Browsing by Subject "Traffic flow theory"

Now showing 1 - 3 of 3
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    Development and estimation of traffic data-based operational measures for effective winter snow management
    (2014-08) Hong, Seong Ah
    Snow storms degenerate safety and traffic mobility by creating adverse driving conditions including visibility impairments, reducing pavement friction, obstructed road facilities. Improving the effectiveness of the snow management requires an efficient assessment of the operational strategies with the data that can be directly measureable from the field. While there have been various types of operational measures used in the state DOTs in the US, the traffic data-based operational measures that can quantify the effects of snow plowing activities are still lacking. To be sure, the existing approaches developed to date employed the variations of traffic speed or travel time during the snow events and those measures cannot fully reflect the road weather conditions when traffic is congested.Developing the operational measures that can objectively and accurately reflect the time-variant road weather conditions is essential in improving the effectiveness of the snow management strategies. In this research, an automatic process is developed for estimating the traffic data-based operational measures for winter snow management. Those measures include the Road Condition Recovery Time (RCR), the estimate of the `bare-lane regain time', which is the major performance measure of the winter snow maintenance operations at the Minnesota Department of Transportation. The automatic process was then applied to a sample snow section in Twin Cities, MN. The results of the example application showed that the operational measures can reasonably measure the performance of snow plowing operations by reflecting the impacts of the traffic flow resulted by the time-variant road weather conditions.
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    Development and estimation of traffic data-based operational measures for effective winter snow management
    (2014-08) Hong, Seong Ah
    Snow storms degenerate safety and traffic mobility by creating adverse driving conditions including visibility impairments, reducing pavement friction, obstructed road facilities. Improving the effectiveness of the snow management requires an efficient assessment of the operational strategies with the data that can be directly measureable from the field. While there have been various types of operational measures used in the state DOTs in the US, the traffic data-based operational measures that can quantify the effects of snow plowing activities are still lacking. To be sure, the existing approaches developed to date employed the variations of traffic speed or travel time during the snow events and those measures cannot fully reflect the road weather conditions when traffic is congested.Developing the operational measures that can objectively and accurately reflect the time-variant road weather conditions is essential in improving the effectiveness of the snow management strategies. In this research, an automatic process is developed for estimating the traffic data-based operational measures for winter snow management. Those measures include the Road Condition Recovery Time (RCR), the estimate of the `bare-lane regain time', which is the major performance measure of the winter snow maintenance operations at the Minnesota Department of Transportation. The automatic process was then applied to a sample snow section in Twin Cities, MN. The results of the example application showed that the operational measures can reasonably measure the performance of snow plowing operations by reflecting the impacts of the traffic flow resulted by the time-variant road weather conditions.
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    Modeling stochastic human-driver car following behavior in oscillatory traffic conditions
    (Center for Transportation Studies, University of Minnesota, 2021-08) Stern, Raphael; Shang, Mingfeng
    Accurately modeling the realistic and unstable traffic dynamics of human-driven traffic flow is crucial to being able to understand how traffic dynamics evolve, and how new agents such as autonomous vehicles might influence traffic flow stability. This work is motivated by a recent dataset that allows us to calibrate accurate models, specifically in conditions when traffic waves arise. Three microscopic car-following models are calibrated using a microscopic vehicle trajectory dataset that is collected with the intent of capturing oscillatory driving conditions. For each model, five traffic flow metrics are constructed to compare the flow-level characteristics of the simulated traffic with experimental data.