Non-motorized (i.e., bicycle and pedestrian) traffic patterns are an understudied but important part of transportation systems. A key need for transportation planners is traffic monitoring programs similar to motorized traffic. Count campaigns can help estimate mode choice, measure infrastructure performance, track changes in volume, prioritize projects, analyze travel patterns (e.g., annual average daily traffic [AADT] and miles traveled [MT]), and conduct safety analysis (e.g., crash, injury and collision). However, unlike for motorized traffic, non-motorized traffic has not been comprehensively monitored in communities throughout the U.S. and is generally performed in an ad hoc fashion. My thesis explores how to (1) best count bicycles and pedestrians on the entire transportation network, rather than only focus on off-street trail systems or specific transportation corridors and (2) estimate AADT of bicycles and pedestrians in a small college town (i.e., Blacksburg, VA). I used a previously developed count campaign in Blacksburg, VA to collect bicycle and pedestrian counts using existing monitoring technologies (e.g., pneumatic tubes, passive infrared, and RadioBeam). I then summarized those counts to (1) identify seasonal, daily, and hourly patterns of non-motorized traffic and (2) develop scaling factors (analogous to those used in motor vehicle count programs) derived from the continuous reference sites to estimate long-term averages (i.e., AADT) for short-duration count sites. I collected ~40,000 hours of bicycle and pedestrian counts from early September 2014 to January 2016. The count campaign included 4 continuous reference sites (~ full year-2015 counts) and 97 short-duration sites (≥ 1-week counts) that covered different road and trail types (i.e., major road, local road, and off-street trails). I used 25 commercially available counters (i.e., 12 MetroCount MC 5600 Vehicle Classifier System [pneumatic tube counters], 10 Eco-counter 'Pyro' [passive infrared counters], and 3 Chambers RadioBeam Bicycle-People Counter [radiobeam counters]) to conduct the traffic count campaign. Three MetroCount, 4 Eco-counter, and 1 RadioBeam counter were installed at the 4 continuous reference sites; the remaining counters were rotated on a weekly basis at the short-duration count sites. I validated automated counts with field-based manual counts for all counters (210 total hours of validation counts). The validation counts were used to adjust automated counts due to systematic counter errors (e.g., occlusion) by developing correction equations for each type of counter. All automated counters were well correlated with the manual counts (MetroCount R2 [absolute error]: 0.90 [38%]; Eco-counter: 0.97 [24%]; RadioBeam bicycle: 0.92 [19%], RadioBeam pedestrian: 0.92 [22%]). I compared three bicycle-based classification schemes provided by MetroCount (i.e., ARX Cycle, BOCO and Bicycle 15). Based on the validation counts the BOCO (Boulder County, CO) classification scheme (hourly counts) had similar R2 using a polynomial correction equation (0.898) as compared to ARX Cycle (0.895) and Bicycle 15 (0.897). Using a linear fit, the slope was smallest for BOCO (1.26) as compared to ARX Cycle (1.29) and Bicycle 15 (1.31). Therefore, I used the BOCO classification scheme to adjust the automated hourly bicycle counts from MetroCount. To ensure a valid count dataset was used for further analysis, I conducted quality assurance and quality control (QA/QC) protocols to the raw dataset. Overall, the continuous reference sites demonstrated good temporal coverage during the period the counters were deployed (bicycles: 96%; pedestrians: 87%) and for the calendar year-2015 (bicycles: 75%; pedestrians: 87%). For short-duration sites, 98% and 94% of sites had at least 7 days of monitoring for bicycles and pedestrians, respectively; no sites experienced 5 days or less of counts. I analyzed the traffic patterns and estimated AADT for all monitoring sites. I calculated average daily traffic, mode share, weekend to weekday ratio and hourly traffic curves to assess monthly, daily, and hourly patterns of bicycle and pedestrian traffic at the continuous reference sites. I then classified short-duration count sites into factor groups (i.e., commute [28%], recreation [11%], and mixed [61%]). These factor groups are normally used for corresponding continuous reference sites with the same patterns to apply scaling factors. However, due to limitations of the number (n=4) of continuous reference sites, the factor groups were only used as supplemental information in this analysis. To impute missing days at the 4 continuous reference sites to build a full year-2015 (i.e., 365 days) dataset, I built 8 site-specific negative binomial regression models (4 for bicycles and 4 for pedestrians) using temporal and weather variables (i.e., daily max temperature, daily temperature variation compared to the normal 30-year averages [1980-2010], precipitation, wind speed, weekend, and university in session). In general, the goodness-of-fit for the models was better for the bicycle traffic models (validation R2 = ~0.70) as compared to the pedestrian traffic models (validation R2 = ~0.30). The selected variables were correlated with bicycle and pedestrian traffic and cyclists are more sensitive to weather conditions than pedestrians. Adding model-generated estimates of missing days into the existing observed reference site counts allowed for calculating AADT for each continuous reference site (bicycles volumes ranged from 21 to 179; pedestrian volumes ranged from 98 to 4,232). Since a full year-2015 dataset was not available at the short-duration sites, I developed day-of-year scaling factors from the 4 continuous reference sites to apply to the short-duration counts. The scaling factors were used to estimate site-specific AADT for each day of the short-duration count sites (~7 days of counts per location). I explored the spatial relationships among bicycle and pedestrian AADT, road and trail types, and bike facility (i.e., bike lane). The results indicated that bicycle AADT is significantly higher (p < 0.01) on roads with a bike lane (mean: 72) as compared to roads without (mean: 30); bicycle AADT is significantly higher (p < 0.01) on off-street trails (mean: 72) as compared to major roads (mean: 33). Pedestrian AADT is significantly higher (p < 0.01) on local roads (mean: 693) as compared to off-street trails (mean: 111); this finding is likely owing to the fact that most roads on the Virginia Tech campus are classified as local roads. In Chapter 5, I conclude with (1) recommendations for implementation (e.g., counter installation and data analysis), (2) key findings of bicycle and pedestrian traffic analysis in Blacksburg and (3) strengths, limitations, and directions for future research. This research has the potential to influence urban planning; for example, offering guidance on developing routine non-motorized traffic monitoring, estimating bicycle and pedestrian AADT, prioritizing projects and measuring performance. However, this work could be expanded in several ways; for example, deploying more continuous reference sites, exploring ways to monitor or estimate pedestrians where no sidewalks exist and incorporating other spatial variables (e.g., land use variables) to study pedestrian volumes in future research. The overarching goal of my research is to yield guidance for jurisdictions that seek to implement systematic bicycle and pedestrian monitoring campaigns and to help decision making to encourage healthy, safe, and harmonious communities. / Master of Urban and Regional Planning

Master of Landscape Architecture / Department of Landscape Architecture/Regional and Community Planning / Melanie F. Klein / In a nation of speedy and convenient technologies, the default pace of life has become “fast.” For many American cities, the primary mode of transportation is the private automobile. Daily life is conveniently seen through the car window: we drive to, drive-thru, and drive home. Auto-dependent growth patterns have evolved into sprawling networks of streets and low density, single land-use development. With few pedestrian amenities or destinations, long travel distances, and dominating automobile infrastructure, this development pattern decreases pedestrian and bicycle circulation as a viable and enjoyable mode of transportation. Transportation growth centered around the private automobile compromises compact development, physical activity, safety of pedestrians and bicyclists, interaction with nature, social exchange, and social equity in street corridors. Automobile circulation dominates the transportation system of Manhattan, Kansas. The result is low-density development, sprawling into the surrounding tall-grass prairie and flint hills of the region. Despite several City documents stating goals for multi-modal transportation and accommodation of all users, the existing built environment remains heavily dominated by automobile circulation. The current transportation system inhibits safe and enjoyable pedestrian and bicyclist transit. Inspired by Allan Jacobs’s Greet Streets vision and structured around the Institute of Transportation Engineers’ Context Sensitive Solutions, street networks can be public places for community: “people acting and interacting to achieve in concert what they might not achieve alone” (Jacobs 1993). Movement corridors should be public spaces that encourage physical activity and time to experience a healthier body, mind, and spirit. With priority for bicycle and pedestrian circulation, movement corridors support a more sustainable development pattern and foster meaningful time in transit through more natural speeds of engagement and active presence. Great streets for all users, and a means to integrate improvements in the planning and design of movement corridors will activate progressive growth. The action framework presented here emphasizes important elements concerning the vision for Manhattan’s movement corridors, shows how district development reinforces attributes for walkable communities, and demonstrates guidelines for integrating improvements in Manhattan’s transportation planning and design.