ABSTRACT: AI based Intelligent Traffic Light Control System using CNN – Traffic signals are essential to guarantee safe driving at road intersections. However, they disturb and reduce the traffic fluency due to the queue delay at each traffic flow. In this work, we introduce an Intelligent Traffic Light Controlling (ITLC) algorithm. This algorithm considers the real-time traffic characteristics of each traffic flow that intends to cross the road intersection of interest, whilst scheduling the time phases of each traffic light. The introduced algorithm aims at increasing traffic fluency by decreasing the waiting time of traveling vehicles at signalized road intersections. Moreover, it aims to increase the number of vehicles crossing the road intersection per second.
In modern life, we have to face many problems one of which is traffic congestion becoming more serious day after day. Traffic flow determination can play a principal role in gathering information about them. This data is used to establish censorious flow time periods such as the effect of large vehicles, specific parts on vehicular traffic flow and provide a factual record of traffic volume trends. This recorded information is also useful for processing better traffic in terms of the periodic time of traffic lights. There are many routes to count the number of vehicles passed at a particular time and can give judgment of traffic flow. Now a day’s camera-based systems are better choices for tracing the vehicle’s data. This project focuses on a firmware-based novel technique for vehicle detection. This approach detects the vehicles in the source image and applies an existing identifier for each of the vehicles. Later it classifies each vehicle on its vehicle-type group and counts them all individually. The developed approach was implemented in a firmware platform which results in better accuracy, high reliability, and fewer errors. Traffic lights play a very significant role in traffic control and regulation on a daily basis. Using python the density of the roads is determined and the microcontroller changes the duration of green light given for each road as per the output after image processing.
The main objective of this paper is to detect traffic light control correctly by using neural network techniques.
Scope of the Project
The main contributions of this project therefore are
- Data Analysis
- Dataset Preprocessing
- Training the Model
- Testing of Dataset
- 4 GB of RAM
- 500 GB of Hard disk
In this modern era as the population is increased rapidly the usage of vehicles has also increased tremendously. The cause of it is heavy traffic. In order to avoid this problem, it is better that we flow new communication methods such as image processing based intelligent traffic controlling and monitoring system using OpenCV. By using this method we can get the details about information about vehicles in particular junctions through internet access. This is more beneficial for emergency traveling.
In the future, we increased the performance of this process and be able to get more accuracy.
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