Enriching Traffic Information with a Spatiotemporal Model based on Social Media

Autor(es)

Santos, Bruno P. ; Rettore, Paulo H. L. ; Ramos, Heitor S. ; Vieira, Luiz F. M. ; Loureiro, Antonio A. E. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2018 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC); v. N/A, p. 6-pg., 2018-01-01.

Resumo

In this work, we argue that Location-Based Social Media (LBSM) feeds may offer a new layer to improve traffic and transit comprehension. Initially, we showed the significant correlation between Twitter's feed and traditional traffic sensors. Then, we presented the Twitter MAPS (T-MAPS) a low-cost spatiotemporal model to improve the description of traffic conditions through tweets. T-MAPS enhance traditional traffic sensors by carrying the human lens into the transportation system. We conducted a case study by running T-MAPS and Google Maps route recommendation, in which, we showed T-MAPS viability, as an additional traffic descriptor. As a result, we noticed the median of route similarity reached 62%, and for a quarter of the evaluated trajectories, the similarity achieved between 75% and 100%. Also, we presented three route description services, based on natural language analyzes, Route Sentiment (RS), Route Information (RI), and Area' Tags (AT) aiming to enhance the route information. (AU)

• Processo FAPESP: 15/24494-8 - Comunicação e processamento de big data em nuvens e névoas computacionais
• Beneficiário: Nelson Luis Saldanha da Fonseca
• Modalidade de apoio: Auxílio à Pesquisa - Temático