TY - JOUR
T1 - Traffic congestion analysis based on a web‐gis and data mining of traffic events from twitter
AU - Salazar‐carrillo, Juan
AU - Torres‐ruiz, Miguel
AU - Davis, Clodoveu A.
AU - Quintero, Rolando
AU - Moreno‐ibarra, Marco
AU - Guzmán, Giovanni
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - Smart cities are characterized by the use of massive information and digital communication technologies as well as sensor networks where the Internet and smart data are the core. This paper proposes a methodology to geocode traffic‐related events that are collected from Twitter and how to use geocoded information to gather a training dataset, apply a Support Vector Machine method, and build a prediction model. This model produces spatiotemporal information regarding traffic congestions with a spatiotemporal analysis. Furthermore, a spatial distribution represented by heat maps is proposed to describe the traffic behavior of specific and sensed areas of Mexico City in a Web‐GIS application. This work demonstrates that social media are a good alternative that can be leveraged to gather collaboratively Volunteered Geographic Information for sensing the dynamic of a city in which citizens act as sensors.
AB - Smart cities are characterized by the use of massive information and digital communication technologies as well as sensor networks where the Internet and smart data are the core. This paper proposes a methodology to geocode traffic‐related events that are collected from Twitter and how to use geocoded information to gather a training dataset, apply a Support Vector Machine method, and build a prediction model. This model produces spatiotemporal information regarding traffic congestions with a spatiotemporal analysis. Furthermore, a spatial distribution represented by heat maps is proposed to describe the traffic behavior of specific and sensed areas of Mexico City in a Web‐GIS application. This work demonstrates that social media are a good alternative that can be leveraged to gather collaboratively Volunteered Geographic Information for sensing the dynamic of a city in which citizens act as sensors.
KW - Crowdsourcing
KW - Geographic information system
KW - Spatiotemporal analysis
KW - Support vector regression
KW - Twitter
KW - Volunteered geographic information
UR - http://www.scopus.com/inward/record.url?scp=85104510806&partnerID=8YFLogxK
U2 - 10.3390/s21092964
DO - 10.3390/s21092964
M3 - Artículo
C2 - 33922627
AN - SCOPUS:85104510806
SN - 1424-8220
VL - 21
JO - Sensors
JF - Sensors
IS - 9
M1 - 2964
ER -