Transport Optimisation
Transport Optimisation
Multi-Agent Graph Neural Network Framework for Traffic Signal Control
The traffic signal control optimisation aims to reduce congestion, improve traffic flow, and enhance safety for both motorists and pedestrians. Ineffective signal management leads to longer delays at junctions, higher fuel consumption, and increased emissions, exacerbating environmental challenges and creating significant economic costs. In urban areas, such inefficiencies also diminish quality of life, contributing to elevated noise levels and poorer air quality.
Intractābilis has developed an advanced traffic signal optimisation protocol that employs multiple intelligent agents operating in a shared environment. In fully cooperative settings, these agents pursue shared objectives by interacting with both their surroundings and one another, refining their actions in response to real-time feedback. Our system collects essential information from a central server at every time step, processes network-wide traffic data, and enables the application of sophisticated graph neural networks. Within this framework, agents adapt dynamically to evolving traffic patterns, either by working collectively towards a single objective or by maximising their own performance while maintaining system-wide efficiency.
With its modular, extensible architecture, the solution provides a robust foundation for consistent, reproducible, and scalable traffic signal control. This represents a significant step towards more intelligent and adaptive traffic management, with direct benefits for sustainability, operational efficiency, and urban mobility.
Hybrid Quantum-Classical Architectures for Intelligent Transportation Systems
Connected vehicle infrastructure is reshaping urban mobility by enabling seamless interaction between vehicles and road systems. This capability enhances traffic efficiency, improves safety, and contributes to a reduction in environmental impacts through optimised traffic flow and reduced congestion. The development of fully autonomous vehicles is advancing in parallel, supported by sophisticated driver assistance technologies. These systems depend heavily on advanced visual processing to navigate complex environments and accurately detect obstacles.
Intractābilis has pioneered an innovative classical–quantum hybrid framework designed to support the creation of intelligent transportation systems. A key feature of this approach is the accurate classification of traffic light images into red, green, or yellow categories, a capability that is vital for improving real-time traffic management decisions. By applying a state-of-the-art Quantum Neural Network (QNN) framework, this solution establishes a strong foundation for quantum-enabled image classification, opening the door to next-generation traffic control solutions.
Our transportation solutions are purpose-built for the era of autonomous vehicles, connected infrastructure, and data-driven mobility planning.
From securing cloud-edge orchestration in 6G fleets to enabling week-long traffic foresight and optimising city-wide signal coordination, we deliver platforms that are as resilient as they are intelligent. Our solutions operate at the edge, in the cloud, and across simulation environments—bridging today’s limitations and tomorrow’s demands.
Whether enabling national-scale smart mobility rollouts or optimising performance for private fleet operators, we provide the intelligence backbone for transportation systems that are secure, adaptive, and future-ready.
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