Traffic management is crucial for preserving road capacity, enhancing safety, and reducing congestion in urban areas like Jakarta, Indonesia. This project aims to leverage advanced technologies and data-driven solutions to address the challenges associated with traffic management in the city.
Jakarta faces several traffic-related issues, including congestion, accidents, and road defects. These problems lead to inefficiencies, safety hazards, and increased travel times for residents. The primary problems to tackle are:
1. Vehicle speed and category classification to enforce speed limits.
2. Traffic density classification for efficient traffic redirection.
3. Pothole detection for road maintenance and safety.
Project Setup and Data Collection:
Set up the project repository and gather relevant datasets.
Data Preprocessing and Model Planning:
Preprocess the data and plan the machine learning models.
Vehicle Category Classification Model:
Develop and train the vehicle category classification model.
Traffic Density Classification Model:
Create the traffic density classification model.
Pothole Detection Model:
Implement the pothole object detection model.
Web Application Development:
Design and develop a user-friendly web application.
Testing, Documentation, and Future Planning:
Conduct testing, document the project, and plan future developments.
– Improved understanding of deep learning model development and evaluation.
– Proficiency in using PyTorch, OpenCV, and other relevant libraries.
– Experience in creating user-friendly web applications.
– Knowledge of traffic management challenges and solutions.
– Collaboration and project management skills through teamwork on a complex project.
Open source resources: