Fully automated requiring no human intervention

The largest cost savings comes by reducing the maintenance need of systems in place. Unlike traditional models, AI models not only require no recalibration, and maintenance and operate without intervention and guidance but they get better with time.

Purely data driven operating with minimal datasets

Data is essential to the development and operation of AI technologies that help reduce the uncertainty for decision makers. This data is becoming increasingly more available. Mobile-derived traffic information service quality has improved significantly in recent years. 
 Research on data fusion has also established that fusion of floating vehicle data with detector data can produce a more accurate estimate of traffic state than use of detector data alone, and accurate knowledge of current traffic state is an important prerequisite for accurate prediction of future traffic state.
 In future, rich data collected from sensors, smartphones, connected cars, vehicle-to-everything (V2E) and vehicle-to-infrastructure (V2I) systems will be fused and play a significant role in helping cities to pinpoint mobility challenges and prioritise spending.

Built from ground up to handle the largest cities

Since data-driven approaches don’t require complex, computational-intensive underlying traffic models, they are much faster than model-based solutions and can scale. Furthermore, the underlying framework can consider multimodal priority as well as traffic safety, emission or other factors as an objective function.

Accurate insight using AI and optimization technologies

An AI traffic management system can predict traffic, identifies accidents and gridlocks, and prescribe strategies. The solutions are designed to replace human oversight and manual traffic reporting by analysing data from various sources and generating real-time insights into incidents on the roads. AI-based predictive solutions increasingly play a transformative role in smart planning, implementation and management of infrastructure.

Requires no maintenance and improves every hour by self-learning

This is where the AI revolution meets maintenance. Self-learning involves applying the power of machine learning techniques to identify actionable insights for improving existing model. Maintenance is a key area that can drive major cost savings and is significantly reduced or fully eliminated by data-driven solutions.

PeakHour Mobility

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