About Our Project

Traffic Control Optimization Challenge for 2045
Overview: This project aims to address the challenges of heavy urban traffic in the year 2045 by leveraging advanced image analysis techniques and deep learning. Traditional traffic management systems are increasingly ineffective due to the growing volume and complexity of urban traffic. In response, innovative solutions such as using street images for intelligent traffic management are being explored.

Objectives:

• Vehicle Detection:
  • Utilize image analysis to accurately detect the number and speed of vehicles, as well as traffic density.
• Optimize Traffic Light Scheduling:
  • Provide optimal scheduling for traffic signals based on image analysis results to improve traffic flow.

Steps Involved:

1. Image Analysis and Processing:
   • The AI model identifies the number of vehicles, traffic density, and speed from images.
2. Traffic Light Scheduling Optimization:
   • Using analysis results, the model suggests optimal green and red light timings for smoother traffic flow.

Inputs and Outputs:

• Inputs:
  • Images of streets and intersections displaying vehicles and traffic conditions.
• Outputs:
  • Recommended durations for green and red traffic lights.
  • Prioritization of roads based on heavier traffic or longer wait times.

Solution Implementation:

We developed a web application using the Flask framework, enabling users to analyze street images to enhance traffic flow. Below is an outline of the application’s workflow:

Program Workflow:

• Initial Setup and File Upload Path:
  • The application, built with Flask, includes CORS for cross-origin request support.
  • A directory (static/uploads/) is set up to store uploaded files, supporting formats like PNG, JPG, JPEG, and GIF.
• File Format Validation:
  • The allowed_file(filename) function ensures only supported file formats are uploaded.
• Image Upload and Analysis Process:
  • Users select a file to upload via the main route.
  • Once validated and secured, the file is stored in the upload directory.
  • The file is processed by a traffic analysis model using the analyze_traffic_with_visualization function.
• Image Analysis and Result Presentation:
  • The model estimates vehicle count, distribution, optimal green light timings, and crossing times.
  • Results are displayed alongside original and processed images on a webpage (likely result.html).
• User Interaction:
  • Flash messages provide feedback for invalid inputs and requests.
  • Web pages (index.html and result.html) facilitate user engagement.

Strengths:

• Flask Framework: Utilized for its lightweight and flexible properties, allowing rapid web development.
• Direct Image Processing: Users can upload and process images directly.
• Traffic Feature Estimation: Tools are employed to provide actionable optimization suggestions based on image analysis.

Conclusion:

This system serves as an effective traffic management solution and lays the groundwork for future advancements in urban traffic control systems.