Sim-Tree: indexing moving objects in large-scale parallel microscopic traffic simulation

Abstract

Performance is one of the major concerns in large-scale parallel microscopic traffic simulations. This paper focuses on one of the most time-costly data structures: the two-dimensional spatial index. A drawback of using popular two-dimensional tree-based spatial indexes (e.g. the R*-Tree) in large-scale microscopic traffic simulation is the heavy cost to rebalance the tree structure when a large number of vehicles frequently update their locations. This heavy location update cost also reduces the scalability of parallel microscopic traffic simulations. We observe that in real-world traffic systems the road density during a short period is stable, which is not sensitive to an individual vehicle's location. Thus, why not build a balanced tree structure based on the average road density in a road network? Motivated by this observation, this paper proposes Sim-Tree. The key feature of the Sim-Tree is that there is no need to check or rebalance its tree structure when individual vehicles frequently update their locations. In addition, a rebalance function and a bottom-up region query function are designed to optimize Sim-Tree's region query operations. The results of experiments simulating a city-scale traffic scenario on a 6-core machine show that the Sim-Tree is scalable and performs significantly better than the R*-tree family of spatial indexes.