To facilitate traffic understanding

planning and management optimization

we present a new spectral clustering method(TSSC) for big trajectory data mining based on latent semantic correlation.First

a matrix model is proposed to represent vehicle trajectories and the underlying road network with a grid-vehicle matrix

which is then transformed to a low-dimensional semantic subspace with random projection.Second

through matrix decomposition we extract hidden characteristics of the mass trajectory data and construct a similarity matrix for road network cells.Third

we adopt and implement a fast spectral clustering method to discover road network clusters based on the similarity matrix in the semantic space.Finally

we evaluate our approach with a large trajectory data set collected by the Fujian Communications Department

which has 19

719 vehicles and a total mileage of more than 14 million kilometers.Experiment results show that the approach can efficiently cluster the road network with traffic context semantic information derived from massive trajectory data.The approach is capable to discover inherent characteristics of complex road networks and provide insights for traffic planning and management optimization.