Mining spatial-temporal correlation of sensory data for estimating traffic volumes on highways

Sensory data are often of low quality, for example, data are incomplete, ambiguous, or indirect, which has become the bottleneck of many data-driven applications. Two kinds of data which are handled in the paper for estimating traffic volumes on highways are no exception. In particular, the traffic volume data obtained from the loop detectors are accurate but sparse, and the mobile signaling data for estimating relative traffic volumes are wide in coverage and low in cost, but they are indirect and inaccurate. Keeping the characteristics of data in mind, the paper proposes a data fusion approach named Polaris which extends compressive sensing to estimate traffic volumes on highways. The Polaris analyzes the sparsity of the traffic volumes reported by detectors, mines the spatial-temporal correlations between the two kinds of data, and then gives the computational steps in the light of compressive sensing. Experiments are conducted on the large-scale real signaling data and the loop detector data. The experimental results show that the Polaris has the lowest estimation errors in comparison with several other methods. The corresponding Polaris system has been built and deployed in Fujian Province, China. It can obtain real-time traffic volumes on the highways with full coverage at a very low cost.