A key element of public security is the ability of the emergency services to respond to incidents in a timely fashion. Therefore, the intelligent deployment of resources is essential to maximise financial efficiency and human performance. Knowing when and where to deploy manpower can reduce the impact of road congestion and free up staff for further training, without compromising performance.

The Public Security project aims to develop a system capable of analyzing data and generating optimal resource deployment plans that are intelligent and dynamic, adapting to the unique challenges of each day as it comes.

Conceptual Overview

The Public Security project combines data analysis with optimisation and prediction to reduce manpower while maintaining response time standards. The project combines the following elements:

• Incident prediction: machine learning analysis of historical data allows the learning of predictive models, enabling the generation of synthetic incident sets for analysis.
• Resource optimisation: robust optimisation techniques are used to generate efficient deployment plans and manpower shift schedules.
• Simulation: resource plans are evaluated through simulations that may be used to examine solution performance across a range of test scenarios.
• Visualisation: a responsive UI allows key decision-makers to plan deployments and run simulations, enabling them to make resource allocation decisions that combine AI-driven analysis with on-the-ground experience.

GRAND-VISION Optimizing Security Resources

GRAND-VISION (Ground Response Allocation & Deployment - Visualization, Simulation, Optimization) is a solution that automatically optimizes allocation of police resources based on crime prediction learnt through AI by adding factors such as past crime data, weather, and seasons of the day. It automatically optimizes the allocation of police and other emergency vehicles as well as shifts of personnel.