8 Real-Life Use Cases of AI in Mobility
The autonomous vehicles market was $23.33 billion in 2020 and is forecast to grow to $64.88 billion by 2026 at a CAGR of 22.7%. Yet, the applications of AI in mobility reach far beyond driverless vehicles.
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AI in mobility can help address many transportation challenges, both by improving the efficiency of the modes and mobility at a system-level across modes.
Given the characteristics of AI, it can help the mobility landscape by:
- Acquiring and interpreting data — such as traffic flow, weather, crowding.
- Mining insights and predicting future conditions — such as congestions or disruptions.
- Aiding real-time decision making — such as optimizing routes or adjusting speeds.
Applications of AI in Mobility
Businesses are creating mobility solutions using AI, machine learning and data analytics to help cities move towards more personalized, environmentally-friendly and autonomous systems.
A few applications of artificial intelligence in the mobility landscape include:
- Smart Grid Management
- Mobility-as-a-Service (MaaS)
- Driver Monitoring
- Autonomous Vehicles
- Traffic Management
- Vehicle Manufacturing
- Insurtech
- Smart Cities
8 Real-Life Use Cases of AI in Mobility
Applications of AI and machine learning will continue to have a significant impact on the mobility sector as these technologies not only improve efficiencies within current transportation systems but also unlock brand new products, services and markets.
AI in Transportation Planning and Execution
Determining infrastructure needs: The UK’s National Infrastructure Systems Model (NISMOD) works across various domains, including energy, digital, transportation, and water management. They use AI and deep learning capabilities to accurately understand interdependencies across different types of infrastructure.
Dynamically optimizing transport network control: The Norfolk Southern Corporation in Georgia uses AI to process a much greater volume of data in real-time to optimize traffic patterns across the rail freight network, enabling drivers to increase speed by 10-20%.
Predicting flight arrival times for long-haul flights: Singapore’s Changi Airport uses machine learning and AI-based decision-making to predict flight arrival times with 95% accuracy, enabling more responsive and efficient ground-handling operations and shorter queues for travelers.
AI in Asset Lifecycle Management
Diagnosing asset condition from automatically captured images: After capturing the images, AI-based systems can analyze them and recommend maintenance interventions, improving asset performance and reducing costs and downtime. Deutsche Bahn uses AI to secure stock maintenance.
Improving the security and efficiency of operations: Seattle-Tacoma International and Gatwick use the Switzerland-based Assaia’s Apron AI, which takes unstructured data from the aircraft and identifies the tasks to be performed. It then matches them with maintenance procedures to ensure all tasks are performed efficiently and securely across all teams.
AI in Customer Engagement
Personalizing mobility user experience: The GPS navigation app Waze introduced new features that proactively create itineraries using personal trip history, localization and time data.
Facilitating behavior change: Apps like AxonVibe and MotionTag detect and predict real-time traffic patterns, push contextual information only at the right moment and in the right place to change consumer behavior. Personalization is becoming significant as mobility becomes more human-centric.
Improving customer experience: Many airports — including La Guardia and Philadelphia International Airports — have started using AI-powered robots to perform a wide range of tasks, from cleaning and security to check-in and baggage handling.
The demand for AI applications in mobility is on a steep rise. New entrants are making their way into the automotive market even as Tesla is perfecting its autopilot system, Uber is testing robo-taxis and Google is developing self-driving cars.
AI in Transportation Planning and Execution
Determining infrastructure needs: The UK’s National Infrastructure Systems Model (NISMOD) works across various domains, including energy, digital, transportation and water management. They use AI and deep learning capabilities to accurately understand interdependencies across different types of infrastructure.
Dynamically optimizing transport network control: The Norfolk Southern Corporation in Georgia uses AI to process a much greater volume of data in real-time to optimize traffic patterns across the rail freight network, enabling drivers to increase speed by 10-20%.
Predicting flight arrival times for long-haul flights: Singapore’s Changi Airport uses machine learning and AI-based decision-making to predict flight arrival times with 95% accuracy, enabling more responsive and efficient ground-handling operations and shorter queues for travelers.
AI in Asset Lifecycle Management
Diagnosing asset condition from automatically captured images: After capturing the images, AI-based systems can analyze them and recommend maintenance interventions, improving asset performance and reducing costs and downtime. Deutsche Bahn uses AI to secure stock maintenance.
Improving the security and efficiency of operations: Seattle-Tacoma International and Gatwick use the Switzerland-based Assaia’s Apron AI, which takes unstructured data from the aircraft and identifies the tasks to be performed. It then matches them with maintenance procedures to ensure all tasks are performed efficiently and securely across all teams.
AI in Customer Engagement
Personalizing mobility user experience: The GPS navigation app Waze introduced new features that proactively create itineraries using personal trip history, localization and time data.
Facilitating behavior change: Apps like AxonVibe and MotionTag detect and predict real-time traffic patterns, push contextual information only at the right moment and in the right place to change consumer behavior. Personalization is becoming significant as mobility becomes more human-centric.
Improving customer experience: Many airports — including La Guardia and Philadelphia International Airports — have started using AI-powered robots to perform a wide range of tasks, from cleaning and security to check-in and baggage handling.
The demand for AI applications in mobility is on a steep rise. New entrants are making their way into the automotive market even as Tesla is perfecting its autopilot system, Uber is testing robo-taxis and Google is developing self-driving cars.
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