Parking recommender system using q-learning and cloud computing

Abstract

Artificial Intelligence (AI) based recommender systems help to make our life easy and comfortable. From simple chatbot to YouTube recommendation, AI is used to recommend news, videos, etc. which provide us more information and saves our time. In big cities, parking seems to be a major problem where commuters need to find a suitable parking space among many parking areas which cause wastage of time and fuel. Our paper proposes a parking recommender system where commuters will be suggested a parking area to a nearby place for helping them to save time, parking cost and ensure high security. To collect data of parking spaces, we propose a Cloud architecture where we use the concept of Edge and Cloud computing to collect and process data smoothly and reduce latency. To deal with bigger amounts of data we use Data Streaming Pipelining to process and analyze those data. We use Amazon Web Services (AWS) to implement our proposed Cloud architecture. For creating the AI based recommender system, we propose the Q-learning algorithm with ε-soft policy to suggest nearby parking areas. Our novel approach will be helpful for both local and global citizens to find an ideal parking area close to their working place, home, etc. Our proposed Cloud architecture is able to reduce latency and make data transferring system faster. Also the Q-learning algorithm can outperform in terms of both certain and uncertain situations.