A Workflow to Detect Traffic Events Using Multiple Algorithms and Data Sources

Autor(es)

Pereira, Alexandra S. ; Braga Silva, Thais R. M. ; Silva, Fabricio A. ; Correia, Luiz H. A. ; Loureiro, Antonio A. F. ; IEEE COMP SOC

• Tipo de documento: Artigo Científico
• Fonte: 17TH ANNUAL INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (DCOSS 2021); v. N/A, p. 7-pg., 2021-01-01.

Resumo

An event can be defined as something that happens at a particular place and time. A traffic event is a specific event kind that occurs on roads and affects the users' mobility. Traffic events can be helpful in a variety of Intelligent Transportation System (ITS) applications, such as routing planning and emergency notifications. The detection of traffic events is not a trivial task, given the particularities of the environment and data availability. In this work, we propose a workflow that guides an ITS application designer on modeling how to detect events of interest, given the application's requirements and the available data characteristics. As part of the workflow, we propose a decision component that selects the most appropriate event extraction algorithm for a particular scenario. An instance of the proposed model using two social networks as data sources and four machine learning algorithms was implemented as a case study. The results reveal that it was possible to extract a significant part of the expected events, all of them with complete what, where, and when information. (AU)

• Processo FAPESP: 18/23064-8 - Mobilidade na computação urbana: caracterização, modelagem e aplicações (MOBILIS)
• Beneficiário: Antonio Alfredo Ferreira Loureiro
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• 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