A Force-Directed Approach for Offline GPS Trajectory Map Matching

We present a novel algorithm to match GPS trajectories onto maps offline (in batch mode) using techniques borrowed from the field of force-directed graph drawing. We consider a simulated physical system where each GPS trajectory is attracted or repelled by the underlying road network via electrical-like forces. We let the system evolve under the action of these physical forces such that individual trajectories are attracted towards candidate roads to obtain a map matching path. Our approach has several advantages compared to traditional, routing-based, algorithms for map matching, including the ability to account for noise and to avoid large detours due to outliers in the data whilst taking into account the underlying topological restrictions (such as one-way roads). Our empirical evaluation using real GPS traces shows that our method produces better map matching results compared to alternative offline map matching algorithms on average, especially for routes in dense, urban areas.Comment: 10 pages, 12 figures, accepted version of article submitted to ACM SIGSPATIAL 2018, Seattle, US

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Sistemas de suporte à condução autónoma adequados a plataforma robótica 4-wheel skid-steer: percepção, movimento e simulação

As competições de robótica móvel desempenham papel preponderante na difusão da ciência e da engenharia ao público em geral. E também um espaço dedicado ao ensaio e comparação de diferentes estratégias e abordagens aos diversos desafios da robótica móvel. Uma das vertentes que tem reunido maior interesse nos promotores deste género de iniciativas e entre o público em geral são as competições de condução autónoma. Tipicamente as Competi¸c˜oes de Condução Autónoma (CCA) tentam reproduzir um ambiente semelhante a uma estrutura rodoviária tradicional, no qual sistemas autónomos deverão dar resposta a um conjunto variado de desafios que vão desde a deteção da faixa de rodagem `a interação com distintos elementos que compõem uma estrutura rodoviária típica, do planeamento trajetórias à localização. O objectivo desta dissertação de mestrado visa documentar o processo de desenho e concepção de uma plataforma robótica móvel do tipo 4-wheel skid-steer para realização de tarefas de condução autónoma em ambiente estruturado numa pista que pretende replicar uma via de circulação automóvel dotada de sinalética básica e alguns obstáculos. Paralelamente, a dissertação pretende também fazer uma análise qualitativa entre o processo de simulação e a sua transposição para uma plataforma robótica física. inferir sobre a diferenças de performance e de comportamento.Mobile robotics competitions play an important role in the diffusion of science and engineering to the general public. It is also a space dedicated to test and compare different strategies and approaches to several challenges of mobile robotics. One of the aspects that has attracted more the interest of promoters for this kind of initiatives and general public is the autonomous driving competitions. Typically, Autonomous Driving Competitions (CCAs) attempt to replicate an environment similar to a traditional road structure, in which autonomous systems should respond to a wide variety of challenges ranging from lane detection to interaction with distinct elements that exist in a typical road structure, from planning trajectories to location. The aim of this master’s thesis is to document the process of designing and endow a 4-wheel skid-steer mobile robotic platform to carry out autonomous driving tasks in a structured environment on a track that intends to replicate a motorized roadway including signs and obstacles. In parallel, the dissertation also intends to make a qualitative analysis between the simulation process and the transposition of the developed algorithm to a physical robotic platform, analysing the differences in performance and behavior

Pedestrian real-time location and routing information delivered to mobile digital architectural guides

The work described in this thesis deals with two particular issues relating to the effective delivery of Architectural information that includes textual, 2D graphics and 3D graphic information to small mobile digital devices on location. These issues were investigated, and a solution was suggested in this thesis as part of an ongoing research project, 'City in the Palm of your Hand', that is being applied in the city of Liverpool, UK. The outcomes have broader implications for other applications of the theories and technologies related to pedestrian guides

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Learning Approach to Delineation of Curvilinear Structures in 2D and 3D Images

Detection of curvilinear structures has long been of interest due to its wide range of applications. Large amounts of imaging data could be readily used in many fields, but it is practically not possible to analyze them manually. Hence, the need for automated delineation approaches. In the recent years Computer Vision witnessed a paradigm shift from mathematical modelling to data-driven methods based on Machine Learning. This led to improvements in performance and robustness of the detection algorithms. Nonetheless, most Machine Learning methods are general-purpose and they do not exploit the specificity of the delineation problem. In this thesis, we present learning methods suited for this task and we apply them to various kinds of microscopic and natural images, proving the general applicability of the presented solutions. First, we introduce a topology loss - a new training loss term, which captures higher-level features of curvilinear networks such as smoothness, connectivity and continuity. This is in contrast to most Deep Learning segmentation methods that do not take into account the geometry of the resulting prediction. In order to compute the new loss term, we extract topology features of prediction and ground-truth using a pre-trained network, whose filters are activated by structures at different scales and orientations. We show that this approach yields better results in terms of conventional segmentation metrics and overall topology of the resulting delineation. Although segmentation of curvilinear structures provides useful information, it is not always sufficient. In many cases, such as neuroscience and cartography, it is crucial to estimate the network connectivity. In order to find the graph representation of the structure depicted in the image, we propose an approach for joint segmentation and connection classification. Apart from pixel probabilities, this approach also returns the likelihood of a proposed path being a part of the reconstructed network. We show that segmentation and path classification are closely related tasks and can benefit from the synergy. The aforementioned methods rely on Machine Learning, which requires significant amounts of annotated ground-truth data to train models. The labelling process often requires expertise, it is costly and tiresome. To alleviate this problem, we introduce an Active Learning method that significantly decreases the time spent on annotating images. It queries the annotator only about the most informative examples, in this case the hypothetical paths belonging to the structure of interest. Contrary to conventional Active Learning methods, our approach exploits local consistency of linear paths to pick the ones that stand out from their neighborhood. Our final contribution is a method suited for both Active Learning and proofreading the result, which often requires more time than the automated delineation itself. It investigates edges of the delineation graph and determines the ones that are especially significant for the global reconstruction by perturbing their weights. Our Active Learning and proofreading strategies are combined with a new efficient formulation of an optimal subgraph computation and reduce the annotation effort by up to 80%

City-scale eco-routing and pavement eco-maintenance scheduling for CO2 mitigation

Responsible for more than one-sixth of the world’s CO2 emissions and growing, the road transportation is an unavoidable component in tackling the global carbon reduction challenge. Within the subdivisions of infrastructure management and traffic management, different carbon mitigation approaches have been discussed to contribute to a greener transportation system. However, given the complex interactions that exist between the road users and the infrastructures, certain carbon mitigation proposals are best evaluated within a compre- hensive environment to ensure the correct consideration of the interactions, as well as the comparability of the results. It is the aim of this thesis to develop such a unified framework to reflect interactions and outcomes of different CO2 mitigation approaches from both the road infrastructure and the traffic mobility sides. This allows combining and comparing the effectiveness of a single or multiple carbon mitigation approaches across different perspectives. Specifically, on the traffic operation side, a mesoscopic traffic model is adopted for simulating drivers’ route choices with varying percentages of travellers to choose the eco-friendly routes. From the infrastructure asset management perspective, a city-scale pavement degradation model is built and utilised in testing pavement maintenance scenarios. San Francisco is chosen as the case study area due to the availability of various traffic mobility and infrastructure condition data. In Chapter 3 of the thesis, the traffic simulation module is developed that implements the efficient macroscopic road link-level speed-flow relationship while retaining detailed origin, destination and departure hour information for each individual trip. The traffic simulation model uses a highly detailed network representation for the study area and has the hourly traffic demand informed by Traffic Network Companies (TNC) data. The model is capable of capturing the spatio-temporal variations in traffic distributions. In addition, in Chapter 4, assumptions are also tested as for the extent that the availability of real-time traffic information affects the travellers’ behaviours and model results. As ageing pavements induce additional carbon emissions, in Chapter 5, a city-scale pavement degradation model is proposed based on 20 years of survey data. After comparing three model forms (non-spatial categorical, non-spatial individual road based and spatial hierarchical models) and two independent predictors (pavement age, cumulative traffic load), the spatial model with age as the predictor is found to give the best overall performance in terms of model fitting and complexity. As a result, it is used later in this thesis for degradation forecasting and maintenance planning. The traffic simulation and the pavement degradation models are joined together in Chapter 6 to test the carbon mitigation scenarios, including the eco-friendly route selection (eco- routing) and eco-friendly pavement maintenance scheduling (eco-maintenance). Interactions between the road users and the pavement management occur when: (1) pavement maintenance site selection is based on both pavement roughness and traffic volume (the eco-maintenance case). (2) The renewed pavement condition, with a smoother surface and reduced emission factor, becomes part of the route selection criteria of the drivers (the eco-routing case). It is found that the outcomes of eco-maintenance are sensitive to a variety of factors, including the budget level, the pavement degradation rate as well as the maintenance quality. The eco-routing approach tends to shift travellers to the local network but is effective in reducing the overall emissions. However, the reinforcing interactions between these two strategies are the most noticeable only when both eco-routing and eco-maintenance strategies are enforced to an extreme. Through the simulation of city-scale traffic and infrastructure dynamics, it is able to quantitatively compare carbon mitigation scenarios and understand how an action from one specific perspective ripples through the transportation system. Also, sensitivity tests suggest limiting conditions for each approach to make a difference. This research highlights the need to include combined simulations in certain cases and such results are expected to give confidence to decision makers as for the potential induced demand or other secondary effects whose influences extend beyond a single sub-system.Cambridge International Students Scholarship The Alan Turing Institute Enrichment Studentshi