CJAMmer - traffic JAM Cause Prediction using Boosted Trees

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Matias, LM (Author) Other; Cerqueira, V (Author) Other

Conference proceedings International

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

Pages: 743-748

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

Rio de Janeiro, 1 de novembro de 2016

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific