A mapreduce-based approach for shortest path problem in road networks

In the era of big data, using of data mining instead of data collection represents a new challenge for researchers and engineers. In the field of transportation, computing of the shortest path based on MapReduce using widely existing vehicle data is meaningful both in theory and practice. Therefore, this article proposes a simple shortest path approach to relieve urban traffic congestion. The objective is not to guarantee the optimality but to provide high-quality solutions in acceptable computational time. The proposed approach is based on partitioning of original graph into a set of subgraphs, and parallel solving of the shortest path for each subgraph in order to obtain a solution for the original graph. An iterative procedure is introduced to improve the accuracy. The experimental results show that proposed approach significantly reduces computational time.