  Motivated by the need for accurate traffic flow prediction in transportation
management, we propose a functional data method to analyze traffic flow
patterns and predict future traffic flow. In this study we approach the problem
by sampling traffic flow trajectories from a mixture of stochastic processes.
The proposed functional mixture prediction approach combines functional
prediction with probabilistic functional classification to take distinct
traffic flow patterns into account. The probabilistic classification procedure,
which incorporates functional clustering and discrimination, hinges on subspace
projection. The proposed methods not only assist in predicting traffic flow
trajectories, but also identify distinct patterns in daily traffic flow of
typical temporal trends and variabilities. The proposed methodology is widely
applicable in analysis and prediction of longitudinally recorded functional
data.
