Congestion in metropolitan areas is a persistent problem that has far-reaching consequences for things like travel time, gas mileage, and environmental quality. In many cases, conventional traffic management systems are too rigid and out of date to effectively address issues as they arise in real time.
Associate Professor Dr. Mohamad Nazim Jambli from the Faculty of Computer Science and Information Technology (FCSIT), UNIMAS has been at the forefront of this revolutionary methodology. He is the mastermind in the creation of a groundbreaking prototype research called DeepTrafficSense (DTS).
The Ministry of Higher Education (MoHE) has awarded his team a grant of RM74,000 in support of this ground-breaking research as part of the Prototype Development Research Grant Scheme (PRGS) Fund. The Internet of Things (IoT), artificial intelligence (AI), and deep learning are all brought together in Dr. Mohamad Nazim’s impressive research proposal. DTS will be built on the strategic combination of AI, advanced deep learning methods, and Internet of Things (IoT) devices.
In intelligent, sustainable cities, this convergence strives to reimagine the system for traffic congestion forecasting and management. Modern deep-learning algorithms are used in the DTS system to decipher the intricate and ever-changing nature of traffic patterns. By going above and beyond simple statistical analysis, these algorithms are able to respond to dynamic changes in traffic conditions in order to provide reliable, actionable forecasts.
“Understanding the current state of affairs is important, but the true value of DTS lies in predictive intervention. We aim to discern not just what is happening now, but also the future impact of specific interventions we might implement,” says Dr. Mohamad Nazim on the goals of DTS.
DTS’s long-term vision is to foresee a future in which urban places function with more fluidity and efficiency, going far beyond merely predicting traffic patterns.
Prepared by Ts. Hj Syahrul Nizam Junaini