Experience the future of mobility at Universiti Teknologi Malaysia. Powered by Agent-Based Modeling and GIS real-world data.
ABM is a computational model that simulates the actions and interactions of autonomous agents (vehicles) to assess their effects on the system as a whole.
We utilize real-world spatial data of the UTM Johor Bahru campus, mapping every road, intersection, and building with high precision using OpenStreetMap data.
Select a module to launch the simulation in your browser.
Each module focuses on different aspects of campus traffic analysis.
This module simulates adherence to Malaysian traffic regulations within the campus environment. It focuses on critical behaviors such as lane discipline, stopping at intersections, and right-of-way logic.
This simulation provides a focused view on real-time vehicle flow rates across the network. Users can adjust density sliders to observe how quickly main arteries clog up during peak hours.
This module performs high-density stress testing to evaluate parking availability and road capacity. It analyzes the delicate equilibrium between parked cars and active vehicles under stress.
This feature utilizes the "Jam Meter" algorithm to identify traffic bottlenecks visually. It highlights areas where average speeds drop below 5km/h, signaling infrastructure failures.
This is the most advanced iteration of the system simulation. It combines multiple metrics including Jam Percentage and density adjustments to provide long-term flow predictions.
Translating complex simulation data into actionable strategies for a greener, safer, and smarter university campus.
Optimization of traffic signals and reduction of idle time at intersections directly correlates to lower vehicle emissions across campus zones.
Every single road segment, junction, and parking lot within UTM is mapped, digitized, and simulated for total administrative visibility.
Identifying accident-prone hotspots before they happen. Our logic proactively suggests infrastructure changes to enhance pedestrian safety.