Towards a Fog-Enabled Intelligent Transportation System to Reduce Traffic Jam

Abstract

Frustrations, monetary losses, lost time, high fuel consumption and CO 2 emissions are some of the problems caused by traffic jams in urban centers. In an attempt to solve this problem, this article proposes a traffic service to control congestion, named FOXS–Fast Offset Xpath Service. FOXS aims to reduce the problems generated by a traffic jam in a distributed way through roads classification and the suggestion of new routes to vehicles. Unlike the related works, FOXS is modeled using the Fog computing paradigm. Therefore, it is possible to take advantage of the inherent aspects of this paradigm, such as low latency, processing load balancing, scalability, geographical correlation and the reduction of bandwidth usage. In order to validate FOXS, our performance evaluation considers two realistic urban scenarios with different characteristics. When compared with related works, FOXS shows a reduction in stop time by up to 70%, the CO 2 emissions by up to 29% and, the planning time index by up to 49%. When considering communication evaluation metrics, FOXS reaches a better result than other solutions on the packet collisions metric (up to 11.5%) and on the application delay metric (up to 30%).