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

A Systematic Review of Urban Navigation Systems for Visually Impaired People

Blind and Visually impaired people (BVIP) face a range of practical difficulties when undertaking outdoor journeys as pedestrians. Over the past decade, a variety of assistive devices have been researched and developed to help BVIP navigate more safely and independently. In~addition, research in overlapping domains are addressing the problem of automatic environment interpretation using computer vision and machine learning, particularly deep learning, approaches. Our aim in this article is to present a comprehensive review of research directly in, or relevant to, assistive outdoor navigation for BVIP. We breakdown the navigation area into a series of navigation phases and tasks. We then use this structure for our systematic review of research, analysing articles, methods, datasets and current limitations by task. We also provide an overview of commercial and non-commercial navigation applications targeted at BVIP. Our review contributes to the body of knowledge by providing a comprehensive, structured analysis of work in the domain, including the state of the art, and guidance on future directions. It will support both researchers and other stakeholders in the domain to establish an informed view of research progress