RT Journal Article
T1 Detection of barriers to mobility in the smart city using Twitter
A1 Sánchez Ávila, Mario
A1 Mouriño García, Marcos Antonio
A1 Arias Fisteus, Jesús
A1 Sánchez Fernández, Luis
AB We present a system that analyzes data extracted from the microbloging site Twitter to detect the occurrence of events and obstacles that can affect pedestrian mobility, with a special focus on people with impaired mobility. First, the system extracts tweets that match certain prede ned terms. Then, it obtains location information from them by using the location provided by Twitter when available, as well as searching the text of the tweet for locations. Finally, it applies natural language processing techniques to con rm that an actual event that affects mobility is reported and extract its properties (which urban element is affected and how). We also present some empirical results that validate the feasibility of our approach.
PB IEEE
SN 2169-3536
YR 2020
FD 2020-09-09
LK https://hdl.handle.net/10016/32667
UL https://hdl.handle.net/10016/32667
LA eng
NO This work was supported in part by the Analytics Using Sensor Data for FLATCity Project (Ministerio de Ciencia, innovación y Universidades/ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI), under Grant TIN2016-77158-C4-1-R, and in part by the European Regional Development Fund (ERDF).
DS e-Archivo
RD 27 jul. 2024