Smart Mobility
Traffic Optimization in London
Using real-time sensor data and reinforcement learning to reduce congestion by 39% during peak hours.
12 minute read
Read Analysis
How urban researchers leverage data science to create safer, more efficient, and sustainable metropolitan environments.
From IoT sensors to citizen surveys, capturing 12+ data streams in real time
Machine learning models predicting urban challenges with 87% accuracy
Automating policy recommendations with 386+ decision variables
Using real-time sensor data and reinforcement learning to reduce congestion by 39% during peak hours.
Predictive models reduced operational costs by 28% while improving recycling rates in Tokyo districts.
High-volume processing of 38 data types across 14,000+ connected devices
Distributed GPU clusters training 46 different predictive models nightly
Reinforcement learning framework optimizing 750+ daily operational decisions
Pilot projects testing 512-qubit processors for complex scenario modeling by 2030
Next-generation systems incorporating 1,000+ real-time citizen feedback streams
Self-improving AI systems managing 78% of municipal operations by 2030
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