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Browsing by Subject "Bicycle counts"

Now showing 1 - 3 of 3
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    The Minnesota Bicycle and Pedestrian Counting Initiative: Methodologies for Non-motorized Traffic Monitoring
    (Minnesota Department of Transportation, 2013-10) Lindsey, Greg; Hankey, Steve; Wang, Xize; Chen, Junzhou
    The purpose of this project was to develop methodologies for monitoring non-motorized traffic in Minnesota. The project included an inventory of bicycle and pedestrian monitoring programs; development of guidance for manual, field counts; pilot field counts in 43 Minnesota communities; and analyses of automated, continuous-motorized counts from locations in Minneapolis. The analyses showed hourly, daily, and monthly patterns are comparable despite variation in volumes and that adjustment factors can be used to extrapolate short-term counts and estimate annual traffic. The project technical advisory panel made five recommendations: (1) MnDOT should continue and institutionalize coordination of annual statewide manual bicycle and pedestrian counts; (2) MnDOT should improve methods for reporting results of field counts and explore web-based programs for data reporting and analysis; (3) MnDOT should lead efforts to deploy and demonstrate the feasibility of new automated technologies for bicycle and pedestrian counting, focusing on new technologies not presently used in Minnesota; (4) MnDOT should begin integration of non-motorized traffic counts from existing automated, continuous counters in Minneapolis into its new databases for vehicular traffic monitoring data; and (5) MnDOT should work with local governments and explore institutional arrangements for (a) establishing a network of permanent, automated continuous monitoring sites across the state and (b) sharing and deploying new technologies for short-duration monitoring to generate traffic counts that provide a more comprehensive understanding of spatial variation in nonmotorized traffic volumes.
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    Modeling Bicyclist Exposure to Risk and Crash Risk: Some Exploratory Studies
    (Center for Transportation Studies, University of Minnesota, 2018-07) Lindsey, Greg; Wang, Jueyu; Hankey, Steven; Pterka, Michael
    This report presents models for estimating bicyclist exposure to risk and crash risk. Direct demand models for estimating weekday PM peak-period bicyclist exposure to risk are estimated from a database of PM peak-period bicycle counts in Minneapolis and used to estimate exposure for the street network. Bicycle crashes in Minneapolis are described and crash risk is assessed. Probability models to assess crash risk at both intersections and along segments show that both bicyclist exposure and vehicular exposure are associated with the likelihood of a bicycle crash. Estimates of exposure at 184 roadway-trail crossings are used to apply warrants for traffic controls. The results show that warrants for traffic signals and pedestrian hybrid beacons are most likely to be met using weekend peak-hour traffic flows. Most locations that meet warrants already have controls, but site specific safety investigations may be warranted at 9% of all crossings. Count-based models of bicyclist exposure are estimated for Duluth using origin-destination centrality indices as explanatory variables. Although these indices correlate positively and significantly with bicyclist volumes, estimates of exposure do not correlate with bicycle crashes. Together, these analyses illustrate how measures of bicyclist exposure to risk can be used in assessments of safety and crash risk. The approaches can be used in planning-level studies where consistent measures of exposure or risk are needed. These results underscore the need to continue bicycle traffic monitoring and make available estimates of exposure for safety assessments.
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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.