Challenge background
Bangkok is notorious for its heavy traffic congestion, ranking among the most congested cities in the world. Rapid urbanization, high vehicle density, and unpredictable road incidents contribute to severe delays, economic losses, and increased pollution like PM2.5. The average commuter in Bangkok spends hours stuck in traffic, leading to inefficiencies in transportation and logistics.
The problem
Traditional traffic management systems rely on static data and road infrastructure, making them less effective in handling real-time congestion and sudden incidents like accidents or roadblocks. There is a growing need for AI-driven solutions that can dynamically predict travel times, detect incidents, and analyze affected area in real-time.
Goal of the project
- Uses real-time traffic data to predict estimated travel times.
- Detects accidents and determines affected areas.
- Suggests optimal routes to minimize congestion and delays.
- Provides a user-friendly mobile or web application for commuters.
Project timeline
- 1
Week 1
Gather real-time and historical traffic data from various sources (GPS data, Google Maps API, government traffic databases, social media, CCTV footage). Collect weather data to analyze its impact on traffic conditions. Set up data pipelines for continuous data collection and store in a cloud-based system.
- 2
Week 2
Clean and format raw data, handling missing values and inconsistencies. Normalize and standardize data for machine learning models. Extract key features (time of day, road type, accident reports, congestion levels) Annotate accident data using computer vision and NLP techniques.
- 3
Week 3
Perform statistical analysis to understand traffic patterns and congestion trends. Visualize data using charts, maps, and time-series plots to identify correlations. Analyze accident hotspots and their impact on traffic flow. Identify potential biases or inconsistencies in the dataset.
- 4
Week 4
Train machine learning models for travel time prediction (LSTM, GRU). Develop a computer vision model for accident detection using CCTV footage. Implement NLP techniques to extract traffic-related incidents from social media. Optimize models for accuracy, speed, and efficiency.
- 5
Week 5
Validate models using test datasets and real-time traffic data. Evaluate performance metrics (MAE, RMSE for travel time prediction, precision/recall for accident detection). Fine-tune models based on feedbacks and error analysis. Ensure scalability for real-time deployment.
- 6
Week 6
Develop a REST API to integrate machine learning models with fronted applications. Implement database storage for real-time traffic updates. Set up cloud services for scalable AI processing. Test API endpoints for efficiency and response time.
- 7
Week 7
Design and develop a user-friendly interface for the traffic prediction app. Implement interactive maps to show congestion, accidents, and route suggestions. Integrate real-time traffic and public transport options. Conduct usability testing to refine the user experience.
- 8
Week 8
Deploy the app and backed services on cloud platforms. Conduct final testing with live traffic data to ensure real-time functionality. Optimize system performance for scalability and real-world usage. Document project findings, prepare a final report, and present results.
What you'll learn
- Data Collection
- Data Cleaning
- Data Analysis
- Building Machine Learning Models
- Developing an API
- Build a real-world AI-powered App