Self-Sustainable Intelligent Transportation System

Abstract

Intelligent Transportation Systems (ITS) today have a significant impact on community's well-being and satisfaction. It coordinates traffic movement and manages the capacity of highways and freeways by ultimately minimizing congestions and travel times. The amount of traffic data generated from these systems is increasing dramatically. This creates new challenges for data transmission, storage, and retrieval. Existing big-data solutions addresses such issues and provides real-time services of processing, storing and retrieving the data. Many technologies have emerged to make the best use of big-data solutions in combination with cloud computing technologies. Integrating these technologies within the ITS is a key objective of this research in addition to other objectives including maintaining secure transmission to preserve data integrity and to guarantee self-sustainability for autonomous error and failure recovery. The framework of the proposed module includes a multi-stage approach. The first stage is data acquirement from real-time sensors or monitoring devices such as traffic cameras. The second stage is to develop a pre-processing algorithm that process the acquired data and convert it to a proper format for cloud storage and transmission. The final stage is represented by cloud operations and services which include big-data analytics that ultimately delivers valuable information to the system which can manage or predict traffic congestions and queues. Inevitably, the system is put into testing stage to evaluate the results and how it conforms to the objectives of this research.