云计算通常是通过互联网来提供动态易扩展且经常是虚拟化的计算资源，并已经拓展到各个领域，尤其是交通海量数据处理。MapReduce并行编程模型是一种支持云计算算法设计的新框架，可利用网络中大量不同位置的计算机进行集群式海量数据处理。本文基于MapReduce构建一个新的计算框架，建立了基于子图分割的并行搜索方法，实现超大规模真实交通路网中最短路径搜索。案例分析证明：该方法能够在可接受的计算时间内提供高质量的最短路径搜索服务。

Cloud computing usually provides dynamically scalable and virtualized computing resources through the Internet, and has widely been used in various fields, especially mass traffic processing. The MapReduce parallel programming model is a novel framework that supports the design of cloud computing algorithms, and can invoke clustered computers at different locations to conduct huge data processing. This study proposed a new computing logic on the base of MapReduce, and developed a parallel searching algorithm with subgraph partitions to search the shortest path in large-scale real traffic networks. In tests of the field networks, the proposed method can provide high-quality shortest path searching service within acceptable calculation time.