AI Use Cases
Identify performance and risk for pilots through flying patterns and other data
Identify performance and risk for pilots through flying patterns and other data. Similar analysis can be done on road drivers although the data is inevitably more likely to be complicated by ground-level interactions that would be more traceable in the air (e.g. other vehicular movement).
Personalise loyalty programs and promotional offerings to individual customers
Personalise loyalty programs and promotional offerings to individual customers - typically this will involve balancing availability of time-sensitive supplier capacity (e.g. airline seats) with customer demand and preference profile
Personalise flight recommendations to target individual consumers in time sensitive and dynamic market
Personalise product recommendations to target individual consumers reflecting multiple pricing, product and service preference indicators. The time sensitive nature of the product offering increases the need to find an appropriate market-clearing price.
Augment traffic control processes at (air)ports
Support management of real time data and decision-making in traffic control - for example at airports. Automatic data flows from key assets and data modelling enhanced from multiple sources (e.g. weather models) help with flow optimisation, operator support and risk prediction.
Identify fraudulent customer claims for transportation delays
Freight And Logistics
Ensure rapid response - and fraud risk analysis - on claims by customers whose trains have been delayed. By nature this will be peaky (given that train delays frequently cascade) - and customers will expect brisk turnaround (the timing of this may also be open to regulatory monitoring). AI enables this challenge to be met and can also check for those attempting to use the situation to defraud the business.
Optimise traffic/passenger flow through visual data including video and images
Real time information such as images or live footage can provide information on crowd flow in areas where congestion is undesirable (e.g. airports or train stations). Machine vision is being used to process the data and prompt the necessary management actions to optimise passenger flow.
Monitor and predict problems on rail network
Most railway networks are large, complex, frequently old, subject to all manner of environmental hazard and prone to degradation. Using sensors and cameras to monitor tracks and predict required maintenance can have a considerable cost and safety impact.
Pilot and resupply drone independently
Fully automated drone piloting and resupply (e.g. energy power ups). Typically this would be deployed in areas of low human habitation to minimise risk - for example in agricultural or mining applications. As reliability improves these will be deployed in more urban environments.