Scalable Methods to Collect and Visualize Sidewalk Accessibility Data for People with Mobility Impairments

Poorly maintained sidewalks pose considerable accessibility challenges for people with mobility impairments. Despite comprehensive civil rights legislation of Americans with Disabilities Act, many city streets and sidewalks in the U.S. remain inaccessible. The problem is not just that sidewalk accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori. To address this problem, my Ph.D. dissertation introduces and evaluates new scalable methods for collecting data about street-level accessibility using a combination of crowdsourcing, automated methods, and Google Street View (GSV). My dissertation has four research threads. First, we conduct a formative interview study to establish a better understanding of how people with mobility impairments currently assess accessibility in the built environment and the role of emerging location-based technologies therein. The study uncovers the existing methods for assessing accessibility of physical environment and identify useful features of future assistive technologies. Second, we develop and evaluate scalable crowdsourced accessibility data collection methods. We show that paid crowd workers recruited from an online labor marketplace can find and label accessibility attributes in GSV with accuracy of 81%. This accuracy improves to 93% with quality control mechanisms such as majority vote. Third, we design a system that combines crowdsourcing and automated methods to increase data collection efficiency. Our work shows that by combining crowdsourcing and automated methods, we can increase data collection efficiency by 13% without sacrificing accuracy. Fourth, we develop and deploy a web tool that lets volunteers to help us collect the street-level accessibility data from Washington, D.C. As of writing this dissertation, we have collected the accessibility data from 20% of the streets in D.C. We conduct a preliminary evaluation on how the said web tool is used. Finally, we implement proof-of-concept accessibility-aware applications with accessibility data collected with the help of volunteers. My dissertation contributes to the accessibility, computer science, and HCI communities by: (i) extending the knowledge of how people with mobility impairments interact with technology to navigate in cities; (ii) introducing the first work that demonstrates that GSV is a viable source for learning about the accessibility of the physical world; (iii) introducing the first method that combines crowdsourcing and automated methods to remotely collect accessibility information; (iv) deploying interactive web tools that allow volunteers to help populate the largest dataset about street-level accessibility of the world; and (v) demonstrating accessibility-aware applications that empower people with mobility impairments

Data Collection and Machine Learning Methods for Automated Pedestrian Facility Detection and Mensuration

Large-scale collection of pedestrian facility (crosswalks, sidewalks, etc.) presence data is vital to the success of efforts to improve pedestrian facility management, safety analysis, and road network planning. However, this kind of data is typically not available on a large scale due to the high labor and time costs that are the result of relying on manual data collection methods. Therefore, methods for automating this process using techniques such as machine learning are currently being explored by researchers. In our work, we mainly focus on machine learning methods for the detection of crosswalks and sidewalks from both aerial and street-view imagery. We test data from these two viewpoints individually and with an ensemble method that we refer to as our “dual-perspective prediction model”. In order to obtain this data, we developed a data collection pipeline that combines crowdsourced pedestrian facility location data with aerial and street-view imagery from Bing Maps. In addition to the Convolutional Neural Network used to perform pedestrian facility detection using this data, we also trained a segmentation network to measure the length and width of crosswalks from aerial images. In our tests with a dual-perspective image dataset that was heavily occluded in the aerial view but relatively clear in the street view, our dual-perspective prediction model was able to increase prediction accuracy, recall, and precision by 49%, 383%, and 15%, respectively (compared to using a single perspective model based on only aerial view images). In our tests with satellite imagery provided by the Mississippi Department of Transportation, we were able to achieve accuracies as high as 99.23%, 91.26%, and 93.7% for aerial crosswalk detection, aerial sidewalk detection, and aerial crosswalk mensuration, respectively. The final system that we developed packages all of our machine learning models into an easy-to-use system that enables users to process large batches of imagery or examine individual images in a directory using a graphical interface. Our data collection and filtering guidelines can also be used to guide future research in this area by establishing standards for data quality and labelling

3 D analysis methods for supporting the design of walkable streets

Tese de Doutoramento em Arquitetura com a especialização em Desenho e Computação apresentada na Faculdade de Arquitetura da Universidade de Lisboa para obtenção de grau de Doutor.Os aglomerados urbanos em rápido crescimento contribuem e enfrentam hoje, as consequências de crises globais, como a poluição, as alterações climáticas, a diminuição dos recursos naturais, conflitos sociais e migrações em massa. O planeamento e projecto do ambiente construído são essenciais para uma correcta organização da vida urbana, de modo a reduzir a poluição, distribuir recursos de maneira justa, fortalecer laços sociais e comunitários e prosperar economicamente. Projectar cidades incentivando a pedestrianização como meio de transporte constitui uma contribuição para esses objectivos, facilitando a mitigação da poluição, o acesso livre e democrático aos recursos urbanos, revitalizando as ruas e consequentemente apoiando as economias locais. Embora a investigação sobre a pedestrianização e caminhabilidade do ambiente construído já tenha décadas, temos hoje dados urbanos atualizados e ferramentas mais precisas do que nunca, que permitem uma análise detalhada dos factores que promovem a pedestrianização, podendo suportar decisões baseadas em evidências para o desenvolvimento de uma mobilidade mais sustentável. Tais ferramentas de planeamento viabilizam também uma melhor integração destes dados nos processos de projecto bem como a sua comunicação aos vários agentes participantes na decisão. Esta dissertação defende a necessidade de um método de análise 3D à escala da rua para informar soluções flexíveis de projecto urbano baseadas em dados urbanos rapidamente actualizáveis e acessíveis remotamente, obtidos sem a necessidade de pesquisas no local. Este método preenche uma lacuna existente na literatura propondo um fluxo de trabalho semi-automático. Este fluxo de trabalho propõe-se solucionar a desconexão entre a investigação no campo da pedestrianização, as ferramentas existentes e os processos de planeamento e projecto urbano. Argumenta-se que essa desconexão resulta da priorização de preocupações financeiras nos processos de planeamento e desenho urbano e da falta de métodos de avaliação rápidos e práticos aplicáveis nas várias etapas e escalas de projecto e de um modo fragmentado ou holístico. Além disso, os métodos existentes de avaliação da caminhabilidade que avaliam contextos urbanos nestas escalas e detalhe, não são capazes de avaliar ruas através de dados urbanos acedidos remotamente, recorrendo geralmente a auditorias ou pesquisas onerosas e morosas no local. O fluxo de trabalho proposto neste estudo visa responder a esta necessidade; combina um modelo 3D de uma unidade de vizinhança desenvolvido num ambiente de programação visual, SIG e códigos personalizados, e utiliza um modelo de análise morfológica chamado Convex e Solid-Void, combinado com técnicas de Web-scrapping e reconhecimento de imagem. A dissertação contribui para a investigação sobre caminhabilidade, propondo um fluxo de trabalho de análise de caminhabilidade em escala micro, em 3D, e remotamente aplicável, além de distinguir indicadores aplicáveis a ruas com diferentes formas e usos. O método promove o modelo computacional de análise urbana, Convex e Solid-Void, apresentando a sua primeira aplicação ao problema urbano da caminhabilidade. Também demonstra a integração de fontes de dados acessíveis remotamente, incluindo imagens de Street View obtidas de uma plataforma de mapas on-line e dados de redes sociais geo-localizados, para a avaliação quantitativa dos espaços urbanos. De futuro, pretende-se desenvolver o método para permitir o acesso remoto da avaliação a várias dessas fontes de dados. Tal é possível pelo uso combinado de SIG com representações espaciais 3D e ferramentas de programação integradas no mesmo fluxo de trabalho. Estes ambientes, que facilitam a associação de elementos espaciais com informações semânticas por meio de bases de dados, possibilitam a utilização de quaisquer dados que possam ser processados em análise espacial para alimentação de processos de projecto gerativo. O resultado desta pesquisa apresenta-se na forma de recomendações de planeamento e desenho urbano e também pretende ser um recurso prático a ser usado em projectos de reabilitação urbana. Como parte do modelo Convex e Solid-Void usado neste estudo, apresenta-se uma nova unidade espacial 3D "Street-Void", na qual todos os dados coletados são agregados para análise. Identificam-se indicadores específicos para avaliar com mais precisão os espaços das ruas, primeiro distinguindo entre ruas e praças e depois avaliando quantitativamente espaços semelhantes a ruas e espaços semelhantes a praças, e ainda espaços residenciais e de uso misto. Com base nos resultados da aplicação do método a quatro bairros estudados nas cidades de Istambul e Lisboa, e uma classificação das ruas usando os indicadores identificados, apresenta-se um conjunto de recomendações, que se atribuem a intervalos de valores próprios das tipologias específicas de ruas. Estas recomendações são formuladas para que possam ser aplicadas holisticamente ou de maneira fragmentada em diferentes fases de projecto ou cenários de melhoria urbana. Este estudo amplia o conhecimento sobre pedestrianização, sugerindo diferentes indicadores e faixas de valor para a avaliação de ruas, relacionando caminhabilidade com a variação das suas formas e usos. A tese está organizada da seguinte forma. No capítulo de introdução, são apresentados brevemente os objetivos da pesquisa, a contribuição e importância para o tema, metodologia, resultados e conclusão. No segundo capítulo, são apresentadas as questões de investigação a que a tese responde e a hipótese construída sobre essas questões. Estas questões podem ser listadas da seguinte maneira. Como podem a caminhabilidade e seus critérios serem integrados nos processos de desenho urbano (à escala do bairro)? Quais as qualidades do ambiente urbano construído que devem ser consideradas para a avaliação da caminhabilidade, para que as decisões de projecto possam ser informadas com mais eficácia? Como podemos avaliar a pedestrianização de um bairro num ambiente urbano complexo e em constante mudança? O terceiro capítulo apresenta uma revisão da literatura no tema da pesquisa, incluindo os temas do projecto urbano centrados no ser humano, investigação existente sobre a medição da caminhabilidade e sobre ferramentas de projecto algorítmico desenvolvidas para a escala urbana e em particular para a escala do bairro. No quarto capítulo, são explicados o método do estudo realizado e os princípios do fluxo de trabalho acima apresentados. Discute-se o processo de selecção utilizado para determinar os atributos quantitativos para a medição da caminhabilidade. As “características” sob as quais esses atributos são agrupados são a densidade, diversidade, conectividade, escala humana, complexidade, clausura (enclosure), forma, inclinação, permeabilidade e infraestrutura. Estas características e atributos são reduzidos posteriormente através de um processo de eliminação aos seus componentes principais. O quinto capítulo apresenta os estudos de caso dos bairros que são utilizados no desenvolvimento do fluxo de trabalho de medição, a interpretação dos atributos de caminhabilidade face aos dados medidos e uma análise inicial desses dados quantitativos. No sexto capítulo, o uso de dados de redes sociais e imagens street view como representantes de caminhabilidade são testados por métodos estatísticos e os espaços das ruas analisados são classificados com base nos atributos medidos (através de um método de clustering). Tipologias de rua com atributos específicos são identificadas nas várias classes (clusters) obtidas. Os atributos são avaliados com base na comparação de seus resultados quantitativos para cada tipologia de rua e são reduzidos através de um processo de filtragem. O sétimo capítulo inclui uma reclassificação das ruas com base em suas formas e usos e uma avaliação das medidas dos seus atributos com base na comparação dos seus resultados para essas classes. Através dessa avaliação, diferentes intervalos de valores foram determinados para serem aplicados aos diferentes atributos das ruas, e as descobertas obtidas por este método foram convertidas num guia destinado a informar os processos de desenho e planeamento urbano. O oitavo capítulo resume a produção geral da tese, a sua contribuição para o conhecimento, bem como para os processos de projecto e planeamento urbano. Partindo dos seus aspectos inovadores, fornece também uma visão geral dos estudos futuros que a tese pode proporcionar. No presente desenvolvimento, o método proposto nesta tese para a medição da caminhabilidade e respectivas recomendações para os processos de projecto e planeamento podem ser utilizadas como parte de serviços de consultoria a ser prestados a municípios, consultoria particular e a profissionais de projecto e planeamento. Em estudos futuros, pretende-se tornar o fluxo de trabalho apresentado numa ferramenta que pode ser utilizada diretamente por projectistas e planeadores. Prevê-se que tais estudos sejam desenvolvidos através da multiplicação dos contextos estudados, melhorando a qualidade e a precisão dos dados urbanos utilizados, aumentando o nível de detalhe capturado pelo modelo de análise e aplicando a análise a fenómenos urbanos que não sejam somente a caminhabilidade. Devido às semelhanças dos seus ambientes construídos, os bairros utilizados no presente estudo, que são Kadikoy e Hasanpasa em Istambul e Chiado e Ajuda em Lisboa, permitiram a avaliação de um conjunto consistente de ruas, oferecendo variedade suficiente. Mais especificamente, devido às semelhanças em termos de escala e uso, quando os espaços das ruas desses bairros foram classificados com base nos atributos utilizados, revelaram-se 6 tipologias diferentes de espaços de rua. Prevê-se que essas tipologias sejam multiplicadas pela aplicação do método a contextos diferentes em termos de escala, forma e uso. Devido à disponibilidade de dados detalhados e a uma variedade de espaços nas ruas em termos dos critérios mencionados, Nova York, Singapura e Amsterdão são exemplos de cidades que poderão ser estudadas como novos casos de estudo.ABSTRACT: Today, rapidly growing urban populations both contribute to global crises such as pollution, climate change, diminishing natural resources, social conflicts and mass migrations and face the consequences. The built environment, its planning and design are critical in organizing urban life so that we pollute less, distribute our resources fairly, strengthen social and communal ties and thrive economically. Designing our cities to support walking as a means of transport contributes in these goals through facilitating pollution free and democratic access to urban resources, supporting local economies and enlivening the street. While research on walkability of the built environment is decades old now, we have more up-to-date, accurate and large-scale urban data than ever and our developing tools make it possible to feed this data into design and management processes to create and sustain more walkable environments. This dissertation argues for the necessity of a street-scale, 3d analysis method to inform flexible urban design solutions based on rapidly updatable and remotely accessible urban data obtained without the necessity of on-site surveys, proposing a semi-automated workflow to fill this gap in existing literature. The workflow combines a 3d neighborhood model in a visual programming environment, GIS and custom codes, utilizing a morphological analysis model named Convex and Solid-Voids, together with web scraping and image recognition techniques. A 3d street space unit “Street-Void” is presented within the Convex and Solid-Void model in which all gathered data is aggregated for analysis. Specific indicators are identified to more accurately assess street spaces, first by distinguishing between and then quantitatively evaluating street-like and square-like, residential and mixed-use streets. Based on the findings from the application of the workflow to four neighborhoods studied in the cities of Istanbul and Lisbon and a classification of street spaces using the proposed attributes, a set of recommendations are presented, with value ranges applicable to specific street typologies. These recommendations are formulated so that they can be applied holistically or in a fragmented way at different stages of planning and urban improvement scenarios with their projected impact grouped under direct/physical or indirect/perceptual. The dissertation contributes to walkability research by proposing a micro-scale, 3d and remotely applicable walkability analysis workflow as well as distinguishing between indicators to be applied to street spaces of different shapes and uses. It furthers the computational urban analysis model Convex and Solid-Voids by presenting its first-time application to the tangible urban problem of walkability. It also demonstrates the integration of remotely accessible data sources including street view images from an online map platform and location based social network data to the quantitative evaluation of urban street spaces. With urban planning and design recommendations, it demonstrates the practical application of the findings to urban improvement scenarios. The study is envisioned to be developed by future work through multiplying the contexts that are studied, improving the quality and accuracy of urban data utilized, increasing the level of detail captured by the morphological analysis model and applying the analysis to other urban phenomena other than walkability.N/

Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data

Urban planning is increasingly data driven, yet the challenge of designing with data at a city scale and remaining sensitive to the impact at a human scale is as important today as it was for Jane Jacobs. We address this challenge with Urban Mosaic,a tool for exploring the urban fabric through a spatially and temporally dense data set of 7.7 million street-level images from New York City, captured over the period of a year. Working in collaboration with professional practitioners, we use Urban Mosaic to investigate questions of accessibility and mobility, and preservation and retrofitting. In doing so, we demonstrate how tools such as this might provide a bridge between the city and the street, by supporting activities such as visual comparison of geographically distant neighborhoods,and temporal analysis of unfolding urban development.Comment: Video: https://www.youtube.com/watch?v=Nrhk7lb3GU

SmartWheels: Detecting urban features for wheelchair users’ navigation

People with mobility impairments have heterogeneous needs and abilities while moving in an urban environment and hence they require personalized navigation instructions. Providing these instructions requires the knowledge of urban features like curb ramps, steps or other obstacles along the way. Since these urban features are not available from maps and change in time, crowdsourcing this information from end-users is a scalable and promising solution. However, it is inconvenient for wheelchair users to input data while on the move. Hence, an automatic crowdsourcing mechanism is needed. In this contribution we present SmartWheels, a solution to detect urban features by analyzing inertial sensors data produced by wheelchair movements. Activity recognition techniques are used to process the sensors data stream. SmartWheels is evaluated on data collected from 17 real wheelchair users navigating in a controlled environment (10 users) and in-the-wild (7 users). Experimental results show that SmartWheels is a viable solution to detect urban features, in particular by applying specific strategies based on the confidence assigned to predictions by the classifier

FINDING OBJECTS IN COMPLEX SCENES

Object detection is one of the fundamental problems in computer vision that has great practical impact. Current object detectors work well under certain con- ditions. However, challenges arise when scenes become more complex. Scenes are often cluttered and object detectors trained on Internet collected data fail when there are large variations in objects’ appearance. We believe the key to tackle those challenges is to understand the rich context of objects in scenes, which includes: the appearance variations of an object due to viewpoint and lighting condition changes; the relationships between objects and their typical environment; and the composition of multiple objects in the same scene. This dissertation aims to study the complexity of scenes from those aspects. To facilitate collecting training data with large variations, we design a novel user interface, ARLabeler, utilizing the power of Augmented Reality (AR) devices. Instead of labeling images from the Internet passively, we put an observer in the real world with full control over the scene complexities. Users walk around freely and observe objects from multiple angles. Lighting can be adjusted. Objects can be added and/or removed to the scene to create rich compositions. Our tool opens new possibilities to prepare data for complex scenes. We also study challenges in deploying object detectors in real world scenes: detecting curb ramps in street view images. A system, Tohme, is proposed to combine detection results from detectors and human crowdsourcing verifications. One core component is a meta-classifier that estimates the complexity of a scene and assigns it to human (accurate but costly) or computer (low cost but error-prone) accordingly. One of the insights from Tohme is that context is crucial in detecting objects. To understand the complex relationship between objects and their environment, we propose a standalone context model that predicts where an object can occur in an image. By combining this model with object detection, it can find regions where an object is missing. It can also be used to find out-of-context objects. To take a step beyond single object based detections, we explicitly model the geometrical relationships between groups of objects and use the layout information to represent scenes as a whole. We show that such a strategy is useful in retrieving indoor furniture scenes with natural language inputs

健康地理学研究の発展のための人のモビリティに関する都市情報学の応用

Tohoku University中谷友樹課

ANALYZING DETERMINANTS OF URBAN VIBRANCY – A BIG DATA APPROACH ON CONNECTING BUILT ENVIRONMENT, SOCIAL ACTIVITY, AND IMAGES OF PLACES

This dissertation includes three self-contained and interrelated papers on built environment, urban vibrancy and vibrant places. Paper 1: Built environment and social activity: Exploring the relationship between place pattern and tweeting in Chicago Understanding the relationships between human activities and the built environment are central to urban planning. The increase in readily available, location-based social media data offers scholars important new data for understanding this relationship. This study examines the relationship between the spatial distribution of geotagged tweets and key characteristics of the built environment at the census block group level in Chicago. First, we performed a hotspot analysis to ascertain the distribution of tweets in the study area. Then, we employed a count regression model with Twitter message counts by census block group as the dependent variable to test the significance and magnitude of the associations between the built environment and tweeting. Then, we standardized the coefficients to compare the variables’ effects on tweeting. The analysis found that the built environment significantly influenced tweeting and provides empirical statistical evidence to guide urban planners’ placemaking decisions. Paper 2: Built environment and social activity: Exploring the relationship between place pattern and tweeting in the U.S. This study focuses on the relationship between the spatial distribution of geotagged tweets and the key characteristics of built environments as well as the pattern of the features at the census block group (CBG) level within the contiguous U.S. First, we employed a count regression model, setting the Twitter message density by CBG as the dependent variable to test the significance and magnitude of the associations between the built environment and tweeting behavior. Then, we utilized a combined research framework based on cluster analysis, hotspot analysis, and standardized score to explore the built environment pattern of the most vibrant tweeting areas. Results revealed that the built environment significantly influenced tweeting behavior. We further discovered four different built environment pattern types that provides empirical statistical evidence to guide urban planners’ placemaking decisions. Paper3: Built environment and vibrancy perception: Applications of deep learning and computer vision techniques in streetview. This study shows that street view image data from digital platforms such as Google Maps can further improve our understanding of the built environment patterns in cities. Compared with traditional human environment audit methods, combining deep learning and computer vision techniques efficiently provides finer resolution information with greater environmental detail. This will allow for many larger-scale street view studies to be conducted in the future. Using online survey data on vibrancy perception, the findings indicate that factors such as street parking have a strong positive influence on vibrancy perception while the proportion of sky has a strongly negative association. The study identifies six different patterns of street view landscape and shows that the complex relationship between the built environment and vibrancy perception is better analyzed using pattern discovery methods rather than global regression. A further regression analysis based on each street view pattern confirms that the context is crucial in determining both the significance and magnitude of the built environment factors on urban vibrancy perception.Doctor of Philosoph

NOVA mobility assistive system: Developed and remotely controlled with IOPT-tools

UID/EEA/00066/2020In this paper, a Mobility Assistive System (NOVA-MAS) and a model-driven development approach are proposed to support the acquisition and analysis of data, infrastructures control, and dissemination of information along public roads. A literature review showed that the work related to mobility assistance of pedestrians in wheelchairs has a gap in ensuring their safety on road. The problem is that pedestrians in wheelchairs and scooters often do not enjoy adequate and safe lanes for their circulation on public roads, having to travel sometimes side by side with vehicles and cars moving at high speed. With NOVA-MAS, city infrastructures can obtain information regarding the environment and provide it to their users/vehicles, increasing road safety in an inclusive way, contributing to the decrease of the accidents of pedestrians in wheelchairs. NOVA-MAS not only supports information dissemination, but also data acquisition from sensors and infrastructures control, such as traffic light signs. For that, it proposed a development approach that supports the acquisition of data from the environment and its control while using a tool framework, named IOPT-Tools (Input-Output Place-Transition Tools). IOPT-Tools support controllers’ specification, validation, and implementation, with remote operation capabilities. The infrastructures’ controllers are specified through IOPT Petri net models, which are then simulated using computational tools and verified using state-space-based model-checking tools. In addition, an automatic code generator tool generates the C code, which supports the controllers’ implementation, avoiding manual codification errors. A set of prototypes were developed and tested to validate and conclude on the feasibility of the proposals.publishersversionpublishe