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For highway maintenance and planning purposes, it is desirable to characterize each road segment by its traffic flow[such as the annual average daily traffic (AADT) and the AADT for each vehicle class], by the weight distribution of vehicles that travel on its roads[such as the annual average daily equivalent single axle loadings (ESAL) and the annual average daily weight per vehicle for each vehicle class]. As with almost any data collection effort, the monitoring data suffer from errors from many sources. This report summarizes results of a two year empirical research effort, which was sponsored by the Federal highway Administration, (i) to study and characterize the variability in the traffic data (volume, classification, and weight) from the continuously monitored road segments, and (ii) to study the extent to which this variability is transferred to, and affects the precision of the data produced form the road segments which are monitored only one or two days each year. The ultimate hope is not only that states will eventually be able to publish an estimate of a characteristic such as AADT for each road segment, but also that each estimate will be accompanied by a statement of how good the estimate is in terms of the estimated variability or precision which will likely be experienced as a coefficient of variation (i.e., the quotient of a standard deviation and a mean). This report provides highlights of research reported in five working papers.
Each state in the United States can be viewed as a universe of road segments. For each road segment in each state, it is desired to know various traffic characteristics based on count data, classification count data, and weigh-in-motion data. These data are absolutely essential for highway design, maintenance, safety, and planning. Given no cost constraints, each road segment would be continuously monitored every day of the year. However, in practice, a few road segments are monitored continuously every day of the year to produce annual characteristics of traffic flow. The remaining road segments are monitored for one or two days each year, and this resulting data are àdjusted ̀(using factors based on data collected from the continuously monitored road segments) to produce estimates of annual characteristics. With this general approach, each state strives to provide estimates of annual characteristics for each road segment within its jurisdiction. In 1985, the Federal Highway Administration (FHWA) published the Traffic Monitoring Guide to assist states in achieving this end. As with almost any data collection effort, the monitoring data suffers from errors from many sources. In this paper, we report some empirical findings in a research project sponsored by the FHWA. This research project studied the variability in the traffic data from the continuously monitored road segments from state(s) and, the extent to which this variability is transferred to and affects the precision of the data produced from the road segments which are monitored only one or two days each year. The ultimate hope is that states will eventually be able to not only publish an estimate of a characteristic such as Average Annual Daily Traffic (AADT) for each road segment, but also that each estimate will be accompanied by a statement expressing how good the estimate is in terms of its estimated variability or precision, which will likely be expressed as a coefficient of variation.
"One of the most common traffic volume parameters reported by statewide traffic monitoring programs is annual average daily traffic (AADT). Departments of Transportation (DOT) and other state agencies use a series of continuous vehicle detection devices in association with smaller more mobile short-term counts. Once the short-term counts are recorded a series of adjustment factors (time of day, day of week, month of year, or seasonal) are applied to the short-term counts. The end result is an estimated AADT for a particular segment of roadway. Traditionally, as defined in section two of the Traffic Monitoring Guide (TMG), there are three methodologies, geographic/functional assignment of roads to groups, cluster analysis and the same road application factor. In each case, there are advantages and disadvantages and currently there is not a final peer reviewed nationally suggested method. The benefits associated with this research include an improved method for estimating AADT throughout Ohio"--Technical report documentation page.
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.
The objective of these AASHTO Guidelines is to improve the quality of the traffic information that supports decisions at all levels of the transportation profession. The Guidelines provide a reference for professional traffic monitoring and establish a process for adoption of national traffic nomitoring standards. They specifically address concerns of state transportation agencies.
The Center for Transportation Research and Education (CTRE) issued a report in July 2003, based on a sample study of the application of remote sensed image land use change detection to the methodology of traffic monitoring in Blackhawk County, Iowa. In summary, the results indicated a strong correlation and a statistically significant regression coefficient between the identification of built-up land use change areas from remote sensed data and corresponding changes in traffic patterns, expressed as vehicle miles traveled (VMT). Based on these results, the Iowa Department of Transportation (Iowa DOT) requested that CTRE expand the study area to five counties in the southwest quadrant of the state. These counties are scheduled for traffic counts in 2004, and the Iowa DOT desired the data to 1) evaluate the current methodology used to place the devices; 2) potentially influence the placement of traffic counting devices in areas of high built-up land use change; and 3) determine if opportunities exist to reduce the frequency and/or density of monitoring activity in lower trafficked rural areas of the state. This project is focused on the practical application of built-up land use change data for placement of traffic count data recording devices in five southwest Iowa counties.