CJAMmer - traffic JAM Cause Prediction using Boosted Trees

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2016

Autores

Matias, LM (Autor) Outra; Cerqueira, V (Autor) Outra

Ata de Conferência Internacional

Título: IEEE 19th International Conference on Intelligent Transportation Systems: Proceedings

Páginas: 743-748

ITSC 2016 - 19th IEEE International Conference on Intelligent Transportation Systems

Rio de Janeiro, 1 de novembro de 2016

Indexação

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00N-KHV

DOI: 10.1109/itsc.2016.7795637

Abstract (EN): A traffic incident is defined by an event which provokes a disruption on the normal (free) flow condition of any highway. Such incidents must be caused by a recurrent excessive demand or, in alternative, by a series of possible stochastic occurrences which may suddenly reduce the road capacity (e.g. car accidents, extreme weather changes). This paper proposes a novel binary supervised learning method to classify congestion predictions regarding their causes - CJAMmer. It leverages on heterogeneous and ubiquitous data sources - such as weather, flow counts and traffic incident event logs -To generalize decision models able to understand the road congestion nature. CJAMmer settles on boosted decision trees using the well-known C4.5, as well as a straightforward feature generation process. A real world experiment was used to compare this method against other state-of-The-Art classifiers. The results uncovered the high potential impact of this methodology on industrial scale traffic control systems. © 2016 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica