Dubai’s Roads and Transport Authority has launched the Transport Data Analysis Laboratory inside the Enterprise Command and Control Centre to monitor, collect, and analyze transport data for strategic and operational decision support.

The lab integrates data from more than 35 sources across buses, metro, tram, marine, soft mobility, taxis, limousines, and private vehicles, automating over 150 operational performance indicators.

Its goal is to enable proactive traffic flow solutions, aid transport planning for major events and emergencies, and reinforce Dubai’s status as a smart city with leadership in sustainable mobility.

The system unifies operational monitoring in a single platform, enabling AI-driven predictive models and scenario analysis to boost efficiency and response times during events.

The AI-driven tool lets traffic managers fine-tune signal timings, allocate resources more efficiently, and coordinate inter-agency responses, especially during peak hours, incidents, and large events.

The lab automates more than 150 performance indicators to enhance planning, on-ground coordination, response speed, and policy evaluation.

This automation supports improved monitoring, faster responses, and evaluation of traffic policies.

The project includes collaboration with academic institutions and strategic partners to develop best analytical practices and forecasting models for traffic movement and demand across modes.

Using AI and predictive models, the lab helps authorities make faster, smarter decisions during major events and daily operations.

Beyond congestion relief, the laboratory supports road safety by identifying locations prone to recurring accidents and enabling preventive measures through coordination with relevant entities.

It identifies high-risk locations for recurring accidents to implement corrective safety measures in coordination with relevant entities, enhancing overall traffic safety.

The facility collects real-time data from more than 35 sources in coordination with operational centres and relevant departments.