Applying life cycle assessment to analyze the environmental sustainability of public transit modes for the City of Toronto

One challenge related to transit planning is selecting the appropriate mode: bus, light rail transit (LRT), regional express rail (RER), or subway. This project uses data from life cycle assessment to develop a tool to measure energy requirements for different modes of transit, on a per passenger-kilometer basis. For each of the four transit modes listed, a range of energy requirements associated with different vehicle models and manufacturers was developed. The tool demonstrated that there are distinct ranges where specific transit modes are the best choice. Diesel buses are the clear best choice from 7-51 passengers, LRTs make the most sense from 201-427 passengers, and subways are the best choice above 918 passengers. There are a number of other passenger loading ranges where more than one transit mode makes sense; in particular, LRT and RER represent very energy-efficient options for ridership ranging from 200 to 900 passengers. The tool developed in the thesis was used to analyze the Bloor-Danforth subway line in Toronto using estimated ridership for weekday morning peak hours. It was found that ridership across the line is for the most part actually insufficient to justify subways over LRTs or RER. This suggests that extensions to the existing Bloor-Danforth line should consider LRT options, which could service the passenger loads at the ends of the line with far greater energy efficiency. It was also clear that additional destinations along the entire transit line are necessary to increase the per passenger-kilometer energy efficiency, as the current pattern of commuting to downtown leaves much of the system underutilized. It is hoped that the tool developed in this thesis can be used as an additional resource in the transit mode decision-making process for many developing transportation systems, including the transit systems across the GTHA.

Axis Mapping: The Estimation of Surface Orientations and its Applications in Vehicle Localization and Structural Geology

The map representation of an environment should be selected based on its intended application. For example, a geometrically accurate map describing the Euclidean space of an environment is not necessarily the best choice if only a small subset its features are required. One possible subset is the orientations of the flat surfaces in the environment, represented by a special parameterization of normal vectors called axes. Devoid of positional information, the entries of an axis map form a non-injective relationship with the flat surfaces in the environment, which results in physically distinct flat surfaces being represented by a single axis. This drastically reduces the complexity of the map, but retains important information about the environment that can be used in meaningful applications in both two and three dimensions. This thesis presents axis mapping, which is an algorithm that accurately and automatically estimates an axis map of an environment based on sensor measurements collected by a mobile platform. Furthermore, two major applications of axis maps are developed and implemented. First, the LiDAR compass is a heading estimation algorithm that compares measurements of axes with an axis map of the environment. Pairing the LiDAR compass with simple translation measurements forms the basis for an accurate two-dimensional localization algorithm. It is shown that this algorithm eliminates the growth of heading error in both indoor and outdoor environments, resulting in accurate localization over long distances. Second, in the context of geotechnical engineering, a three-dimensional axis map is called a stereonet, which is used as a tool to examine the strength and stability of a rock face. Axis mapping provides a novel approach to create accurate stereonets safely, rapidly, and inexpensively compared to established methods. The non-injective property of axis maps is leveraged to probabilistically describe the relationships between non-sequential measurements of the rock face. The automatic estimation of stereonets was tested in three separate outdoor environments. It is shown that axis mapping can accurately estimate stereonets while improving safety, requiring significantly less time and effort, and lowering costs compared to traditional and current state-of-the-art approaches.