The B+-tree-based method for nearest neighbour queries in traffic simulation systems. (2015)

• Record Type: Journal Article
• Authors: Song, Zhu; Zhou, Shijie; Luo, Jiaqing; Deng, Weiwei
• Is Part Of: International journal of high performance computing and networking. Volume 8:Number 2(2015)
• Page Start: 110
• Page End: 123
• Publication Date: 2015
• Subjects: nearest neighbour queries -- traffic simulation -- B#43-tree -- linked lists -- mathematical modelling -- random distribution -- vehicle distribution -- traffic planning -- system design -- optimisation -- traffic control -- neighbouring vehicles; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

Abstract

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.