Big Data Analytics architecture for intelligent transportation systems: A tunisian case study

In recent years, the explosion of a large, complex and various amount of data in the transportation field required the implementation of advanced technologies. However, a numerous Intelligent Transportation System (ITS) technologies have been deployed. Data generated in ITS can be stored, managed, analysed and processed using Big Data analytics. The main contributions of this paper are first to design an architecture to deal with big data analytics in ITS and then to implement a parallel Genetic Algorithm(GA)-MapReduce (MR) as an optimization technique in order to minimize the travel distance. The purpose of this parallelism is to improve the population diversity using the parallel processing of the MapReduce and the exploration of the search space using GA in the case of Vehicle routing problem. To test the performance of the proposed model, our analysis is conducted from data collected from sensors located on the highways of Tunisia.