KOTA BHARU, KELANTAN – A new capstone project developed by Faculty of Data Science and Computing (FSDK), Universiti Malaysia Kelantan student Aaron Teah Zhi Jie and his team, under the guidance of their supervisor, Dr. Akmal Remli, is taking aim at the escalating traffic congestion plaguing Kota Bharu. Bringing together industry professionals and cutting-edge technologies, the initiative tackles one of the city’s most pressing issues, while also paving the way for more data-driven solutions and greater opportunities to solve real-world challenges in the state of Kelantan.

The project focuses on several key steps:
1. Collecting and Analyzing Data: Real-time traffic information is gathered from CCTV cameras from social media (Yu KH) at major intersections and examined through Exploratory Data Analysis (EDA) to pinpoint congestion hotspots.
2. Deep Learning Detection: The YOLO framework is employed for vehicle detection, enabling precise and rapid identification of traffic volumes.
3. Predictive Modeling: An LSTM model leverages historical data to forecast congestion patterns, offering valuable insights into future traffic peaks.
4. Web Application Deployment: Using the Flask platform, both the detection and prediction models are integrated into a user-friendly interface, providing real-time traffic monitoring and alerts.

This hands-on project not only aims to alleviate congestion during peak hours and holiday seasons but also represents a significant stride in urban traffic management. According to Dr. Akmal, the experience of applying advanced AI and machine learning in a real-world context has been both challenging and rewarding.

“This project underscores our commitment to developing impactful solutions for local communities,” said Aaron. “I’m proud to be using AI and emerging technologies to address everyday problems, improve the quality of life in Kota Bharu, and open doors for future data-driven initiatives in Kelantan.”

Resources:
CCTV Video sources: Yu KH
Aaron Teah Zhi Jie LinkedIn: View LinkedIn Profile