Formalizing computational intensity of big traffic data understanding and analysis for parallel computing

Nowadays, traffic data can be collected from different types of sensors widely-deployed in urban districts. Big traffic data understanding and analysis in intelligent transportation systems (ITS) turns out to be an urgent requirement. This requirement leads to the computation-intensive and data-intensive problems in ITS, which can be innovatively resolved by using Cyber-Infrastructure (CI). A generic process for the solution contains four steps: (1) formalized data understanding and representation, (2) computational intensity transformation, (3) computing tasks creation, and (4) CI resources allocation. In this paper, we firstly propose a computational domain theory to formally represent heterogeneous big traffic data based on the data understanding, and then use data-centric and operation-centric transformation functions to evaluate the computational intensity of traffic data analysis in different aspects. Afterwards, the computational intensity is leveraged to decompose the domain into sub-domains by octree structure. All the sub-domains create computing tasks which are scheduled to CI resources for parallel computing. Based on the evaluation of overall computational intensity, an example of fusing Sydney Coordinated Adaptive Traffic System (SCATS) data and Global Positioning System (GPS) data for traffic state estimation is parallelized and executed on CI resources to test the accuracy of domain decomposition and the efficiency of parallelized implementation. The experimental results show that the ITS computational domain is decomposed into load-balanced sub-domains, therefore facilitating significant acceleration for parallelized big traffic data fusion.