RT Journal Article
T1 Accessible routes integrating data from multiple sources
A1 Luaces, Miguel R.
A1 Arias Fisteus, Jesús
A1 Sánchez Fernández, Luis
A1 Muñoz Organero, Mario
A1 Balado, Jesús
A1 Díaz Vilariño, Lucía
A1 Lorenzo, Enrique
AB 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).
PB MDPI
SN 2220-9964
YR 2021
FD 2021-01
LK https://hdl.handle.net/10016/32657
UL https://hdl.handle.net/10016/32657
LA eng
NO This work was supported in part by the project Friendly barrierLess AdapTable City (FLATCity) (Ministerio de Ciencia, innovación y Universidades/ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI, doi 10.13039/501100011033), and in part by the European Regional Development Fund (ERDF), under Grants TIN2016-77158-C4-1-R, TIN2016-77158-C4-2-R and TIN2016-77158-C4-3-R. This work was also supported in part by the project Massive Geospatial Data Storage and Processing for Intelligent and Sustainable Urban Transportation (MaGIST), funded by the Spanish Agencia Estatal de Investigación (AEI, doi 10.13039/501100011033) under grants PID2019-105221RBC41, PID2019-105221RB-C43 and PID2019-105221RB-C44. The research of Miguel R. Luaces was also partially founded by: Xunta de Galicia/FEDER-UE GRC: ED431C 2017/58 and Xunta de Galicia/FEDER-UE, ConectaPeme, GEMA: IN852A 2018/14. Miguel R. Luaces also wishes to acknowledge the support received from the Centro de Investigación de Galicia "CITIC", funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program), by grant ED431G 2019/01. Jesús Balado and Lucía Díaz-Vilariño would like to thank to the Xunta de Galicia given through human resources grants ED481B-2019-061 and ED481D 2019/020, respectively.
DS e-Archivo
RD 20 may. 2024