The problem of identifying the k-shortest paths KSPs for short in a dynamic road network is essential to many location-based services. Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic conditions. Very often such services have to process numerous KSP queries over large road networks at the same time, thus there is a pressing need to identify distributed solutions for this problem. However, most existing approaches are designed to identify KSPs on a static graph in a sequential manner, restricting their scalability and applicability in a distributed setting. We therefore propose KSP-DG, a distributed algorithm for identifying k-shortest paths in a dynamic graph. It is based on partitioning the entire graph into smaller subgraphs, and reduces the problem of determining KSPs into the computation of partial KSPs in relevant subgraphs, which can execute in parallel on a cluster of servers. A distributed two-level index called DTLP is developed to facilitate the efficient identification of relevant subgraphs. A salient feature of DTLP is that it indexes a set of virtual paths that are insensitive to varying traffic conditions in an efficient and compact fashion, leading to very low maintenance cost in dynamic road networks. This is the first treatment of the problem of processing KSP queries over dynamic road networks. Extensive experiments conducted on real road networks confirm the superiority of our proposal over baseline methods.

翻译：在动态路网中识别k最短路径（简称KSP）问题对众多基于位置的服务至关重要。路网的动态性体现在其对应图中边的权重随时间不断变化，反映着实时演变的交通状况。这类服务通常需要同时处理大规模路网上的大量KSP查询，因此迫切需要寻求该问题的分布式解决方案。然而，现有方法大多采用顺序方式在静态图上识别KSP，这限制了其在分布式环境中的扩展性和适用性。为此，我们提出KSP-DG，一种面向动态图识别k最短路径的分布式算法。该算法通过将完整图划分为更小的子图，将求解KSP的问题转化为在相关子图中计算局部KSP的问题，并能在服务器集群上并行执行。我们开发了一种名为DTLP的分布式两级索引，用于高效识别相关子图。DTLP的显著特征在于，它能以高效紧凑的方式索引一组对交通状况变化不敏感的虚拟路径，从而在动态路网中实现极低的维护成本。这是首次针对动态路网上处理KSP查询问题提出的解决方案。在真实路网上进行的广泛实验证实了本方案相比基线方法的优越性。