Collective Intelligence and the Mapping of Accessible Ways in the City: a Systematic Literature Review

This paper has the objective of assessing how ICTs are being used to provide accessibility in urban mobility, with special interest to collective intelligence approaches. A systematic literature review (SLR) was performed, using several different criteria to filter down the 500+ academic papers that were originally obtained from a search for “accessible maps” to the 43 papers that finally remained in the corpus of the SLR. Among the findings, it was noticed that (i) few studies explored the motivations of users that actively contribute, providing information to feed maps, and they restricted themselves to exploring three techniques: gaming, monetary reward and ranking; (ii) social networks are rarely used as a source of data for building and updating maps; and (iii) the literature does not discuss any initiative that aims to support the needs of physically and visually impaired citizens at the same time

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

End User Involvement in the Big Data Based Service Development Process

Even though extant research has addressed end user involvement in the process of Information Systems’ Development within a Smart City environment, it has not done it in its early or Fuzzy Front End phase. Therefore, this paper promotes and describes a concrete big data based service process in which end users and other individual stakeholders are involved since the early phases of development. Researchers used this end user data to define which part of the big data would be opened for developers and citizens in future stages of the projectinfo:eu-repo/semantics/publishedVersio

Optimized routing for people with permanent or temporary mobility disability: a case study in Viana do Castelo

Nowadays, it is already common to have apps to assist citizens in their mobility within a city. However, apps are usually designed for the general citizen and do not include the specificity of people with reduced mobility, temporarily or permanently, such as visually impaired people, autistic people, people in wheelchairs, among others segments. This paper illustrates a case study carried out in the city of Viana do Castelo, in Portugal, where the streets of the historic center of the city were classified in a Geographic Information System (GIS) by the City Council together with the institutions that represent each one of the considered segments. Based on this classification, the Viana+Acessivel app was developed, which is about to be made available free of charge to all citizens, and which recommends to each user the optimum route from a source to a destination, taking into account his segment. For example, visually impaired people should avoid streets where emergency vehicles can circulate and autistic people should preferably avoid streets with loud noises, among other conditions. The A -Star Algorithm and Dijkstra Algorithm were used in the app to identify the optimum route. A comparative study was made concluding that both strategies identify the optimum route and A -Star method obtains the optimum solution in a faster time. An evaluation of the app was also made in terms of its effectiveness and usability.info:eu-repo/semantics/publishedVersio

Adding Semantics to Enrich Public Transport and Accessibility Data from the Web

Web technologies and open data practices have now begun to promote new issues and services addressed to both final and specialized users. The smart cities initiative has also introduced new trends and ideas to offer to the public, one of which is the challenge of a more inclusive society that will provide the same opportunities for all. One of the major areas that could benefit from these new initiatives is public transport by, for example, providing open and accessible datasets, which include information by and about people with special needs. In this sense, the Google Transit Feed Specification (GTFS) defines a format to describe public transportation and associated geographic information. It includes details regarding accessibility and what people with special needs might require to get around using public transport. We are, however, of the opinion that this specification has a low granularity and is not sufficient, since it only takes into account only mobility needs. As suggestions for improvement, we propose to enrich GTFS data by combining public transport data from multiple Web sources with semantic metadata techniques. Those data are stored in a public semantic dataset. To define this dataset, we propose a systematic method to extract data from different sources and integrate them. This method is applied to obtain data about the metro system from the website of Metro Madrid and GTFS. Relevant SPARQL queries and two applications are developed to evaluate the usefulness of the dataset obtained

Participatory Management to Improve Accessibility in Consolidated Urban Environments

There is a wide range of regulations on universal accessibility, but our cities are still inaccessible in many cases. Most accessibility problems in cities occur in consolidated areas that were developed prior to the development of current accessibility regulations. This leads to consider the importance of focusing more effort on managing the improvement of the accessibility of existing public urban environments. As such, the objective of this research is to design a conceptual model for accessibility management in consolidated urban environments. Unlike other research focusing on city users to collect information on accessibility problems or to provide services to improve wayfinding, this method has a focus on urban accessibility managers. The model is based on the assessment of the level of accessibility of urban environments together with the assessment of management processes in which city users are actively involved. It consists of a set of basic indicators for the identification of accessible pedestrian routes, and provides a dynamic accessibility index for the evaluation of their efficient management by the responsible governments. The inclusion of this assessment framework in the management process itself enables the necessary improvement actions to be identified and taken in time. ICT (Information and Communication Technologies) provide the communication channel between the responsible governments and city users, making this a more dynamic and efficient management model based on assessment possible.This research was funded by the Conselleria of Innovation, Universities, Science and Digital Society, of the Community of Valencia, Spain, grant number AICO/2020/206, and by the University of Alicante, Spain, grant number GRE19-01

Participatory Sensing and Crowdsourcing in Urban Environment

With an increasing number of people who live in cities, urban mobility becomes one of the most important research fields in the so-called smart city environments. Urban mobility can be defined as the ability of people to move around the city, living and interacting with the space. For these reasons, urban accessibility represents a primary factor to keep into account for social inclusion and for the effective exercise of citizenship. In this thesis, we researched how to use crowdsourcing and participative sensing to effectively and efficiently collect data about aPOIs (accessible Point Of Interests) with the aim of obtaining an updated, trusted and completed accessible map of the urban environment. The data gathered in such a way, was integrated with data retrieved from external open dataset and used in computing personalized accessible urban paths. In order to deeply investigate the issues related to this research, we designed and prototyped mPASS, a context-aware and location-based accessible way-finding system

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