Browsing by Author "Hankey, Steve"

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    Advancing cycling among women: An exploratory study of North American cyclists
    (Journal of Transport and Land Use, 2019) Le, Huyen T. K.; West, Alyson; Quinn, Fionnuala; Hankey, Steve
    Past studies show that women cycle at a lower rate than men due to various factors; few studies examine attitudes and perceptions of women cyclists on a large scale. This study aims to fill that gap by examining the cycling behaviors of women cyclists across multiple cities in North America. We analyzed an online survey of 1,868 women cyclists in the US and Canada, most of whom were confident when cycling. The survey recorded respondents’ cycling skills, attitude, perceptions of safety, surrounding environment, and other factors that may affect the decision to bicycle for transport and recreation. We utilized tree-based machine learning methods (e.g., bagging, random forests, boosting) to select the most common motivations and concerns of these cyclists. Then we used chi-squared and non-parametric tests to examine the differences among cyclists of different skills and those who cycled for utilitarian and non-utilitarian purposes. Tree-based model results indicated that concerns about the lack of bicycle facilities, cycling culture, cycling’s practicality, sustainability, and health were among the most important factors for women to cycle for transport or recreation. We found that very few cyclists cycled by necessity. Most cyclists, regardless of their comfort level, preferred cycling on facilities that were separated from vehicular traffic (e.g., separated bike lanes, trails). Our study suggests opportunities for designing healthy cities for women. Cities may enhance safety to increase cycling rates of women by tailoring policy prescriptions for cyclists of different skill groups who have different concerns. Strategies that were identified as beneficial across groups, such as investing in bicycle facilities and building a cycling culture in communities and at the workplace, could be useful to incorporate in long-range planning efforts.
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    Exposure to particulate air pollution during active travel
    (2014-08) Hankey, Steve
    Increasing active travel is a commonly cited strategy to improve public health and reduce the environmental impact of transportation. Active travel has a number of benefits to both the individual (e.g., physical activity) and to society (e.g., reduced emissions, shifting trips off the motor-vehicle network); however, how cyclists and pedestrians are exposed to hazards during active travel is understudied. One such risk factor is exposure to on-road particulate air pollution. My dissertation explores how people are exposed to particulate air pollution during active travel and how spatial patterns of cycling and walking traffic may impact exposure. I measured real-time particulate air pollution (particle number, black carbon, PM2.5, and particle size) while cycling in Minneapolis, MN. I then use those measurements to explore exposure to particulate air pollution during active travel including: (1) assessing whether measures of particulates are correlated with each other, with motor-vehicle traffic counts, and with traffic mix (i.e., truck, bus, or passenger vehicle), (2) developing land use regression (LUR) models to determine correlates of particulate air pollution and to extrapolate estimates to locations without measurements, and (3) comparing the spatial patterns of modeled particulate air pollution with the spatial patterns of bicycle and pedestrian traffic. I collected 129 hrs of measurements (886 miles) on streets and off-street trails during 42 sampling runs in year-2012. Mobile bicycle-based sampling was completed using a bicycle trailer, modified to safely carry air pollution equipment and to allow for an elevated air intake that samples at heights close to the breathing zone of cyclists and pedestrians. I carried air pollution instrumentation that measures four aspects of particulate air pollution: (1) TSI CPC 3007 (particle number concentration), (2) AethLabs AE-51 micro-aethalometer (black carbon mass concentration), (3) TSI DustTrak 8530 (PM2.5 mass concentration), and (4) TSI NanoScan (particle size distribution). A GPS and temperature and relative humidity sensor were included on the sampling platform to geo-locate and adjust air pollution measurements for meteorological variables. Two video cameras were included to assess the impact of traffic volume and mix on exposure concentrations. In Chapter 3, I explore trends in measured on-road concentrations associated with traffic dynamics (i.e., volume and mix), between-pollutant correlations, and how concentrations vary by street characteristics. I developed a set of underwrite functions to control for day-to-day variability in background concentrations, and I used the background-adjusted dataset for all spatial comparisons of concentrations. (Analyses here that are not focused on spatial patterns do not use this adjustment). I found that black carbon and particle number (two traffic-related pollutants) were the most correlated among the pollutants measured (R2: 0.39 and 0.61, respectively, for 1- and 30-minute averages). Correlations among all pollutant pairs increased with averaging time. Particle size was modestly correlated with particle number (negative correlation) and with black carbon (positive correlation). In regression models that control for meteorological parameters, median particle size on-road is more correlated with background concentration of black carbon and particle number than on-road concentration (ratio of background to on-road model β for black carbon [particle number]: 5.3 [8.3]). Traffic counts (assessed from the video footage) were correlated with particulate concentrations; the presence of a truck in the video frame (from a camera mounted on the handlebars) was correlated with a 30,600 pt/cc [1.6 µg/m3] increase in particle number [black carbon] concentration compared to a 2,300 pt/cc [0.26 µg/m3] increase for buses and a 240 pt/cc [0.02 µg/m3] increase for passenger-vehicles. Concentrations were associated with street functional class and decreased with distance from a major road; presence of a bike facility had little impact on concentrations. In Chapter 4, I describe an approach for developing land use regression (LUR) models from the mobile measurements to estimate concentrations at locations where measurements are not available. I aggregated measurements at various spatial resolutions and tested model performance using a number of different averaging times. Models were developed using 215 candidate independent variables and a stepwise regression approach that selects the variables most correlated with concentrations from those candidate variables. In total 1,224 LUR models were generated; 8 models were chosen to extrapolate concentration estimates to the entire City of Minneapolis. Variables that were most commonly selected in the models either described separation from emission sources (e.g., distance from a major road, area of open space, or length of local [low-traffic] road) or proximity to or density of emissions (e.g., length of major road or freeway, industrial area, or traffic intensity). In general model goodness-of-fit statistics were modest; morning [afternoon] adjusted R2 were 0.50 [0.48] for particle number, 0.28 [0.42] for black carbon, 0.30 [0.49] for PM2.5, and 0.29 [0.20] for particle size. Models generally underestimated the highest concentration locations; analysis of spatial autocorrelation of model residuals suggests this may be owing to measurements at highly congested street segments during rush-hour. Since data were not available on real-time congestion my models do not capture this relationship. In Chapter 5, I present a sample application of the LUR models by comparing spatial patterns of particulate air pollution to those of bicycle and pedestrian traffic. I estimated bicycle and pedestrian traffic volumes for every street segment in Minneapolis using regression models based on peak-hour bicycle and pedestrian traffic counts. I then used the LUR models developed in Chapter 4 to estimate concentrations for the same street segments. I overlaid the estimates of these two factors to assess spatial patterns of population exposure during active travel. Few neighborhoods in Minneapolis were classified as "healthy" (i.e., high rates of active travel and low particulate concentrations). Bicycle and pedestrian traffic is highest on arterial and collector streets where estimated concentrations are also highest; moving one block away from major roads reduces morning [afternoon] exposure concentrations by 21% [12%] for particle number, 15% [20%] for black carbon, and 7% [3%] for PM2.5. Non-motorized traffic increased for areas with greater land use mix while air pollution concentrations were unchanged; non-motorized traffic and particulate concentrations were not well correlated with population density. Finally, the burden of exposure during active travel appears to occur in neighborhoods that are low-income and have the highest proportion of non-whites (i.e., low-income, non-white neighborhoods were associated with high rates of active travel and increased particulate concentrations). People exposed in those locations may be residents or visitors (e.g., in downtown Minneapolis where job density is high). This dissertation presents work to describe the spatial patterns of exposure to particulate air pollution during active travel in Minneapolis. The analyses herein provide evidence that mobile measurements of air pollution concentrations can yield insight into how and where cyclists and pedestrians are exposed. The results presented here may be of interest to planners and policy-makers interested in designing clean, healthy cities.
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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: Implementation Study
    (Minnesota Department of Transportation, 2015-06) Lindsey, Greg; Petesch, Michael; Hankey, Steve
    The 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.
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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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    Non-Motorized Transportation in the City of Minneapolis
    (Hubert H. Humphrey Institute of Public Affairs, 2010-05-21) Borah, Jason; Hankey, Steve; Hoff, Kris; Utecht, Brad; Xu, Zhiyi
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    A social equity analysis of the U.S. public transportation system based on job accessibility
    (Journal of Transport and Land Use, 2018) Yeganeh, Armin Jeddi; Hall, Ralph P.; Pearce, Annie R.; Hankey, Steve
    Access to quality public transportation is critical for employment, especially for low-income and minority populations. This study contributes to previous work on equity analyses of the U.S. public transportation system by including the 45 largest Metropolitan Statistical Areas (MSAs) in a single analysis. Year-2014 Census demographic data were combined with an existing 2014 dataset of transit job accessibility. Then, transit equality and justice indicators were developed and a regression analysis was performed to explore trends in transit job accessibility by race and income. The findings suggest that within individual MSAs, low-income populations and minorities have the highest transit job accessibility. However, the overall transit ridership is low, and in certain MSAs with high transit job accessibility both high and low income populations have high access levels but middle income populations do not. Within individual MSAs, on average, accessibility differences by income are greater than accessibility differences by race. The relative importance of race versus income for injustice increases with MSA size. In upper mid-size and large MSAs, differences by race increase. Also, the differences by race are greater among low-income populations. Accessibility-related equality and justice indicators are only one of many issues that comprise the wider discussion of equity.
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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.