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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.
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.
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.
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.
The grouping of similar traffic patterns and cluster assignment process represent the most critical steps in AADT estimation from short-term traffic counts. Incorrect grouping and assignment often become a significant source of AADT estimation errors. For instance, grouping a commuter traffic trend pattern into a recreational traffic trend may produce an erroneous AADT value. The traditional knowledge-based methods, often aided with visual interpretation, introduce subjective bias while grouping traffic patterns. In addition, the grouping requires personnel resources to process large amounts of data and remains inefficient with unapparent traffic patterns. The functional class grouping, a traditional method, also produces larger errors. Under limited resources and constraints, better methods and techniques may group sites with similar characteristics. The study uses Gaussian Mixture Modeling (GMM) for clustering and an enhanced neural network model (OWO-Newton or ONN) for classification of continuous count data. The researchers compare this modified approach with volume factor grouping and a traditional approach. The study uses Automatic Traffic Recorder (ATR) data from the Oregon Department of Transportation (ODOT) as a comparative case study. Overall, the proposed two-step approach, GMM-ONN, exhibits improved performance. The study observes an error difference of 6% to 27%, which is statistically significant at 5 percent level, between the GMM-ONN and other methods. The GMM-ONN method produces less than five percent error for urban interstates and less than ten percent for urban arterials and freeways. The study method meets the FHWA recommended AADT forecasting error of less than ten percent for commuter patterns. The GMM-ONN also produces less error when compared to studies based on the national average and Minnesota and Florida DOT count data. The lower AADT estimation errors and its distribution show an effective and reliable approach for AADT estimation using short-term traffic counts. Moreover, the lower standard deviation of errors shows the satisfactory accuracy of the AADT estimates. The study recommends the improved two-step process due to its accuracy, economical approach by using daily patterns, and ability to meet the agency's need for a low-cost traffic counting program. The GMM-ONN method not only minimizes judgment errors but also supplements the FHWA guidelines on recommending clustering techniques for grouping the traffic patterns.