The analyzing and processing of multisource real-time transportation data stream lay a foundation for the smart transportation's sensibility, interconnection, integration, and real-time decision making. Strong computing ability and valid mass data management mode provided by the cloud computing, is feasible for handling Skyline continuous query in the mass distributed uncertain transportation data stream. In this paper, we gave architecture of layered smart transportation about data processing, and we formalized the description about continuous query over smart transportation data Skyline. Besides, we proposed mMR-SUDS algorithm (Skyline query algorithm of uncertain transportation stream data based on micro-batchinMap Reduce) based on sliding window division and architecture. 1. Introduction Recently, tremendous changes have taken place in city transportation data sources, transportation data services, and information infrastructure. Traditional ITS (intelligent transport systems) present many defects in higher-dimensional space-time continuous data stream collected and passed back from mass perceptible and measurable sensor networks and the storage, processing, and analysis of big data. With the advent of computing technology such as Internet of things, cloud computing [1], and smarter transportation [2] has emerged, as a new concept of comprehensive transportation system. As shown in Figure 1, smarter transportation system covers various aspects of transportation and is a complex and comprehensive system consisting of plenty of subsystems. Analytical processing of multi-source and real-time transportation data stream [3] is the basis of realizing perceptible Smarter Transportation with interconnection integration and real-time decision. Besides, such analytical processing is critical to establishing global sustainable transportation surveillance, network optimization of dynamic transportation, automatic response to accidents, and integration of location-based transportation services. Figure 1: Smarter Transportation Information. With the rapid development of information technology, monitoring platform in various types of transportation information management collects complex mass transportation stream data including video information [4, 5] from cameras, monitoring information of sensors, positioning system information of vehicle, and so on. Hence, transportation stream data are provided with diverse sources, wide varieties, different forms, and typical data-intensive processing characteristics. For example, by December 28, 2012, there were 8842 fixed
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