Assessing Roadway Traffic Count Duration and Frequency Impacts on Annual Average Daily Traffic Estimation PDF Download
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Author: Robert Krile
Publisher:
ISBN:
Category :
Languages : en
Pages : 23
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Book Description
Numerous factoring and baseline values are required to ensure annual average daily traffic (AADT) data are collected and reported correctly. The variability of numerous methods currently used are explored so that those in the traffic community will clearly know the limitations and the extent of each method used and how to properly utilize methods for their agency to obtain the necessary results. Federal Highway Administration (FHWA) Travel Monitoring Analysis System (TMAS) data from 14 years consisting of 24 hours of the day and 7 days of the week volume data from over 6000 continuous permanent volume traffic data sites in the United States comprised the reference dataset for this research. Randomly selected (with some constraints) sites each include one year of 100% complete daily reporting and the set of sites represent 12 functional classes, years 2000 through 2013, 43 states and DC, and various volume ranges. Four AADT estimation methods were examined for accuracy when data from various time periods were removed. This report is a final task report that summarizes identified inaccuracies with current methods that are used for AADT estimation, and includes the analysis methodology and summary statistics findings.
Author: Robert Krile
Publisher:
ISBN:
Category :
Languages : en
Pages : 23
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Book Description
Numerous factoring and baseline values are required to ensure annual average daily traffic (AADT) data are collected and reported correctly. The variability of numerous methods currently used are explored so that those in the traffic community will clearly know the limitations and the extent of each method used and how to properly utilize methods for their agency to obtain the necessary results. Federal Highway Administration (FHWA) Travel Monitoring Analysis System (TMAS) data from 14 years consisting of 24 hours of the day and 7 days of the week volume data from over 6000 continuous permanent volume traffic data sites in the United States comprised the reference dataset for this research. Randomly selected (with some constraints) sites each include one year of 100% complete daily reporting and the set of sites represent 12 functional classes, years 2000 through 2013, 43 states and DC, and various volume ranges. Four AADT estimation methods were examined for accuracy when data from various time periods were removed. This report is a final task report that summarizes identified inaccuracies with current methods that are used for AADT estimation, and includes the analysis methodology and summary statistics findings.
Author: Robert Krile
Publisher:
ISBN:
Category :
Languages : en
Pages : 41
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Book Description
The FHWA Travel Monitoring Analysis System (TMAS) volume data were utilized from 418 sites/years in the United States where data were available for all 24 hours of every day of the year. These sites collectively represented a wide range of AADT volumes, 9 functional classes, 35 states, and years 2000 through 2012. The TMAS hourly data were converted to daily ratios of volume to the overall AADT for the site. These daily volume ratios were fit to statistical analysis of variance models to estimate the mean changes in volume for national holidays and the days surrounding them. Further subsets of sites were utilized to model the traffic impacts of roadways near recreational areas and associated with special events. The report includes the analysis methodology and summary statistics findings.
Author: Karalee Klassen-Townsend
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Traffic volume data, commonly summarized as annual average daily traffic (AADT), is a fundamental input for transportation engineering decisions. Current traffic monitoring guidance provides insufficient detail on the development of AADT estimates from short-duration counts conducted within towns. This is due to limited knowledge of the attributes that characterize a town count and uncertainty about the temporal factors required to estimate AADT from short-duration town count data. This research addressed these gaps by using a decision algorithm and GIS analysis to identify which short-duration counts should be considered town counts and by developing and validating a methodology to estimate AADT from short-duration town count data. The analysis demonstrated that temporal factors generated from continuous counts conducted near towns could be reliably applied to short-duration town count data. This finding enables traffic monitoring authorities to leverage existing data and methods to improve the representativeness of traffic volume estimates in towns.
Author: Shawn Turner
Publisher:
ISBN:
Category : Global Positioning System
Languages : en
Pages :
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Book Description
In this study, the Texas A&M Transportation Institute (TTI) conducted an independent, follow-up evaluation of StreetLight Data’s 2019 traffic count estimates using MnDOT sources of traffic count data. At 442 permanent benchmark locations, TTI found that average annual daily traffic ( AADT) estimation accuracy by StreetLight Data has improved significantly since the 2017 evaluation, especially in moderate- to high-volume categories (i.e., more than 10,000 AADT). The mean absolute error ranged from 8% to 10% for locations greater than 10,000 AADT and gradually increased to 42% for sites with less than 1,000 AADT. TTI also found significant overestimation bias for low-volume roadways (i.e., less than 2,500 to 5,000 AADT). This result was present in the permanent benchmark sites and more pronounced in the 265 short-duration count sites. Based on these findings, TTI recommends that MnDOT consider a phased approach to using probe-based traffic count estimates: 1) Continue to maintain MnDOT permanent counter sites; 2) start using probe-based counts for about 90% of the moderate- to high-volume roadways (20,000 or more AADT); 3) continue to use traditional short-duration counts at the remaining 10% of the moderate- to high-volume roadways as a spot check to ensure that probe-based AADT estimates remain within acceptable tolerances in the next five to ten years; 4) periodically monitor the error of AADT estimates on low- to moderate-volume roadways (less than 20,000 AADT); and 5) once acceptable error tolerances for these lower-volume categories are reached, repeat Step 2 for these lower-volume categories.
Author: John H. Lemmerman
Publisher:
ISBN:
Category : Traffic flow
Languages : en
Pages : 38
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Author: Giuseppe Grande
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This research presents a series of projects that contribute to the understanding of how traffic variability affects the measurement and application of annual average daily traffic (AADT). AADT is the most fundamental traffic statistic in transportation engineering. It is defined as the number of vehicles expected to use a facility on an average day. However, traffic is known to experience periodical fluctuations over time; these periodicities are location-specific. This underlying variability in time and space can be lost when calculating and reporting AADT. This research comprises four research projects. The first evaluates the effectiveness of multiple AADT formulations using simulated data loss scenarios. It finds that a relatively new methodology, proposed by the Federal Highway Administration in the United States, removes a small, systematic bias (0.1%) from the existing calculation convention and reduces the width of the 95% confidence interval by 0.5%. The second project provides a method for measuring and reducing the error produced during the assignment step of the AADT estimation process. It applies this method to a case study, finding that the novel assignment method reduces errors by 2.5% on average. The third project explores the use of unconventional traffic data sources (passively-collected vehicle probe data) in tandem with conventional sources. The research finds that speed-based probe data are most closely correlated with truck-specific volume data, specifically around urban centres and along major trade routes. In the studied data, the Pearson correlation coefficient reached 0.9 at some sites. The final project tests the sensitivity of grade crossing design and regulation to predicted fluctuations in traffic. The results show that daily variations in traffic can cause sites to be apparently over- or under-designed for a day or group of days, when compared to regulatory standards. Moreover, they show that within-day variations can be used to express more detailed grade crossing exposure estimates than the daily averages that are used in current regulations. On aggregate, the research finds that, while AADT estimates are convenient to calculate and ubiquitously applied, there is a need to better disclose the source data and methodologies used to produce AADT estimates to avoid misuse and false assumptions about comparability. Further, AADT summarizes the traffic at a site into a single average volume, which fails to express the known periodical traffic variability at a site.
Author:
Publisher:
ISBN:
Category : Traffic flow
Languages : en
Pages : 68
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Book Description
Author: United States. Bureau of Public Roads
Publisher:
ISBN:
Category : Traffic flow
Languages : en
Pages : 52
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Book Description
Author: Nicholas J. Garber
Publisher:
ISBN:
Category : Traffic estimation
Languages : en
Pages : 19
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Book Description
Estimates of annual average daily traffic (AADT) volumes are important in the planning and operations of state highway departments. These estimates are used in the planning of new construction and improvements of existing facilities, and, in some cases, in the allocation of maintenance funds. It is, therefore, important that any method used in obtaining the estimates provide data of sufficient accuracy for the intended use. This importance of having reliable and current data on traffic volumes at hand is generally recognized, and over the years data collection programs have tended to expand. This expansion has ledto huge amounts of money being spent annually for the collection and analysis of traffic data. Renewed efforts are, however, now being made to reduce the annual expenditure on traffic counts while at the same time maintaining the desired level of accuracy. A study is, therefore, being carried out by the Council to develop an optimal counting program for the state. This interim report presents the results of that portion of the study in which the feasibility of estimating AADT volumes from short counts was established. The procedure was first to use 1980 data for 16 continuous count stations to determine periods that are stable throughout the year for different short counts. It was found that stable periods for short counts occurred mainly on Mondays, Tuesdays, and Wednesdays, and expansion factors were then developed for short counts of different durations and different starting times for these days. The expansion factors were then used to estimate 1981AADT's from short counts extracted from data obtained in 1981 continuous counts. The results indicate that relative errors of less than 10% were obtained for AADT's estimated from counts of 6-, 8-, 10-, and 12-hour durations on Mondays, Tuesdays, and Wednesdays-The results for Tuesdays and Wednesdays tended to be more accurate than those for Mondays, and counts taken between February and November tended to give more accurate results than those taken in January and December.
Author: Gary A. Davis
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 104
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Book Description
This report describes the application of Bayesian statistical methods to several related problems arising in the estimation of mean daily traffic for roadway locations lacking permanent automatic traffic recorders. A lognormal regression model is fit to daily count data obtained from automatic traffic recorders, and this model is then used to develop (1) a heuristic algorithm for developing traffic sampling plans which minimize the likelihood of assigning a site to an incorrect factor group, (2) an empirical Bayes method for assigning a short-count site to a factor group using the information in a sample of traffic counts, and (3) an empirical Bayes estimator of mean daily traffic which allows for uncertainty concerning the appropriate factors to be used in adjusting a sample count.
