Intelligent Transport System
The system architecture and models are developed by a team from Monash University, lead by Prof Dong Ngoduy with support from Dr Takao Dantsuji, Mr Tri Le and Prof Hai Vu
Project items
- Equip signal cabinets with both connectivity and central operation.
- Traffic simulation and emulation create digital traffic twins
- Transport network coordination reduces traffic congestion using network control algorithms
Traffic simulation and emulation
Traffic Simulation and Emulation Project belongs to the category of construction and installation of traffic monitoring system with requirements:
- Using monitoring technology solutions are sensors that can monitor multiple vehicles at the same time, transmitting data quickly with high accuracy.
- Using machine learning technology, artificial intelligence combined with digital twin models in simulating and emulating data
- Proposing traffic coordination scenarios to reduce congestion
RADAR monitoring technology solutions
Using specialized Radar technology for traffic monitoring:
-High resolution, allowing to measure all means of transport from a distance of > 140m
-Can operate in all times and weather conditions:
-Optimized in terms of data and using 4G wireless network for transmission
Philippines Manual counting
Ho Chi Minh City Automatic monitoring
Project implementation in the trunk road area
- The solution to equip sensors is on existing traffic lights, lighting or cameras.
Sensors mounted on traffic lights
Sensor mounted on Longmen rack
Sensor mounted on lighting pole
Device management system
Device management system allows administrators to manage and control devices on the system: intuitively and continuously
Device management system and data collected from sensors
Point-based sensor count data (MCT 1.1)
Real-time correlation chart of volume and speed across nodes
Traffic simulation and emulation
Digital Twin Solution
Real-world data is stored in a “data lake” system and continuously simulated through a vehicle flow model with algorithms from machine learning and artificial intelligence.
Collected data is simulated on the Center’s software system.
