Browsing by Subject "Annual average daily traffic"
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Item Improve Traffic Volume Estimates from MnDOT's Regional Traffic Management Center(Minnesota Department of Transportation, 2020-02) Kwon, Taek M.The Regional Transportation Management Center (RTMC) at the Minnesota Department of Transportation (MnDOT) deploys a large number of traffic detectors in the Twin Cities' freeway network and continuously collects traffic data. While RTMC mainly uses the data for traffic and incident management, the TFA (Traffic Forecasting and Analysis) office uses the same data for monitoring, forecasting, planning, and reporting of transportation applications. RTMC provides current and historical volume data generated from its freeway network, but it does not provide quality information on that data. The objective of this project was to develop a new tool that can quickly explore the quality of detector data. To allow exploration of data quality, 13 detector-health parameters were computed using raw volume and occupancy data and then they were stored in a relational database. The final detector-health system was implemented as a client server-based system, in that a single server served many remote clients through the Internet. This report provides descriptions of the detector-health parameters, principles applied, server implementation, client software, and some analyses and application examples.Item The Minnesota Bicycle and Pedestrian Counting Initiative: Implementation Study(Minnesota Department of Transportation, 2015-06) Lindsey, Greg; Petesch, Michael; Hankey, SteveThe Minnesota Bicycle and Pedestrian Counting Initiative: Implementation Study reports results from the second in a series of three MnDOT projects to foster non-motorized traffic monitoring. The objectives were to install and validate permanent automated sensors, use portable sensors for short duration counts, develop models for extrapolating counts, and integrate continuous counts into MnDOT traffic monitoring databases. Commercially available sensors, including inductive loops, integrated inductive loops and passive infrared, pneumatic tubes, and radio beams, were installed both as permanent monitor sites and used for short-duration counts at a variety of locations in cities, suburbs, and small towns across Minnesota. All sensors tested in the study produced reasonably accurate measures of bicycle and pedestrian traffic. Most sensors undercounted because of their inability to distinguish and count bicyclists or pedestrians passing simultaneously. Accuracy varied with technology, care and configuration of deployment, maintenance, and analytic methods. Bicycle and pedestrian traffic volumes varied greatly across locations, with highest volumes being on multiuse trails in urban areas. FHWA protocols were used to estimate annual average daily traffic and miles traveled on an 80-mile multiuse trail network in Minneapolis. Project findings were incorporated in a new MnDOT guidance document, “DRAFT Bicycle and Pedestrian Data Collection Manual” used in statewide training workshops. A major challenge in implementing bicycle and pedestrian traffic monitoring is data management. Years will be required to institutionalize bicycle and pedestrian traffic successfully.Item Optimizing Automatic Traffic Recorders Network in Minnesota(Minnesota Department of Transportation, 2016-01) Gupta, Diwakar; Tang, XiaoxuAccurate 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.