The challenges of modern cities in terms of traffic congestion and road safety are many. Traditional traffic management systems usually respond quite slowly to real-time conditions. As a result, this means inefficiency and longer travel time. Because of the same, this project objective will be directed at the design of AI-based solutions for dynamic management and optimization of traffic flow.
This system will collate data in real-time from various sources to drive informed decisions toward mitigating this congestion and the safety of road travel. It is a contribution that could very well help in assuring smarter, more efficient urban transportation networks.
Week 1-2: Initial Planning and Requirement Analysis
Definition of the project's objectives, scope, and high-level requirements
Collection of required data, and resources Week 3-4: Data Collection and Preprocessing Phase
Collection and preprocessing of real-time traffic data,
Extraction of features and cleaning of data.
Week 5-6: Model Development Phase
Developing the machine learning models developed for traffic prediction and management, and their subsequent training.
The accuracy and performance of the models will be tested against test data.
Week 7-8: System Implementation Phase
Implementation of an AI-based traffic management system.
Integrate the model into a user-friendly interface.
Test and improve the system based on performance metrics and user feedback.
Week 9-10: System Integration and Testing Phase
Do full-scale tests of the system, ensuring the management of the traffic is perfect.
Observe the performance of the system and user’s activities.
Make continuous improvement with the help of real analytics and user feedback.
Week 11-12: Final Evaluation and Reporting Phase
Final evaluation and validation of the traffic management system in compliance with the initial Scope of Work.
Compilation of the final project report together with documentation.
Presentation of individual reports by students.
