A novel framework for road traffic data management for cloud-based storage

Abstract

With the tremendous growth of population and the consequent road traffic increase, the demand for optimized traffic data collection and management framework is demanding extensive research. The collection of traffic data using multiple sensors and other capture devices have been addressed in multiple researches deploying the mechanism using geodetically static sensor agents. However, the sheath factors for the parallel research outcomes have significantly ignored the fact of data replication control during processing. This work proposes a novel framework for capturing and storing traffic data. During the multi-node traffic data analysis, controlling the replication in order to reduce the cost has also been a challenge. Recent research outcomes demonstrate the use of agent-based sensor networks to accumulate road traffic data. However, a multipurpose framework for accumulating and managing the traffic data is still a demand. The outcomes of this research is also to consider the most effective cloud-based storage for the traffic data with the knowledge of most popular cloud-based storage service providers. The accumulation of the data is also followed by the predictive system for road traffic data analysis. Hence in this work we also explore the use of standard machine learning techniques to identify the most suitable technique with performance consideration. Also this work proposes a performance evaluation matrix for comparing the traffic frameworks.