  Critical incident stages identification and reasonable prediction of traffic
incident duration are essential in traffic incident management. In this paper,
we propose a traffic incident duration prediction model that simultaneously
predicts the impact of the traffic incidents and identifies the critical groups
of temporal features via a multi-task learning framework. First, we formulate a
sparsity optimization problem that extracts low-level temporal features based
on traffic speed readings and then generalizes higher level features as phases
of traffic incidents. Second, we propose novel constraints on feature
similarity exploiting prior knowledge about the spatial connectivity of the
road network to predict the incident duration. The proposed problem is
challenging to solve due to the orthogonality constraints, non-convexity
objective, and non-smoothness penalties. We develop an algorithm based on the
alternating direction method of multipliers (ADMM) framework to solve the
proposed formulation. Extensive experiments and comparisons to other models on
real-world traffic data and traffic incident records justify the efficacy of
our model.
