CJAMmer - traffic JAM Cause Prediction using Boosted Trees

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.