In intelligent transportation systems (ITS), various detectors, such as the Sydney coordinated adaptive traffic system (SCATS), the global position system (GPS), and the microwave detector, are deployed in the urban road network. Traffic data detected from these detectors are generally heterogeneous. In this paper, the heterogeneous multi-sensor traffic data will be integrated and formally represented to provide a data resource to be effectively shared by various ITS applications. In this framework, the directive road segment is expressed as the basic element, and a three-dimensional spatio-temporal data domain is established to formalize the data representation. In the data domain, the heterogeneous traffic data are transformed to extract the spatio-temporal information as well as the traffic state features, such as speed, flux, and queuing time, therefore constituting a vector as the basic data element of the domain. Moreover, as an attachment to the data domain, a related sparse matrix is constructed to further demonstrate the relations among different directive road segments. As a case, the traffic data detected by SCATS and GPS are respectively transformed into flux of road segments, and the flux of next time is predicted by combining the related sparse matrix and the flux matrix of previous time.
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