Traffic congestion analysis based on a web‐gis and data mining of traffic events from twitter

Juan Salazar‐carrillo, Miguel Torres‐ruiz, Clodoveu A. Davis, Rolando Quintero, Marco Moreno‐ibarra, Giovanni Guzmán

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

5 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Número de artículo2964
PublicaciónSensors
Volumen21
N.º9
DOI
EstadoPublicada - 1 may 2021

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