GAM: A GPU-Accelerated Algorithm for MaxRS Queries in Road Networks

In smart phones, vehicles and wearable devices, GPS sensors are ubiquitous and collect a lot of valuable spatial data from the real world. Given a set of weighted points and a rectangle r in the space, a maximizing range sum (MaxRS) query is to find the position of r, so as to maximize the total weight of the points covered by r (i.e., the range sum). It has a wide spectrum of applications in spatial crowdsourcing, facility location and traffic monitoring. Most of the existing research focuses on the Euclidean space; however, in real life, the user's moving route is constrained by the road network, and the existing MaxRS query algorithms in the road network are inefficient. In this paper, we propose a novel GPU-accelerated algorithm, namely, GAM, to tackle MaxRS queries in road networks in two phases efficiently. In phase 1, we partition the entire road network into many small cells by a grid and theoretically prove the correctness of parallel query results by grid shifting, and then we propose an effective multi-grained pruning technique, by which the majority of cells can be pruned without further checking. In phase 2, we design a GPU-friendly storage structure, cell-based road network (CRN), and a two-level parallel framework to compute the final result in the remaining cells. Finally, we conduct extensive experiments on two real-world road networks, and the experimental results demonstrate that GAM is on average one order faster than state-of-the-art competitors, and the maximum speedup can achieve about 55 times.