详情

原文传递 Twenty-Four Hour Peaking Relationship to Level of Service and Other Measures of Effectiveness.

题名：	Twenty-Four Hour Peaking Relationship to Level of Service and Other Measures of Effectiveness.
作者：	Moses, R.
关键词：	Transportation planning, Traffic engineers, Peak hour traffic, Traffic data, Modeling analysis, Measure of effectiveness, Peaking relationship, Vehicles, Telemetered traffic monitoring sites (TTMS), speed reduction factor(SRF)
摘要：	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.
总页数：	Moses, R.
报告类型：	科技报告