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Browsing by Author "Wang, Xize"

Now showing 1 - 4 of 4
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    Feasibility of Using GPS to Track Bicycle Lane Positioning
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-03) Lindsey, Greg; Hankey, Steve; Wang, Xize; Chen, Junzhou; Gorjestani, Alec
    Researchers have shown that GPS units in smartphones can be used to identify routes taken by cyclists, including whether cyclists deviate from shortest paths to use bike lanes and other facilities. Researchers previously have not reported whether GPS tracking can be used to monitor whether and how bicyclists actually use lanes on streets, where these lanes have been provided, or other types of facilities. The objective of this research was to determine whether smartphone GPS units or enhanced GPS units could be used to track and map the location of cyclists on streets. The research team modified an open-source smartphone application (CycleTracks) to integrate with a higher-quality external GPS unit. Cyclists then mounted the smartphone with route-tracking applications to bicycles and repeatedly rode four different routes. The routes for the field tests were chosen because each included a striped lane for bicycle traffic and because the routes bisected a variety of built urban environments, ranging from an open location on a bridge over the Mississippi River to a narrow urban street lined by tall, multi-story office buildings. The field tests demonstrated that neither the smartphone GPS units nor the higher-quality external GPS receiver generate data accurate enough to monitor bicyclists’ use of bike lanes or other facilities. This lack of accuracy means that researchers interested in obtaining data about the propensity of cyclists to ride in lanes, when available, must rely on other technologies to obtain data for analyses.
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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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    Sharing to Grow: Economic Activity Associated with Nice Ride Bike Share Stations
    (Hubert H. Humphrey School of Public Affairs, 2012-08-31) Schoner, Jessica; Harrison, R. Andrew; Wang, Xize
    This study examines local economic activity associated with bike sharing programs through a mixed methods investigation of the Nice Ride Minnesota bike share system. The literature on bike share systems is rapidly growing, but little information about the ways in which ridership is both influenced by the presence of businesses and influences those businesses is available. This research provides new information about economic aspects of bike share operations by (1) measuring the marginal effects of the presence of different types of businesses and job accessibility on station activity while controlling for other variables; (2) reporting the perceptions of business owners and managers about the effects of a nearby Nice Ride station on these businesses; and (3) using survey results to describe Nice Ride users’ trip making and expenditure patterns. We observed a statistically significant relationship between station trip activity and the number of food-related businesses and job accessibility within a bike share station area. Business owners and managers corroborated these findings by revealing general positive attitudes toward Nice Ride users as customers, although interviewees were ambivalent when asked if they would trade parking or sidewalk cafe space for a Nice Ride station. The user survey revealed that respondents use bike sharing to go to cafes, restaurants, grocery stores, concerts, bars, and the like, and they spend modest amounts of money on these trips. The availability of Nice Ride stations mainly supports mode shifts (e.g., people who choose to bike rather than drive or walk) but it also may induce some new trips. The principal economic effect may be the reallocation of user expenditures to businesses that are more accessible to more people because of the nearby stations.
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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.