Abstract
This paper deals with an efficient parallel and distributed framework for intensive computation with A* algorithm based on MapReduce concept. The A* algorithm is one of the most popular graph traversal algorithm used in route guidance. It requires exponential time computation and very costly hardware to compute the shortest path on large-scale networks. Thus, it is necessary to reduce the time complexity while exploiting a low cost commodity hardwares. To cope with this situation, we propose a novel approach that reduces the A* algorithm into a set of Map and Reduce tasks for running the path computation on Hadoop MapReduce framework. An application on real road networks illustrates the feasibility and reliability of the proposed framework. The experiments performed on a 6-node Hadoop cluster proves that the proposed approach outperforms A* algorithm and achieves significant gain in terms of computation time.
Highlights
With the increasing size of road networks (4.4 billions of vertices and 6 billions of uploaded GPS points, according to OpenStreetMap data stats 2018 [1]), there has been vast improvement in hardware architecture for intelligent transportation system
The basic algorithms used for the Single Source Shortest Path Problem (SSSPP) are not suited for intensive computation in large-scale networks because of long latency time
We show by comparison that the proposed MapReduce framework of A* algorithm is more effective than the MapReduce framework of Dijkstra algorithm presented in [6, 7]
Summary
With the increasing size of road networks (4.4 billions of vertices and 6 billions of uploaded GPS points, according to OpenStreetMap data stats 2018 [1]), there has been vast improvement in hardware architecture for intelligent transportation system. The basic algorithms used for the Single Source Shortest Path Problem (SSSPP) are not suited for intensive computation in large-scale networks because of long latency time. This is one of the crucial problem of route-guidance systems for highway vehicles including the Vehicle Routing Problem (VPR), Traveling Salesman Problem (TSP) and Pickup and Delivery Problem (PDP). Hadoop and MapReduce According to Hadoop documentation [10], Hadoop is an Apache open source framework inspired by Google File System [28] It allows parallel processing on distributed data sets across a cluster of multiple nodes connected under a master-slaves architecture. While the slave nodes (DataNode) manage the storage of block files and periodically report the status to NameNode
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