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巨大的爆炸的城市交通数据,传统的智能交通系统体系结构已经大规模结构化数据处理不足,尤其是在“以人为本,服务民生”背景下,智能交通系统提出了更高的要求,因此应考虑系统的架构与数十亿级数据引擎的选择,综合日志数据检索的大碰撞,采矿和其他新技术;系统应该针对Map Reduse和火花的能力不同属性的数据优化计算模型,满足城市智能交通数据离线和实时应用的要求。随着“以人为本”的智能交通系统,为了缓解交通拥堵,优化交通、城市管理、服务贡献的人。
Due to the huge explosion of urban traffic data and the traditional intelligent transportation system architecture, large-scale structured data processing has not been adequately dealt with. In particular, in the context of “people-oriented, serving the people’s livelihood ”, intelligent transportation systems have set higher demands Consider the architecture of the system and the choice of billions of data engines, comprehensive collision data mining, mining and other new technologies; the system should be based on Map Reduse and Spark capabilities of different data optimization computing model to meet the urban intelligent traffic data Offline and real-time application requirements. With the “people-oriented” intelligent transportation system, in order to ease traffic congestion, traffic optimization, urban management, service contribution to people.