3D point cloud processing has been a critical task due to the increasing demand of a variety of applications such as urban planning and management, as-built mapping of industrial sites, infrastructure monitoring, and road safety inspection. Point clouds are mainly acquired from two sources, laser scanning and optical imaging systems. However, the original point clouds usually do not provide explicit semantic information, and the collected data needs to undergo a sequence of processing steps to derive and extract the required information. Moreover, according to application requirements, the outcomes from the point cloud processing could be different. This dissertation presents two tiers of data processing. The first tier proposes an adaptive data processing framework to deal with multi-source and multi-platform point clouds. The second tier introduces two point clouds processing strategies targeting applications mainly from urban environments and transportation corridors.<div><br></div><div>For the first tier of data processing, the internal characteristics (e.g., noise level and local point density) of data should be considered first since point clouds might come from a variety of sources/platforms. The acquired point clouds may have a large number of points. Data processing (e.g., segmentation) of such large datasets is time-consuming. Hence, to attain high computational efficiency, this dissertation presents a down-sampling approach while considering the internal characteristics of data and maintaining the nature of the local surface. Moreover, point cloud segmentation is one of the essential steps in the initial data processing chain to derive the semantic information and model point clouds. Therefore, a multi-class simultaneous segmentation procedure is proposed to partition point cloud into planar, linear/cylindrical, and rough features. Since segmentation outcomes could suffer from some artifacts, a series of quality control procedures are introduced to evaluate and improve the quality of the results.<br></div><div><br></div><div>For the second tier of data processing, this dissertation focuses on two applications for high human activity areas, urban environments and transportation corridors. For urban environments, a new framework is introduced to generate digital building models with accurate right-angle, multi-orientation, and curved boundary from building hypotheses which are derived from the proposed segmentation approach. For transportation corridors, this dissertation presents an approach to derive accurate lane width estimates using point clouds acquired from a calibrated mobile mapping system. In summary, this dissertation provides two tiers of data processing. The first tier of data processing, adaptive down-sampling and segmentation, can be utilized for all kinds of point clouds. The second tier of data processing aims at digital building model generation and lane width estimation applications.<br></div>

Multi-combination vehicles (MCVs) in urban areas impact on productivity, safety, infrastructure, congestion and the environment. However, psychological effects of MCVs on other drivers may also influence the positioning of vehicles and congestion. A literature review revealed little information on the psychological effects of heavy vehicles on other road users. This research can be used to quantify some psychological impacts of MCVs. A testing program was undertaken on the Gateway Motorway to observe passenger car behaviour around MCVs in a lateral and longitudinal sense. Video footage was collected on a four lane divided urban motorway section which was level, straight and away from any off/on ramps. It experiences high traffic volumes with a one-way AADT of approximately 33,500. The route is currently designated for B-doubles, which is the most common MCV in urban areas. In a lateral sense, the research showed that passenger car behaviour changes around heavy vehicles (prime mover semi-trailer combination and B-doubles); however, there is no statistical difference in passenger car behaviour around semi-trailers and B-doubles. Longitudinally it was found that, even though passenger cars shy away from B-doubles more than semi-trailers, B-doubles are still more efficient in a spatial sense since they carry more freight. The outcomes of this research indicate that there is no further psychological impact on passenger cars, when travelling around B-doubles compared with semi-trailers. Where the results identified longitudinal behaviour changes, it was still concluded that B-doubles were more efficient at transporting freight when the passenger car equivalent (PCE) per tonne of freight was considered. Tracking ability testing was undertaken in a rural area to determine the lateral spatial requirements of three different MCVs. The rural testing was considered appropriate since parts of the urban network have similar characteristics to rural networks. A model was developed as a part of this project to process the data collected by Haldane (2002), but results could not be relied upon due to poor quality data.