Browsing by Subject "Traffic counts"

Now showing 1 - 5 of 5
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    Estimation Theory Approach to Monitoring and Updating Average Daily Traffic: Final Report
    (Minnesota Department of Transportation, 1997-01) Davis, Gary A.
    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. An evaluation of these methods confirmed results reported in other work, in which a sample consisting of two, 1-week counts was found to be adequate for overcoming prior uncertainty concerning the correct adjustments for a site. The empirical Bayes method produced sample-based estimates of mean daily traffic that on the average differed by 5%-6% from estimates based on daily counts for an entire year. The report concludes with suggestions for agencies wishing to implement these methods.
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    Operational Evidence of Changing Travel Patterns
    (Institute of Transportation Engineers, 1994) Levinson, David M; Kumar, Ajay
    This paper utilizes a traffic counts database covering a ten year period (1976-1985) to identify travel trends for Montgomery County, a suburb of Washington D.C. Generally, travel behavior is analyzed using person based travel survey data. The use of traffic counts to understand travel behavior is a relatively new approach. Unlike household surveys, which are typically characterized by respondent and sample bias, and require special effort for their collection, traffic counts are routinely collected by Departments of Transportation and provide the best available measure of observed traffic volumes. The study provides fresh evidence to support some of the earlier findings: an increase in lateral commuting as a share of travel, changes in work and non-work trip proportions, and increase in peak spreading. An interesting result in this paper relates to a more pronounced directionality in radial as compared with lateral trips. The relative symmetry of traffic flows along lateral routes compared with radial routes results in better utilization of the suburban road network. Non-work trips emerge as the more elastic trips, shifting to off-peak hours with an increase in congestion.
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    Optimizing Automatic Traffic Recorders Network in Minnesota
    (Minnesota Department of Transportation, 2016-01) Gupta, Diwakar; Tang, Xiaoxu
    Accurate traffic counts are important for budgeting, traffic planning, and roadway design. With thousands of centerline miles of roadways, it is not possible to install continuous counters at all locations of interest (e.g., intersections). Therefore, at the vast majority of locations, MnDOT samples axle counts for short durations, called portable traffic recorder (PTR) sites, and obtains continuous counts from a small number of strategically important locations. The continuous-count data is leveraged to convert short-duration axle counts into average-annual-daily- traffic counts. This requires estimation of seasonal adjustment factors (SAFs) and axle correction factors at short- count locations. This project focused on developing a method for estimating SAFs for PTR sites. The continuous- count data was grouped into a small number of groups based on seasonal traffic-volume patterns. Traffic patterns at PTR sites were hypothesized by polling professional opinions and then verified by performing statistical tests. PTRs with matching seasonal patterns inherited SAFs from the corresponding continuous-count locations. Researchers developed a survey tool, based on the analytic hierarchy process, to elicit professional judgments. MnDOT staff tested this tool. The statistical testing approach was based on bootstrapping and computer simulation. It was tested using simulated data. The results of this analysis show that in the majority of cases, three weekly samples, one in each of the three seasons, will suffice to reliably estimate traffic patterns. Data could be collected over several years to fit MnDOT’s available resources. Sites that require many weeks of data (say, more than five) may be candidates for installation of continuous counters.
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    Refining Inductive Loop Signature Technology for Statewide Vehicle Classification Counts
    (Minnesota Department of Transportation, 2021-12) Liao, Chen-Fu
    Transportation agencies in the U.S. use devices such as loop detectors, automatic traffic recorders (ATR), or weigh-in-motion (WIM) sensors to monitor the performance of traffic network for planning, forecasting, and traffic operations. With a limited number of ATR and WIM sensors deployed throughout the state roadways, temporary double tubes are often deployed to get axle-based vehicle classification counts. An inductive loop signature technology previously developed by a Small Business Innovation Research (SBIR) program sponsored by the US Department of Transportation is used to classify vehicles using existing loops. This technology has the potential to save time and money while providing the state, counties or cities more data especially in the metro area where loop detectors have already been installed. This research leveraged the outcomes from previous development to validate the classification accuracy with video data. A loop signature system was initially installed at a traffic station in Jordan, MN, to evaluate its performance. The system was later moved to another location on US-52 near Coates, MN, to validate its classification accuracy with more heavy- vehicle traffic. Individual vehicle records were manually verified and validated with ground-truth video data using both the 13 and 7-bin classification schemes from the Federal Highway Administration (FHWA) and the Highway Performance Monitoring System (HPMS). The combined results from both test sites indicated that the loop signature technology had an overall classification accuracy of 93% and 96% using the FHWA and HPMS schemes, respectively. The classification performance can be further improved by including additional vehicle signatures from the state to the classification library.
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    Understanding the Use of Non-Motorized Transportation Facilities
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-07) Lindsey, Greg; Hoff, Kristopher; Hankey, Steve; Wang, Xize
    Traffic counts and models for describing use of non-motorized facilities such as sidewalks, bike lanes, and trails are generally unavailable. Because transportation officials lack the data and tools needed to estimate use of facilities, their ability to make evidence-based choices among investment alternatives is limited. This report describes and assesses manual and automated methods of counting non-motorized traffic; summarizes counts of cyclists and pedestrians in Minneapolis, Minnesota; develops scaling factors to describe temporal patterns in non-motorized traffic volumes; validates models for estimating traffic using ordinary least squares and negative binomial regressions; and estimates bicycle and pedestrian traffic volumes for every street in Minneapolis. Research shows that automated counters are sufficiently accurate for most purposes. Automated counter error rates vary as a function of type of technology and traffic mode and volume. Across all locations, mean pedestrian traffic (51/hour) exceeded mean bicycle traffic (38/hour) by 35 percent. One-hour counts were highly correlated with 12-hour "daily" counts. Significant correlates of non-motorized traffic vary by mode and include weather (temperature, precipitation), neighborhood socio-demographics (household income, education), built environment characteristics (land use mix), and street (or bicycle facility) type. When controlling for these factors, bicycle traffic, but not pedestrian traffic, increased over time and was higher on streets with bicycle facilities than without (and highest on off-street facilities). These new models can be used to estimate non-motorized traffic where counts are unavailable and to estimate changes associated with infrastructure improvements.