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Browsing by Subject "Traffic volume"

Now showing 1 - 3 of 3
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    Development of a Travel-Time Reliability Measurement System
    (Minnesota Department of Transportation, 2018-09) Kwon, Eil; Park, Chongmyung
    This study has developed a computerized Travel-Time Reliability Measurement System (TTRMS), which can automate the time-consuming process of gathering and managing data from multiple sources and calculating various types of reliability measures under user-specified conditions for given corridors. The TTRMS adopts a server and client structure, where the main database and computational engines reside in the server, while the user- clients are designed for entering the data and generating the output files. In particular, most of the external data, such as traffic and weather datasets, can be remotely downloaded following predefined time schedules. Further, the travel-time calculation process developed in this study can explicitly reflect various lane-configurations at work zones for correctly calculating travel times of the routes with work zones. The map-based user interfaces provide users with a flexible environment, where the route selection and specification of operating conditions for reliability estimation can be efficiently performed. The integrated TTRMS was tested in the Twin Cities’ metro freeway network by estimating the reliability measures of selected corridors with real data for a two-year period, 2012-13. The test results indicate that the TTRMS can substantially reduce the time and effort in estimating various types of reliability measures under different operating conditions for predefined corridors.
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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.
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    Vehicle Probe Based Real-Time Traffic Monitoring on Urban Roadway Networks
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-10) Feng, Yiheng; Hourdos, John; Davis, Gary; Collins, Michael
    Travel time is a crucial variable both in traffic demand modeling and for measuring network performance. The objectives of this study focused on developing a methodology to characterize arterial travel time patterns by travel time distributions, proposing methods for estimating such distributions from static information and refining them with the use of historical GPS probe information, and given such time and location-based distribution, using realtime GPS probe information to produce accurate path travel times as well as monitor arterial traffic conditions. This project set the foundations for a realistic use of GPS probe travel time information and presented the proposed methodologies through two comprehensive case studies. The first study used the Next Generation SIMulation (NGSIM) Peachtree Street dataset, and the second utilized both real GPS and simulation data of Washington Avenue, in Minneapolis, MN.