type was not a significant predicting ; variable. Gaussian models developed for weekday hourly volumes were able to reasonably replicate the peaking ; profiles with R-squared values higher than 0.95 for all facility types. The Gaussian hourly volume models can also ; be used to predict future traffic volumes if the characteristics of future trip making are known. Such characteristics ; may be used to modify the amplitude, centroid, width and number of peak periods. Estimates of future change in ; traffic volumes can be obtained by multiplying the average function of the hourly volume by elasticity parameter ; and the fraction of the change in cost of travel. Estimation of future change in traffic volume can be used by ; transportation planners to determine if the peak period is expected to spread.
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• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29137/dot_29137_DS1.pdf
• File Type:
  [PDF - 8.75 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:060be649491ea4eb2458a4a5d9976b03c6ccd88a6adc6f893495e67135bd9a54