Transportation cyber-physical systems are constrained by spatiality and real-time because of their high level of heterogeneity. Therefore, applications like traffic control generally manage moving objects in a single-machine multithreaded manner, whereas suffering from frequent locking operations. To address this problem and improve the throughput of moving object databases, we propose a GPU-accelerated indexing method, based on a grid data structure, combined with quad-trees. We count object movements and decide whether a particular node should be split or be merged on the GPU. In this case, bottlenecked nodes can be translated to quad-tree without interfering with the CPU. Hence, waiting time of other threads caused by locking operations raised by object data updating can be reduced. The method is simple while more adaptive to scenarios where the distribution of moving objects is skewed. It also avoids shortcomings of existing methods with performance bottleneck on the hot area or spending plenty of calculation resources on structure balancing. Experiments suggest that our method shows higher throughput and lower response time than the existing indexing methods. The advantage is even more significant under the skewed distribution of moving objects.

中文翻译：

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一种自适应的运输运动对象索引方法

由于高度的异构性，运输网络物理系统受到空间性和实时性的限制。因此，诸如流量控制之类的应用程序通常以单机多线程方式管理移动对象，而却遭受频繁的锁定操作。为了解决此问题并提高移动对象数据库的吞吐量，我们提出了一种基于网格数据结构并结合四叉树的GPU加速索引方法。我们计算对象的运动并决定是否应在GPU上拆分或合并特定节点。在这种情况下，瓶颈节点可以转换为四叉树，而不会干扰CPU。因此，可以减少由于对象数据更新引起的锁定操作而导致的其他线程的等待时间。该方法很简单，同时更适合于移动对象的分布倾斜的情况。它也避免了现有方法的缺点，即在热点区域出现性能瓶颈，或在结构平衡上花费大量计算资源。实验表明，与现有索引方法相比，我们的方法显示出更高的吞吐量和更低的响应时间。在移动物体的偏斜分布下，该优势更加明显。