Cloud - based Adaptive Traffic Signal System using Amazon AWS

In today's urban landscapes, efficient traffic management is crucial for ensuring smooth vehicular movement and reducing congestion. Providentially, advancements in Deep learning has the potential to address this problem with various methods to implement adaptive traffic system. However, such solutions require large amounts of infrastructural expenditure to establish the connectivity of all intersections of a large road network. Although, such enormous investments have given only a few preliminary successes. In this paper, a cloud-based adaptive traffic control and management system that utilizes a set of Cloud services offered by a very well-known Cloud provider Amazon Web Services (AWS) such as Amazon Kinesis, a serverless flowing data service which streamlines the process of capturing, data processing and storing data streams, Amazon Rekognition, a high-powered video analysis service, for real-time detection and counting, Amazon Lambda and other services is proposed. This system can detect and count vehicles with more accuracy and optimizes the signal time by a custom-made algorithm to reduce overall travel time. This proposed system also helps emergency vehicles from being impassable by other vehicles during stop phase, reduce vehicular emission at intersections with minimal cost of setup.