Ppt for sih presentation 2024 topic traffic management

Here's the updated version with radio and sound sensors included:

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**Sensors and Data Collection:**

We utilize cameras, along with radio and sound sensors, strategically placed to monitor traffic at key intersections. The data collected from these sensors is continuously sent to our backend server using WebSockets, which ensures real-time, bidirectional communication.

**Data Transmission Workflow:**

1. **Sensors to Local Computer:** The cameras, along with the radio and sound sensors, feed live data to a local computer. This local computer acts as an intermediary, handling initial processing and ensuring stable communication.

2. **Local Computer to Backend Server:** From the local computer, the processed data is transmitted to our backend server for deeper analysis.

**AI-Powered Backend Processing:**

- **YOLOv5 Model:** Once the data reaches our backend, it is first decoded and passed through the YOLOv5 model. This model is responsible for identifying and classifying various traffic scenarios, such as the number of vehicles, types of vehicles, and any potential traffic violations.

- **Deep Q-Learning with CNN:** The identified data is then processed by our Deep Q-Learning model, integrated with a Convolutional Neural Network (CNN). This AI model analyzes the traffic scenarios to determine the optimal traffic signal pattern.

**User Interface and Decision Making:**

The decisions made by our AI model are visualized on a user-friendly web interface. This interface is designed for traffic officers, displaying real-time traffic conditions and recommended signal patterns in a clear, picture format. The traffic officer can then review these recommendations and make the final decision. Upon approval, our AI model sends the necessary instructions back to the local computer, which then controls the traffic signals accordingly.

**Data Storage and Future Reference:**

All the data processed, including traffic conditions, AI decisions, and officer inputs, is stored in our MongoDB database. This allows us to maintain a comprehensive record for future analysis, reporting, and system improvements.

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This version includes the additional radio and sound sensors, enhancing the system's ability to monitor and analyze traffic conditions.

1. TITLE PAGE SMART INDIA HACKATHON 2024 Problem Statement ID – SIH1607 Problem Statement Title – Smart Ai Based Traffic Management System Theme - smart automation PS Category- Software Team ID- Team Name (Registered on portal) : AIKYAM

2. IDEA TITLE Solution Explanation The technology has transcended all barriers it has now become easy to solve most human problems and one of these problems include traffic congestion. Traffic congestion has increased drastically over the years and has had negative impacts One of the many causes of traffic congestion is improper traffic management systems. A number of papers have been published with an aim to overcome the disadvantages of the traditional traffic light system. Innovation & Uniqueness The main purpose of the smart traffic managements system is to allot timings to a traffic signal based on the level of traffic on a lane. By image processing we can find the level of traffic in the area. Based on the vehicles present in a section the level of traffic is classified as low, medium and high 2 @SIH Idea submission- Template TEAM AIKYAM “A smart AI based solution for traffic management on routes with heavy traffic from different directions, with real-time monitoring and adaptation of traffic light timings”

3. TECHNICAL APPROACH 3 @SIH Idea submission- Template TEAM AIKYAM At start by vehicle detection traffic is analysed If traffic is low system switch to the predetermined algorithm. If traffic is high, Traffic officer is present :- The suggestions are provided to the officer, these suggestions are approved or rejected by the officer. The rejected decisions are considered and stored in the AI learning. Traffic Officer is not present :- The AI automation technique is activated for traffic management as per stored information for specific situation. Technologies and Tools Backend : Python with Django Frontend : JavaScript with React Database : MongoDB

4. FEASIBILITY AND VIABILITY Feasibility of the Idea: AI traffic management systems are increasingly feasible and practical due to advancements in technology Potential & Challenges: AI traffic management system offer many benefits but they also face several challenges and risks of Data Privacy and Security, System Reliability, Initial cost, etc. Strategies to overcome the above challenges are: For data privacy and Security we should implement strong encryption protocols & we should maintain transparency. For system reliability regular maintenance and testing is necessary. Maintain detailed and accessible documents for system operations and troubleshooting. Re gular audit AI system for biases and correct any unfair outcomes. 4 @SIH Idea submission- Template TEAM AIKYAM

5. IMPACT AND BENEFITS Potential impact on the target audience: Traffic management services are essential for ensuring the safety, efficiency and sustainability of road networks Benefits of the solution : Improved traffic flow Enhanced safety Efficient use of resources Real time monitoring and reporting Enhanced public satisfaction 5 @SIH Idea submission- Template TEAM AIKYAM

6. @SIH Idea submission- Template 6 @SIH Idea submission- Template 6 RESEARCH AND REFRENCES