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

PDFTech ReportBDK83 977-01Peak hour trafficTraffic dataTraffic volumeLevel of serviceMeasures of effectivenessTraffic congestionTransportation planningUrban areasTraffic surveillanceFloridaFlorida. Dept. of TransportationMoses, RenFAMU-FSU College of Engineering (Tallahassee, Fla.). Dept. of Civil EngineeringUnited States. Federal Highway AdministrationUS Transportation CollectionTransportation planners and traffic engineers are increasingly interested in traffic analysis tools that analyzedemand profiles and performance that go beyond analysis of the traditional peak hours and extend the analysis toother hours of the day. The primary objective of this research was to utilize historical traffic data from telemeteredtraffic monitoring sites (TTMS) to analyze 24-hour peaking relationship to various performance measures. Datafrom 26 TTMS sites located in large urbanized areas showed that the 99th percentile hourly volume was close to2,000 vehicles per hour per lane on limited access facilities, i.e., freeways, toll roads, and HOV lanes. The 99thpercentile 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 MobilityReport by Texas A&M Transportation Institute in which speed reduction factor (SRF) is calculated by dividing theaverage combined peak period speed by the free-flow speed. The results of congestion level analysis usingpermanent count stations data showed that on limited access facilities, severe congestion occurs in only 4 hours ofthe 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 in11 hours and relatively free flowing operations in 8 hours of the day. The results of the linear models for the peakvolumes developed from the hourly data analyzed by lane showed that area type was not a significant predictingvariable. Gaussian models developed for weekday hourly volumes were able to reasonably replicate the peakingprofiles with R-squared values higher than 0.95 for all facility types. The Gaussian hourly volume models can alsobe used to predict future traffic volumes if the characteristics of future trip making are known. Such characteristicsmay be used to modify the amplitude, centroid, width and number of peak periods. Estimates of future change intraffic volumes can be obtained by multiplying the average function of the hourly volume by elasticity parameterand the fraction of the change in cost of travel. Estimation of future change in traffic volume can be used bytransportation planners to determine if the peak period is expected to spread