Artificial Intelligence based Smart Traffic Enforcement and Management System in urban areas

Abstract

In the urban environment, traffic congestion has become a significant concern. Congestion negatively influences the economy, the environment, and the quality of life in general. Unfortunately, traditional traffic control systems fail to control traffic discipline due to inadequate human resource management and limited extension of current infrastructure, resulting in increased traffic congestion and road infractions. This paper aims to create an intelligent system dashboard to make judgments on its own, detect congested areas and actual congestion locations, and plan alternative routes. The system should collect all available data from different cities and create forecasts based on the previous year's data. The designing Artificial Intelligence traffic controllers in our proposal can adapt to current data from sensors to perform constant optimizations on the signal timing plan for intersections in a network to minimize traffic congestions by using real-time traffic data, which is the main issue in traffic flow control today. A new technology known as Radio Frequency Identification (RFID) has been introduced , which can be used in conjunction with the existing signalling system to provide real-time smart traffic control. Traffic congestion will be decreased as a result of the use of this innovative technology. In addition, bottlenecks and traffic violations will be spotted early, allowing for early preventative actions to be implemented, saving the motorist time and money. Long-term decision-making is aided by traffic monitoring, mainly when designing transportation plans and budgets. It also helps law enforcement agencies identify the different types of traffic and take appropriate precautions, such as installing security cameras and other control mechanisms.