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)

Shared mobility is considered a more sustainable alternative to private modes. Nonetheless, its sudden and sometimes “out of control” emergence poses issues that need to be addressed. Lack of regulations and public space mismanagement cause sidewalks and city roads to be overcrowded with shared vehicles (especially in the case of micromobility). This causes nuisance and safety concerns and hinders the societal benefits shared mobility may provide. Shared mobility hubs have the potential to address these issues. The research was carried out within the context of the SmartHubs project, an EIT Urban Mobility project initiated in 2021 by a diverse consortium of 7 cities, companies, and universities to develop and validate effective and economically viable mobility hub solutions. This degree project aims to improve the Decision-Support-Tool (DST) developed by SmartHubs to identify locations of shared-mobility hubs having high potential in driving sustainable travel usage. To achieve that, the thesis proposes a methodology for determining smart hub locations and their corresponding utilities based on the combination of GIS cluster analysis of free-floating shared mobility parking patterns and a stated-preference study. The potential hub locations were determined from the cluster analysis of free-floating trip characteristics. Using the stated preference survey data, the thesis develops a model to estimate the probability of parking at the hub as a function of explanatory variables, including walking distance, reward policies and the parking situation. The model testing results showed that the proposed methodology can well predict the hub (usage) demand and improve the current DST originally developed in the SmartHubs project.