- Congestion in traffic is a serious issue.
- In existing system signal timings are fixed and they are independent of traffic density, Large red light delays leads to traffic congestion.
- In this project, Video based traffic estimation system is implemented in which signal timings are updated based on the vehicle counting.
- This system consists of Deep Learning module it detect the vehicle count of the current system and sends to the traffic signal.
- Based on traffic density of Vehicle System will Predict the Traffic Congestion.
- Eliminate of frequent traffic disconnect of Road network.
- Act as Road side Unit as server.
- Implementation of Ambulance Priority system.
- Almost all urban cities in the world use traffic light signals to control the traffic on the roads.
- Different types of traffic light control systems are developed which are vehicle actuated lights and static traffic lights.
- But their traffic lights timing are fixed and switching patterns are also predefined in the system it is independent of traffic conditions for the different lanes and they are not changing with real time data.
- In existing system human and Automatic based traffic light Control system system only mostly in use.
- Also some of the sensor based Traffic monitoring also used
- These techniques are having huge drawbacks.
- That human based system need huge manpower in the form of traffic Police.
- Less accuracy of processing sensor signals
- Prone to Sensor and Man Made errors which leads to system efficiency drop.
- This system proposes a new system for predicting the traffic Density by image processing using AI module.
- A camera will be installed alongside the traffic light.
- It will capture image sequences.
- The image sequence will then be analyzed using digital image processing for vehicle detection, and according to traffic conditions on the road, traffic Density can be Estimated.
- This system employs YOLOv5 and AlexnetNet V3 Convolutional neural network pre-trained model to accurately detect the number of vehicles present on the road, Average Vehicle Area and identify emergency vehicles in real-time.
- Using this information, this system can dynamically adjust traffic signals and reroute vehicles to minimize congestion and ensure priority access for emergency vehicles.
- The process is in real time so in can able to detect from the video stream from live Camera.
- The implementation of this approach runs at 30-40 frames per second, so that it can detect the vehicle very quickly.
- It uses low power processor with 2.4 GHz by using that we can able to achieve low power operation.
- Front End – Anaconda IDE
- Backend – SQL
- Language – Python 3.8
- •Hard Disk: Greater than 500 GB
- •RAM: Greater than 4 GB
- •Processor: I3 and Above
- * Base Paper
- * Complete Source Code
- * Complete Documentation
- * Complete Presentation Slides
- * Flow Diagram
- * Database File
- * Screenshots
- * Execution Procedure
- * Readme File
- * Addons
- * Video Tutorials
- * Supporting Softwares
- * 24/7 Support * Ticketing System
- * Voice Conference
- * Video On Demand
- * Remote Connectivity
- * Code Customization
- * Document Customization
- * Live Chat Support
Intelligent traffic management system, Intelligent traffic management system project, Smart traffic management system using IoT, Traffic Management System using Machine Learning Algorithm, Smart Traffic Management System using Deep Learning, AI-based traffic-management system, density based traffic management system, Density based traffic light control system project report, smart traffic management system, Machine learning Projects 2023 2024