A computer vision system for detecting and analysing critical events in cities

Whether for commuting or leisure, cycling is a growing transport mode in many cities worldwide. However, it is still perceived as a dangerous activity. Although serious incidents related to cycling leading to major injuries are rare, the fear of getting hit or falling hinders the expansion of cycling as a major transport mode. Indeed, it has been shown that focusing on serious injuries only touches the tip of the iceberg. Near miss data can provide much more information about potential problems and how to avoid risky situations that may lead to serious incidents. Unfortunately, there is a gap in the knowledge in identifying and analysing near misses. This hinders drawing statistically significant conclusions to provide measures for the built-environment that ensure a safer environment for people on bikes. In this research, we develop a method to detect and analyse near misses and their risk factors using artificial intelligence. This is accomplished by analysing video streams linked to near miss incidents within a novel framework relying on deep learning and computer vision. This framework automatically detects near misses and extracts their risk factors from video streams before analysing their statistical significance. It also provides practical solutions implemented in a camera with embedded AI (URBAN-i Box) and a cloud-based service (URBAN-i Cloud) to tackle the stated issue in the real-world settings for use by researchers, policy-makers, or citizens. The research aims to provide human-centred evidence that may enable policy-makers and planners to provide a safer built environment for cycling in London, or elsewhere. More broadly, this research aims to contribute to the scientific literature with the theoretical and empirical foundations of a computer vision system that can be utilised for detecting and analysing other critical events in a complex environment. Such a system can be applied to a wide range of events, such as traffic incidents, crime or overcrowding

Photovoltaic potential in building façades

Tese de doutoramento, Sistemas Sustentáveis de Energia, Universidade de Lisboa, Faculdade de Ciências, 2018Consistent reductions in the costs of photovoltaic (PV) systems have prompted interest in applications with less-than-optimum inclinations and orientations. That is the case of building façades, with plenty of free area for the deployment of solar systems. Lower sun heights benefit vertical façades, whereas rooftops are favoured when the sun is near the zenith, therefore the PV potential in urban environments can increase twofold when the contribution from building façades is added to that of the rooftops. This complementarity between façades and rooftops is helpful for a better match between electricity demand and supply. This thesis focuses on: i) the modelling of façade PV potential; ii) the optimization of façade PV yields; and iii) underlining the overall role that building façades will play in future solar cities. Digital surface and solar radiation modelling methodologies were reviewed. Special focus is given to the 3D LiDAR-based model SOL and the CAD/plugin models DIVA and LadyBug. Model SOL was validated against measurements from the BIPV system in the façade of the Solar XXI building (Lisbon), and used to evaluate façade PV potential in different urban sites in Lisbon and Geneva. The plugins DIVA and LadyBug helped assessing the potential for PV glare from façade integrated photovoltaics in distinct urban blocks. Technologies for PV integration in façades were also reviewed. Alternative façade designs, including louvers, geometric forms and balconies, were explored and optimized for the maximization of annual solar irradiation using DIVA. Partial shading impacts on rooftops and façades were addressed through SOL simulations and the interconnections between PV modules were optimized using a custom Multi-Objective Genetic Algorithm. The contribution of PV façades to the solar potential of two dissimilar neighbourhoods in Lisbon was quantified using SOL, considering local electricity consumption. Cost-efficient rooftop/façade PV mixes are proposed based on combined payback times. Impacts of larger scale PV deployment on the spare capacity of power distribution transformers were studied through LadyBug and SolarAnalyst simulations. A new empirical solar factor was proposed to account for PV potential in future upgrade interventions. The combined effect of aggregating building demand, photovoltaic generation and storage on the self-consumption of PV and net load variance was analysed using irradiation results from DIVA, metered distribution transformer loads and custom optimization algorithms. SOL is shown to be an accurate LiDAR-based model (nMBE ranging from around 7% to 51%, nMAE from 20% to 58% and nRMSE from 29% to 81%), being the isotropic diffuse radiation algorithm its current main limitation. In addition, building surface material properties should be regarded when handling façades, for both irradiance simulation and PV glare evaluation. The latter appears to be negligible in comparison to glare from typical glaze/mirror skins used in high-rises. Irradiation levels in the more sunlit façades reach about 50-60% of the rooftop levels. Latitude biases the potential towards the vertical surfaces, which can be enhanced when the proportion of diffuse radiation is high. Façade PV potential can be increased in about 30% if horizontal folded louvers becomes a more common design and in another 6 to 24% if the interconnection of PV modules are optimized. In 2030, a mix of PV systems featuring around 40% façade and 60% rooftop occupation is shown to comprehend a combined financial payback time of 10 years, if conventional module efficiencies reach 20%. This will trigger large-scale PV deployment that might overwhelm current grid assets and lead to electricity grid instability. This challenge can be resolved if the placement of PV modules is optimized to increase self-sufficiency while keeping low net load variance. Aggregated storage within solar communities might help resolving the conflicting interests between prosumers and grid, although the former can achieve self-sufficiency levels above 50% with storage capacities as small as 0.25kWh/kWpv. Business models ought to adapt in order to create conditions for both parts to share the added value of peak power reduction due to optimized solar façades.Fundação para a Ciência e a Tecnologia (FCT), SFRH/BD/52363/201

Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets

Autonomous Driving (AD), the area of robotics with the greatest potential impact on society, has gained a lot of momentum in the last decade. As a result of this, the number of datasets in AD has increased rapidly. Creators and users of datasets can benefit from a better understanding of developments in the field. While scientometric analysis has been conducted in other fields, it rarely revolves around datasets. Thus, the impact, attention, and influence of datasets on autonomous driving remains a rarely investigated field. In this work, we provide a scientometric analysis for over 200 datasets in AD. We perform a rigorous evaluation of relations between available metadata and citation counts based on linear regression. Subsequently, we propose an Influence Score to assess a dataset already early on without the need for a track-record of citations, which is only available with a certain delay.Comment: Daniel Bogdoll and Jonas Hendl contributed equally. Accepted for publication at ICCRE 202

Proceedings of the 9th Arab Society for Computer Aided Architectural Design (ASCAAD) international conference 2021 (ASCAAD 2021): architecture in the age of disruptive technologies: transformation and challenges.

The ASCAAD 2021 conference theme is Architecture in the age of disruptive technologies: transformation and challenges. The theme addresses the gradual shift in computational design from prototypical morphogenetic-centered associations in the architectural discourse. This imminent shift of focus is increasingly stirring a debate in the architectural community and is provoking a much needed critical questioning of the role of computation in architecture as a sole embodiment and enactment of technical dimensions, into one that rather deliberately pursues and embraces the humanities as an ultimate aspiration

Definição de uma metodologia em SIG para a produção de cartografia de suscetibilidade a encandeamento solar na rede viária urbana: o caso de estudo da segunda circular

Trabalho de projeto de mestrado em Sistemas de Informação Geográfica (Tecnologias e Aplicações), Universidade de Lisboa, Faculdade de Ciências, 2019A segurança rodoviária é algo de extrema importância na prática da condução. Para isso é necessária uma atenção total por parte dos condutores e isso implica o uso dos sentidos. O sentido mais importante na prática da condução é a visão. Porém, este sentido pode ser perturbado devido ao encandeamento solar, reduzindo assim parcialmente ou totalmente a capacidade do condutor de ver a via e os perigos que estão a sua frente. O amanhecer, com o nascer do sol, e o anoitecer, com o pôr do sol, são dois períodos críticos em que o fenómeno de encandeamento ganha maiores proporções devido à baixa altura do sol, ficando este no campo de visão dos condutores infligindo assim encandeamento nos mesmos. Sendo que o amanhecer e o anoitecer são períodos em que o fluxo rodoviário está no seu auge, este fenómeno torna-se um grave perigo nas estradas. O objetivo deste trabalho é a definição de uma metodologia, em sistemas de informação geográfica, capaz de produzir cartografia da suscetibilidade rodoviária a encandeamento solar. Foi utilizada, como caso de estudo, a 2ª Circular de Lisboa, devido à sua geometria espacial propícia a encandeamento solar e por ser uma via importante da cidade de Lisboa. Para o efeito, foram calculados parâmetros importantes para cada troço da via na zona de estudo por forma a calcular a ocorrência de encandeamento solar com o uso do algoritmo GLARE2019 em desenvolvimento na Faculdade de Ciências da Universidade de Lisboa pela doutora Paula Redweik. Com a metodologia proposta neste trabalho, foi possível obter de forma sistemática e organizada a cartografia de suscetibilidade a encadeamento solar dos troços da 2ª Circular de Lisboa, podendo esta ser utilizada como forma de prevenção ou alerta para os condutores que nela circulam preparando-se de antemão para este perigo.Road safety is of paramount importance in driving practice. For this it is necessary a total attention on the part of the drivers and this implies use of the senses. The most important sense in the practice of driving is vision. However, this sense can be disturbed due to the solar glare, thus partially or totally reducing the driver's ability to see the road and the dangers in front of him. The dawn, with the sunrise, and the dusk, with the sunset, are two critical periods in which the phenomenon of glare gains greater proportions due to the low height of the sun, being this in the field of vision of the conductors inflicting glare. Since dawn and dusk are periods when the road flow is at its peak, this phenomenon becomes a serious danger on the roads. The objective of this work is the definition of a methodology, in geographic information systems, capable of producing cartography of road susceptibility to solar glare. It was used, as a case study, the 2nd Circular in Lisbon, due to its spatial geometry propitious to solar glare and for being an important route of the city of Lisbon. For this purpose, relevant parameters were calculated for each section of the track in the study area in order to calculate the occurrence of solar glare using the GLARE2019 algorithm under development at the Faculty of Sciences of the University of Lisbon by Dr. Paula Redweik. With the methodology proposed in this work, it was possible to obtain in a systematic and organized way, the mapping of susceptibility to solar glare of sections of the 2nd Circular, which could be used as a form of prevention or alert for the drivers that circulate in it preparing beforehand for this danger

Towards a GIS-based Multiscale Visibility Assessment Method for Solar Urban Planning

Urban areas are facing a growing deployment of solar photovoltaic and thermal tech-nologies on building envelopes, both on roofs and on façades, essential for the realization of the Swiss Energy Strategy 2050. This process often occurs regardless of the desirable archi-tectural integration quality in a given urban context, which depends on socio-cultural sensitivi-ty and on the visibility of the solar modules from the public space. Visibility and visual impact are recurrent decisional factors in spatial planning processes, with practical implications in-cluding touristic and real estate promotion, outdoor human comfort, way finding, public feeling of security and advertisement. In this thesis, the definition of visibility under a geometrical, physical and psycho-physiological perspective is explored, several quantitative indicators being described and test-ed. The objective is to provide a scale-dependent methodology to assess the visibility of build-ing envelope surfaces exposed to solar radiation, which could host solar modules, in urban areas. A visibility index is determined for inclusion as a variable in a multi criteria method, cover-ing areas from the strategic broad territorial scale to the district level, including neighborhoods and clusters of buildings. Accomplished research includes the estimation of public visual inter-est on the basis of crowd-sourced photographic databases, complementing geometry-based parameters such as cumulative viewsheds and solid angles. At each scale, the visibility index is systematically overlapped on an urban sensitivity layer issued from land use and on a spatial representation of the solar energy generation potential, at an appropriate level of detail. Results indicate that stakeholders can reasonably expect to harvest a serious amount of solar energy by means of building integrated solar systems without crucially affecting public perception. In the study area located in the city of Geneva (Switzerland), more than 50 m2 / building of non-visible envelope surface receiving sufficient solar radiation for an economically viable solar re-furbishment is available over half of the buildings. Solar thermal collectors or PV panels in-stalled on scarcely visible surfaces, mainly situated in courtyards, far from the streets or in deep urban canyons, could cover about 10% of the annual heating demand or alternatively, the same share of electricity needs on a district basis. At the same time, plenty of highly visible areas remain available for high-end solar deployments, which could also serve pilot and demonstration purposes

Short-term crash risk prediction considering proactive, reactive, and driver behavior factors

Providing a safe and efficient transportation system is the primary goal of transportation engineering and planning. Highway crashes are among the most significant challenges to achieving this goal. They result in significant societal toll reflected in numerous fatalities, personal injuries, property damage, and traffic congestion. To that end, much attention has been given to predictive models of crash occurrence and severity. Most of these models are reactive: they use the data about crashes that have occurred in the past to identify the significant crash factors, crash hot-spots and crash-prone roadway locations, analyze and select the most effective countermeasures for reducing the number and severity of crashes. More recently, the advancements have been made in developing proactive crash risk models to assess short-term crash risks in near-real time. Such models could be applied as part of traffic management strategies to prevent and mitigate the crashes. The driver behavior is found to be the leading cause of highway crashes. Nevertheless, due to data unavailability, limited studies have explored and quantified the role of driver behavior in crashes. The Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) offers an unprecedented opportunity to perform an in-depth analysis of the impacts of driver behavior on crashes events. The research presented in this dissertation is divided into three parts, corresponding to the research objectives. The first part investigates the application of advanced data modeling methods for proactive crash risk analysis. Several proactive models for segment level crash risk and severity assessment are developed and tested, considering the proactive data available to most transportation agencies in real time at a regional network scale. The data include roadway geometry characteristics, traffic flow characteristics, and weather condition data. The analysis methods include Random-effect Bayesian Logistics Regression, Random Forest, Gradient Boosting Machine, K-Nearest Neighbor, Gaussian Naive Bayes (GNB), and Multi-layer Feedforward Deep Neural Network (MLFDNN). The random oversampling technique is applied to deal with the problem of data imbalance associated with the injury severity analysis. The model training and testing are completed using a dataset containing records of 10,155 crashes that occurred on two interstate highways in New Jersey over a period of two years. The second part of the study analyzes the potential improvement in the prediction abilities of the proposed models by adding reactive data (such as vehicle characteristics and driver characteristics) to the analysis. Commonly, the reactive data is only available (known) after the crash occurs. In the proposed research, the crash analysis is performed by classifying crashes in multiple groupings (instead of a single group), constructed based on the age of drivers and vehicles to account for the impact of reactive data on driver injury severity outcomes. The results of the second part of the study show that while the simultaneous use of reactive and proactive data can improve the prediction performance of the models, the absolute crash probability values must be further improved for operational crash risk prediction. To this end, in the third part of the study, the Naturalistic Driving Study data is used to calibrate the crash risk models, including the driver behavior risk factors. The findings show significant improvement in crash prediction accuracy with the inclusion of driver behavior risk factors, which confirms the driver behavior to be the most critical risk factor affecting the crash likelihood and the associated injury severity