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Urban life is becoming more crowded as a result of the rise in population, resulting in a great increase in the number of motor vehicles. Traffic congestion has emerged as a serious concern in the modern world (Carley & Christie, 2017). Traffic signals are considered the most effective means to control the flow of vehicles at a busy intersection. However, it becomes clear that these signals are ineffective and inadequately managing traffic when one lane has a disproportionately high volume of traffic compared to the other lanes (Bacon et al., 2011). We are employing intelligent traffic redirection technology to alleviate excessive traffic and road congestion. This would work by using sensors to weigh vehicles and relaying the data to a traffic light programmable logic controller (PLC). The traffic detour would be managed by the PLC (Srivastava et al., 2012).
The smart light system is made up of numerous software agents. It is strategically placed at each intersection to intelligently control its traffic lights using optimal green–red distribution from the server. The smart light system would also monitor any significant changes in traffic flow (Mishra et al., 2018). To avoid a single point of failure in our system, the agent is equipped with multiple cameras that cover the full downstream of an intersection. The cameras are monitored by a control center, as well as memory to record distributions. Using a control plan algorithm, we may derive traffic patterns from historical data and act upon them. The solution offered does not address algorithm selection. There are also traffic light-shaped actuators that regulate the flow of traffic (Seniman et al., 2020).
Because of intense traffic in multiple directions and variable fluctuations, traffic lights, like smartphones and other devices, must be updated. Thanks to cameras and wireless technology, we can now determine the number and timing of passing cars. Once this data has been collected in real time from the traffic flow, the issue is simplified to a sophisticated mathematical equation in which the ideal green-to-red signal ratio is determined. As with any optimization issue, the solution is logically straightforward: delegate authority to artificial intelligence (Triana et al., 2014).
In today’s world, traffic congestion is a huge issue due to the tremendous population expansion. Moreover, poor driving judgment and contempt for traffic laws frequently place individuals in life-threatening circumstances (Cohen, 2014). Every day, we waste tens of thousands of hours driving about and honking at other motorists. Our future generation will be gasping for a breath of fresh air due to the yearly escalation of this threat (Liang et al., 2020).
Congestion is the result of competition for a limited yet incredibly valuable resource. Using traffic signals, which are a reliable way of managing daily traffic at junctions, it is possible to manage traffic congestion effectively. Therefore, in this study, we focus largely on how traffic signals interpret real-time traffic data and suggest an AI-assisted runtime solution.
The key to this strategy is recommending a traffic signal that can detect high-traffic regions and highlight which lanes are busy at what times. The next step is to assess the facts and develop a schedule for applying intelligence that is both logical and feasible. Once we have a correct congestion schedule, we can include traffic signals. This communication can minimize traffic congestion. Imagine a signal located in the center of an intersection where four routes converge simultaneously. In order to alleviate congestion, we will therefore suggest a traffic signal that can react to the data and display red, yellow, and green lights accordingly (Das et al., 2021).
Intelligent traffic lights with wireless control of the signal lights would result in reduced traffic junction dangers, as well as selecting the shortest routes with the least amount of congestion. All of this is done to reduce the amount of time a rescue truck must convey a patient to a medical facility. Using wireless communication frameworks, information is delivered and received between terminals. GSM (Global System for Mobile Communications), which is widely employed, is a possibility (Munem et al., 2016). This is owing to the covered area’s dependability as well as its accessibility and mobility. The database is used to store data and generate various reports at the request of the manager.