Cycling, and micromobility tools like bike share, have increasingly been recognized for their health, economic and environmental benefits, and municipalities have recently made encouraging the use of these modes of urban transportation both a policy and a financial priority. Many studies, using varying methods, have identified and confirmed an association between an increased presence and connectivity of cycling infrastructure (bike lanes, cycle tracks, etc.) and cycling or bike share ridership. Determining a more explicit causal link between infrastructure and ridership, however, often proves challenging to researchers, due to data limitations and a variety of simultaneous, exogenous, factors that abound within complex urban transportation systems. Given the financial impacts of capital investment in infrastructure, more closely establishing this causal link, and identifying infrastructure’s ability to generate cycling and bike share traffic, is of growing importance to municipal governments and taxpayers. Using Hamilton Bike Share (HBS) trip logs and GPS trajectories occurring between January, 2019 and August, 2022 (n = 741,369 and 609,746, respectively), this thesis constructs individual shapefiles of each HBS trip for GIS analysis through Dalumpines and Scott’s (2011) GIS-Based Map-Matching Algorithm. It investigates the impact of ten separated cycling infrastructure projects in Hamilton, constructed between 2019 and 2022, on HBS ridership along the respective intervention segments. The thesis also holistically analyzes the spatial and ridership impacts of one infrastructure intervention, the Victoria Avenue cycle track, on the distribution of riders using the segment of interest, a more precise classification of post-intervention trip natures (‘induced’ or ‘diverted’) using a novel categorization process, and maps the impact of the iv segment on trip diversion to use the cycle track. Results indicate that five of the ten interventions have had significant, positive, impacts on monthly HBS ridership along their respective segments, with others having nearly statistically significant results as well. Moreover, the Victoria Avenue cycle track lessened the cost of distance associated with using Victoria Avenue, and 46.9% of trips along the cycle track post-intervention, were determined to be ‘induced’ trips. Finally, of the streets in the Victoria Avenue cycle track’s neighborhood, the cycle track segments were the only segments to experience ridership increases post-intervention, which indicates a significant level of trip diversion and funneling of trips to use the cycle track. These results enhance findings from the literature and more concretely quantify the direct impacts of infrastructure investments. Investments in infrastructure appear to make a significant difference in increasing ridership and serve to benefit more than just existing riders. This thesis can have an important impact on municipal active transportation planning, policy, and financing, through its results and by providing a methodological foundation for future research into infrastructure’s impacts on a variety of users. / Thesis / Master of Science (MSc)

This thesis examines factors that influence the daily number of trip departures and arrivals at over 100 hubs comprising Hamilton, Ontario’s (Canada) bike share program – SoBi (Social Bicycles) Hamilton. SoBi operates all year, and during its first year of operation (April 1, 2015 to March 31, 2016), over 200,000 trips were generated on SoBi bikes. The study utilizes data from SoBi Hamilton, the 2011 Canadian Census, the 2011 Transportation Tomorrow Survey, Environment Canada, and Hamilton’s Open Source Data initiative. From these master files, daily trips, meteorological data, temporal variables, socio-demographic and built environment attributes were obtained to generate a comprehensive suite of explanatory variables to explain the daily trips at each hub. A multilevel regression approach was used to understand the associations between bike share usage at each hub and each suite of explanatory variables at two temporal scales: total daily trips at hubs and total daily trips across four time periods of the day. Findings demonstrate that weather and temporal attributes play a significant role in trip departures and arrivals. In addition, hub attributes vary in significance throughout different times of the day for trip departures and arrivals. Overall, the methodology and findings allow us to identify factors that increase SoBi usage, which can also benefit city planners and engineers who are implementing a bike share system with the goal of maximizing bike share activity in urban centers. / Thesis / Master of Science (MSc)

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As cities worldwide plan for increasing urbanization levels, new challenges in mobility will arise. Any approach taken to address these new issues will need to consider how to move more people with declining resources, thus the need for a sustainable solution arises. This thesis examines the growing trend of cities creating public bicycle systems as a means to add sustainability to a transportation system and identifies what are the criteria and indicators of a sustainable public bicycle. The criteria and indicators are used to examine data collection techniques of three Public Bicycle Systems in the United States: Capital Bikeshare in Washington, D.C., Nice Ride in Minneapolis, Minnesota and Denver B-Cycle in Denver, Colorado.

Low-income groups have greater potential to gain from incorporating health promotion into daily living using bike-share to increase physical activity and expand transport options. The potential is unmet because of socioeconomics and access. Disproportionate uptake of bike-share by higher income individuals widens the gaps in health equity and transportation equity as bike-share use over-represents males with more resources, less need, and lower health risk. The Prescribe a Bike (RxBike) program, a key focus of this study, is a partnership between primary care providers (PCPs) at an urban safety net hospital and the city’s existing income-based, subsidized bike-share membership. Three studies using quantitative and qualitative methods were performed to: examine utilization of bike-share by Boston residents among subsidized and non-subsidized members; examine perceived attributes of the RxBike program by Boston Medical Center (BMC) PCPs; and evaluate BMC patient referrals. The overarching conceptual model uses elements of theories from health services and organizational behavior, in a public health framework. Analysis of Boston resident utilization at the trip-level (2012-2015) demonstrated overall ridership was increasingly by males and residents of more advantaged neighborhoods. Subsidized members had significantly higher likelihood of living in neighborhoods with socioeconomic and health disadvantage, and less gender disparity when compared to non-subsidized members. The impact was minimal because subsidized members made only 7.17% of trips. The survey of PCPs revealed mismatch between highly favorable opinion of RxBike appropriateness and lower intent to refer. Female gender and not being an urban biker predicted lower likelihood of intent to refer. Examination of open-ended survey comments mirrored quantitative data and expanded on the range of provider biking safety concerns in Boston. From 2013-2015, 27 BMC providers made only 72 referrals to RxBike. Patients referred had high cardiovascular health risk, resided in neighborhoods with extremely high levels of disadvantage, and in neighborhoods without meaningful access to bike-share kiosks. Overall, the subsidized membership extends reach of bike-share to residents of neighborhoods with more health and socioeconomic risk than the rest of the city; RxBike has strong potential to impact this vulnerable population. The most critical matters for program success are safety and neighborhood access.

Level of Traffic Stress (LTS) is a four-level system that classifies the stress experienced by cyclists on road segments and at intersections. While LTS has been used in past studies to assess cycling connectivity, accessibility, and safety, very little is known concerning its influence on cycling preferences. This study investigates this topic using a dataset containing 323,163 unique GPS trajectories of Hamilton Bike Share (HBS) users collected over a 12-month period (January 1st to December 31st, 2019). A GIS-based map-matching algorithm is used to generate users’ routes from these trajectories along with attributes such as route length, number of intersections, and number of turns. Unique routes and their use frequencies are then extracted from all routes. The most popular routes between bike share hub (station) pairs are then identified as dominant routes while shortest distance routes are derived by minimizing distance traveled. Weighted level of traffic stress (WLTS), a novel measure of impedance (travel cost) developed for this study, is used to derive the least stressful routes between hub pairs. The three types of routes are compared statistically. The comparison finds that HBS users tend to choose longer routes with bicycle infrastructure in an effort to reduce their traffic stress. However, they do not choose to minimize traffic stress in its entirety by choosing the lowest WLTS routes. In other words, dominant routes are not the least stressful routes in a bike share system. Likewise, minimizing distance is not the sole consideration of HBS users. The findings suggest that other factors also influence route choice. This study not only enhances our understanding of cyclist route preferences with respect to LTS, it also presents a novel measure of impedance – WLTS – that could be used when planning new cycling infrastructure or as an alternative means to route cyclists between origins and destinations. / Thesis / Master of Science (MSc)

This thesis examines the existence of a dominant route between a hub pair and factors that influence bike share cyclists route choices. This research collects 132,396 hub to-hub global positioning system (GPS) trajectories over a 12-month period between April 1, 2015 and March 31, 2016 from 750 bicycles provided by a bike share program (BSP) called SoBi (Social Bicycles) Hamilton. Then, a GIS-based map-matching toolkit is used to convert GPS points to map-matched trips and generate a series of route attributes. In order to create choice sets, unique routes between the same hub pair are extracted from all corresponding repeated trips using a link signature tool. The results from t statistics and Path-size logit models indicate that bike share cyclists are willing to detour for some positive features, such as bicycle facilities and low traffic volumes, but they also try to avoid too circuitous routes, turns, and steep slopes over 4% though detouring may come with a slight increase in turns. This research not only helps us understand BSP cyclist route preferences but also presents a GIS-based approach to determine potential road segments for additional bike facilities on the basis of such preferences. / Thesis / Master of Science (MSc)

Modern bike share systems (BSSs) allow users to rent from a fleet of bicycles at hubs across the designated service area. With clear evidence of cycling being a health-positive form of active transport, furthering our understanding of the underlying processes that affect BSS ridership is essential to continue further adoption. Using 286,587 global positioning system (GPS) trajectories over a 12-month period between January 1st, 2018 and December 31st, 2018 from a BSS called SoBi (Social Bicycles) Hamilton, the number of trips on every traveled link in the service area are predicted. A GIS-based map-matching toolkit is used to generate cyclists’ routes along the cycling network of Hamilton, Ontario to determine the number of observed unique trips on every road segment (link) in the study area. To predict trips, several variables were created at the individual link level including accessibility measures, distances to important locations in the city, proximity to active travel infrastructure (SoBi hubs, bus stops), and bike infrastructure. Linear regression models were used to estimate trips. Eigenvector spatial filtering (ESF) was used to explicitly model spatial autocorrelation. The results suggest the largest positive predictors of cycling traffic in terms of cycling infrastructure are those that are physically separated from the automobile network (e.g., designated bike lanes). Additionally, hub-trip distance accessibility, a novel measure, was found to be the most significant variable in predicting trips. A demonstration of how the model can be used for strategic planning of road network upgrades is also presented. / Thesis / Master of Science (MSc)

Public bike share systems have been recognized as an effective way to promote active and sustainable public transportation. With the health benefits of bike share becoming better understood, North American cities have continued to invest in cycling infrastructure and impose new policies to not only encourage the usage of bike share systems but also expand their operations to new cities. The city of Hamilton, Ontario, implemented its own bike share system in March 2015. Using the system’s global positioning system (GPS) data for annually aggregated trip departures, arrivals, and totals in 2017, this research explores various environment factors that have an impact on users’ bike share usage at hub level. Nine predictive linear regression models were developed for three different scenarios depending on the type of hubs and members for trip departures, arrivals, and totals. In terms of variance explained across the core service area, the models suggested the main factors that attract users were distance to McMaster University and the number of racks available at hubs. Furthermore, the working population and distance to the Central Business District and the closest bike lane in the immediate vicinity (200 m buffer) also played important roles as contributing factors. Based on the primary predictors, this research takes one step further and estimates potential trips at candidate sites to inform future expansion of public bike share system. The candidate locations were created on appropriate land uses by applying a continuous surface of regularly shaped cells, a hexagonal tessellation, on the area of interest. The estimated potential usage at candidate sites demonstrated that the east part of the city should be targeted for future bike share expansion. / Dissertation / Master of Science (MSc)