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

Toward an Automatic Road Accessibility Information Collecting and Sharing Based on Human Behavior Sensing Technologies of Wheelchair Users

AbstractThis research proposes a methodology for digitizing street level accessibility with human sensing of wheelchair users. The dig- itization of street level accessibility is essential to develop accessibility maps or to personalize a route considering accessibility. However, current digitization methodologies are not sufficient because it requires a lot of manpower and therefore money and time cost. The proposed method makes it possible to digitize the accessibility semi-automatically. In this research, a three-axis accelerometer embedded on iPod touch sensed actions of nine wheelchair users across the range of disabilities and aged groups, in Tokyo, approximately 9hours. This paper reports out attempts to estimate both environmental factors: the status of street and subjective factors: driver's fatigue from human sensing data using machine learning

A Pilot Study of Sidewalk Equity in Seattle Using Crowdsourced Sidewalk Assessment Data

We examine the potential of using large-scale open crowdsourced sidewalk data from Project Sidewalk to study the distribution and condition of sidewalks in Seattle, WA. While potentially noisier than professionally gathered sidewalk datasets, crowdsourced data enables large, cross-regional studies that would be otherwise expensive and difficult to manage. As an initial case study, we examine spatial patterns of sidewalk quality in Seattle and their relationship to racial diversity, income level, built density, and transit modes. We close with a reflection on our approach, key limitations, and opportunities for future work.Comment: Workshop paper presented at "The 1st ASSETS'22 Workshop on The Future or urban Accessibility (UrbanAccess'22)

Combining crowdsourcing and google street view to identify street-level accessibility problems

ABSTRACT Poorly maintained sidewalks, missing curb ramps, and other obstacles pose considerable accessibility challenges; however, there are currently few, if any, mechanisms to determine accessible areas of a city a priori. In this paper, we investigate the feasibility of using untrained crowd workers from Amazon Mechanical Turk (turkers) to find, label, and assess sidewalk accessibility problems in Google Street View imagery. We report on two studies: Study 1 examines the feasibility of this labeling task with six dedicated labelers including three wheelchair users; Study 2 investigates the comparative performance of turkers. In all, we collected 13,379 labels and 19,189 verification labels from a total of 402 turkers. We show that turkers are capable of determining the presence of an accessibility problem with 81% accuracy. With simple quality control methods, this number increases to 93%. Our work demonstrates a promising new, highly scalable method for acquiring knowledge about sidewalk accessibility

Combining crowdsourcing and Google street view to identify street-level accessibility problems

ABSTRACT Poorly maintained sidewalks, missing curb ramps, and other obstacles pose considerable accessibility challenges; however, there are currently few, if any, mechanisms to determine accessible areas of a city a priori. In this paper, we investigate the feasibility of using untrained crowd workers from Amazon Mechanical Turk (turkers) to find, label, and assess sidewalk accessibility problems in Google Street View imagery. We report on two studies: Study 1 examines the feasibility of this labeling task with six dedicated labelers including three wheelchair users; Study 2 investigates the comparative performance of turkers. In all, we collected 13,379 labels and 19,189 verification labels from a total of 402 turkers. We show that turkers are capable of determining the presence of an accessibility problem with 81% accuracy. With simple quality control methods, this number increases to 93%. Our work demonstrates a promising new, highly scalable method for acquiring knowledge about sidewalk accessibility

Project Sidewalk: A Web-based crowdsourcing tool for collecting sidewalk accessibility data at scale

Ministry of Education, Singapore under its Academic Research Funding Tier

Évaluation et la représentation spatiotemporelle de l'accessibilité des réseaux piétonniers pour le déplacement des personnes à mobilité réduite

La mobilité des personnes à mobilité réduite (PMR) joue un rôle important dans leur inclusion sociale. Les PMR ont besoin de se déplacer de manière autonome pour effectuer leurs routines quotidiennes comme aller à l'école, au travail, au centre de remise en forme ou faire du magasinage. Cependant, celles-ci ne sont pas entièrement exécutées en raison de la conception non-adaptée des villes pour ces personnes. En effet, la mobilité est une habitude de vie humaine qui est le résultat d'interactions entre les facteurs humains (par exemple, les capacités) et les facteurs environnementaux. Au cours des dernières années, la mise au point de technologies d’aide technique s'est développée progressivement pour permettre aux PMR d’améliorer leur qualité de vie. En particulier, ces technologies offrent une variété de caractéristiques qui permettent à ces personnes de surmonter divers obstacles qui réduisent leur mobilité et contribuent à leur exclusion sociale. Cependant, malgré la disponibilité des technologies d’aide à la navigation et à la mobilité, leur potentiel est mal exploité pour les PMR. En effet, ces technologies ne considèrent pas les interactions « humain-environnement » adéquatement pour ces utilisateurs. L'objectif général de cette thèse est d'utiliser les potentiels des méthodes et des technologies de science de l'information géographique (SIG) afin d’aider à surmonter les problèmes de mobilité des PMR en créant un cadre d'évaluation de l'accessibilité et en développant une approche personnalisée de routage qui prend en compte les profils de ces personnes. Pour atteindre ce but, quatre objectifs spécifiques sont considérés: 1) développer une ontologie de mobilité pour les PMR qui considère les facteurs personnels et environnementaux, 2) proposer une méthode de l’évaluation de l'accessibilité du réseau piétonnier pour la mobilité des PMR en considérant spécifiquement les interactions entre les facteurs humains (la confiance) et les facteurs environnementaux, 3) étudier le rôle des facteurs sociaux dans l'accessibilité des zones urbaines et, finalement, 4) affiner les algorithmes existants pour calculer les itinéraires accessibles personnalisés pour les PMR en considérant leurs profils. En effet, tout d'abord pour développer une ontologie pour la mobilité des PMR, la dimension sociale de l'environnement ainsi que la dimension physique sont intégrées et une nouvelle approche basée sur une perspective « nature-développement » est présentée. Ensuite, une approche fondée sur la confiance des PMR est développée pour l'évaluation de l'accessibilité du réseau piétonnier, compte tenu de l'interaction entre les facteurs personnels et les facteurs environnementaux. De plus, dans une perspective de considération des facteurs sociaux, le rôle des actions politiques sur l'accessibilité du réseau piétonnier est étudié et l'influence de trois politiques potentielles est analysée. Enfin, une nouvelle approche pour calculer des itinéraires personnalisés pour les PMR en tenant compte de leurs perceptions, de leurs préférences et de leurs confidences est proposée. Les approches proposées sont développées et évaluées dans le quartier Saint-Roch à Québec, et ce, en utilisant une application d'assistance mobile et multimodale développée dans le cadre du projet MobiliSIG.Mobility of people with motor disabilities (PWMD) plays a significant role in their social inclusion. PWMD need to move around autonomously to perform their daily routines such as going to school, work, shopping, and going to fitness centers. However, mostly these needs are not accomplished because of either limitations concerning their capabilities or inadequate city design. Indeed, mobility is a human life habit, which is the result of interactions between people and their surrounded environments. In recent years, assistive technologies have been increasingly developed to enable PWMD to live independently and participate fully in all aspects of life. In particular, these technologies provide a variety of features that allow these individuals to overcome diverse obstacles that reduce their mobility and contribute to their social exclusion. However, despite increasing availability of assistive technologies for navigation and mobility, their potential is poorly exploited for PWMD. Indeed, these technologies do not fully consider the human-environment interactions. The overall goal of this dissertation is to benefit from the potentials of methods and technologies of the Geographic Information Sciences (GIS) in order to overcome the mobility issues of PWMD by creating an accessibility-assessing framework and ultimately by developing a personalized routing approach, which better considers the humanenvironment interaction. To achieve this goal, four specific objectives were followed: 1) develop a mobility ontology for PWMD that considers personal factors as well as environmental factors, 2) propose a method to evaluate the accessibility of the pedestrian network for the mobility of PWMD considering the interactions between human factors (confidence) and the environmental factors, 3) study of the role of social factors in the accessibility of urban areas, and finally, 4) refine the existing algorithms to calculate accessible routes for PWMD considering their profile. First, to develop an adapted ontology for mobility of the PWMD, the social dimension of the environment with the physical dimension were integrated and a new approach based on a “Nature-Development” perspective was presented. This perspective led to the development of useful ontologies, especially for defining the relationships between the social and physical parts of the environment. Next, a confidence-based approach was developed for evaluation of the accessibility of pedestrian network considering the interaction between personal factors and environmental factors for the mobility of PWMD. In addition, the role of policy actions on the accessibility of the pedestrian network was investigated and the influence of three potential policies was analyzed. Finally, a novel approach to compute personalized routes for PWMD considering their perception, preferences, and confidences was proposed. The approaches proposed were implemented in the Saint-Roch area of Quebec City and visualized within the multimodal mobile assistive technology (MobiliSIG) applicatio

Green Cities Artificial Intelligence

119 pagesIn an era defined by rapid urbanization, the effective planning and management of cities have become paramount to ensure sustainable development, efficient resource allocation, and enhanced quality of life for residents. Traditional methods of urban planning and management are grappling with the complexities and challenges presented by modern cities. Enter Artificial Intelligence (AI), a disruptive technology that holds immense potential to revolutionize the way cities are planned, designed, and operated. The primary aim of this report is to provide an in-depth exploration of the multifaceted role that Artificial Intelligence plays in modern city planning and management. Through a comprehensive analysis of key AI applications, case studies, challenges, and ethical considerations, the report aims to provide resources for urban planners, City staff, and elected officials responsible for community planning and development. These include a model City policy, draft informational public meeting format, AI software and applications, implementation actions, AI timeline, glossary, and research references. This report represents the cumulative efforts of many participants and is sponsored by the City of Salem and Sustainable City Year Program. The Green Cities AI project website is at: https://blogs.uoregon.edu/artificialintelligence/. As cities continue to evolve into complex ecosystems, the integration of Artificial Intelligence stands as a pivotal force in shaping their trajectories. Through this report, we aim to provide a comprehensive understanding of how AI is transforming the way cities are planned, operated, and experienced. By analyzing the tools, applications, and ethical considerations, we hope to equip policymakers, urban planners, and stakeholders with the insights needed to navigate the AI-driven urban landscape effectively and create cities that are not only smart but also sustainable, resilient, and regenerative.This year's SCYP partnership is possible in part due to support from U.S. Senators Ron Wyden and Jeff Merkley, as well as former Congressman Peter DeFazio, who secured federal funding for SCYP through Congressionally Directed Spending. With additional funding from the city of Salem, the partnerships will allow UO students and faculty to study and make recommendations on city-identified projects and issues

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

Design Opportunities for Assistive and Social Technologies Situated in Space

Computers are increasingly integrated into daily environments. Human interactions with computers become inseparable from interacting with physical spaces. I take a qualitative research approach to consider space as an integral part of understanding people, leading to design opportunities for technologies situated in space. My thesis presents the design opportunities for blind assistive technology to support exploratory navigation and physically-grounded social technology for remote connections. Studying space gives insights into how users and technologies situated in space interact with one another, how users perceive the physical space, and what behavioral patterns users display around the space while using technology. The insights contribute to constructing design implications from an underexplored creative angle and shed light on how spatial scenarios dynamically change preferences and opportunities in the design of future technologies