4.5 Article

Accessible Routes Integrating Data from Multiple Sources

出版社

MDPI
DOI: 10.3390/ijgi10010007

关键词

spatial data mining; geospatial NLP; geospatial data fusion; large scale geospatial processing; pedestrian navigation; physical accessibility

资金

  1. project Friendly barrierLess AdapTable City (FLATC-ity) (Ministerio de Ciencia, innovacion y Universidades/ERDF, EU) - Spanish Agencia Estatal de Investigacion (AEI)
  2. European Regional Development Fund (ERDF) [TIN2016-77158-C4-1-R, TIN2016-77158-C4-2-R, TIN2016-77158-C4-3-R]
  3. project Massive Geospatial Data Storage and Processing for Intelligent and Sustainable Urban Transportation (MaGIST) - Spanish Agencia Estatal de Investigacion (AEI) [PID2019-105221RB-C41, PID2019-105221RB-C43, PID2019-105221RB-C44]
  4. Xunta de Galicia/FEDER-UE [GRC: ED431C 2017/58]
  5. Xunta de Galicia/FEDER-UE, ConectaPeme [GEMA: IN852A 2018/14]
  6. Centro de Investigacion de Galicia CITIC - Xunta de Galicia
  7. European Union (European Regional Development Fund-Galicia 2014-2020 Program) [ED431G 2019/01]
  8. Xunta de Galicia [ED481B-2019-061, ED481D 2019/020]

向作者/读者索取更多资源

This paper introduces an architecture of an information system that creates an accessibility data model for cities by ingesting data from different sources and provides an application for computing accessible routes for people with different abilities. The system collects and integrates various information sources, including data extracted from maps and mobile sensing, as well as information collected from citizens' mobile devices sensors, in order to detect accessibility problems in the city.
Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

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