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The B~+-tree-based method for nearest neighbour queries in traffic simulation systems

机译:交通仿真系统中基于B〜+树的最近邻居查询方法

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摘要

Extensive used traffic simulation systems are helpful in planning and controlling the traffic system. In traffic simulation systems, the state of each vehicle is affected by that of nearby vehicles, called neighbours. Nearest neighbour (NN) queries, which are multi one-dimensional and highly concurrent, largely determine the performance of traffic simulation systems. Majority of existing traffic simulation systems use linked list-based methods to process NN queries. Although they are simple and effective, existing methods are neither scalable nor efficient. In this paper, we propose a B~+-tree-based method to improve the efficiency of NN queries by borrowing ideas from methods used in databases. In particular, we create a linked local B~+-tree, called LLB~+-tree, which is a variation of original B~+-tree, to maintain the index of neighbours of each vehicle. We also build a mathematical model to optimise the parameter setting of LLB~+-tree according to multiple parameters for lanes and vehicles. Our theoretical analysis shows that the time complexity of the method is O(logN) under the assumption of random distribution of vehicles. Our simulation results show that LLB~+-tree can outperform linked list and original B~+-tree by 64.2% and 12.8%, respectively.
机译:广泛使用的交通模拟系统有助于规划和控制交通系统。在交通模拟系统中,每辆车的状态都会受到附近车辆(称为邻居)的状态的影响。多维一维且高度并发的最近邻居(NN)查询在很大程度上决定了流量仿真系统的性能。现有的大多数交通仿真系统都使用基于链接列表的方法来处理NN查询。尽管它们简单有效,但是现有方法既无可扩展性,又无效率。在本文中,我们提出了一种基于B〜+树的方法,通过借鉴数据库中使用的方法来提高NN查询的效率。特别是,我们创建了一个链接的本地B〜+树,称为LLB〜+树,它是原始B〜+树的变体,以维护每辆车的邻居索引。我们还建立了数学模型,根据车道和车辆的多个参数来优化LLB〜+ tree的参数设置。我们的理论分析表明,在车辆随机分配的假设下,该方法的时间复杂度为O(logN)。仿真结果表明,LLB〜+树的性能比链表和原始B〜+树分别高64.2%和12.8%。

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