This paper presents collection and analysis of heterogeneous urban traffic data, and integration of them through a kernel-based approach. The recent development in sensing and information technology opens up opportunities for researching the use of this vast amount of new urban traffic data. In this paper, the data fusion algorithm is developed by using a kernel based interpolation approach. Our objective is to reconstruct the underlying urban traffic pattern with fine spatial and temporal granularity through processing and integrating data from different sources. The fusion algorithm can work with data collected in different space time resolution, with different level of accuracy, and from different kinds of sensors. The properties and performance of the fusion algorithm is evaluated by using a virtual test-bed produced by VISSIM microscopic simulation. The methodology is demonstrated through a real-world application in Central London. This paper contributes to analysis and management of urban transport facilities
