The transportation industry has always been keen to adopt Artificial Intelligence and Machine Learning capabilities in its operations. From planes to ships and cars to trains, AI and ML have redefined the entire journey, enriching customer experience, increasing profitability, and fueling growth.
For instance, popular ride-hailing service provider-Uber leverages its Machine Learning platform ‘Michelangelo’, for intelligent traffic management, marketplace forecasting, route planning, ETA and ridesharing, enabling autonomous decision making based on analysis of real-time data.
Uber’s platform is a classic example of how ML implementation has evolved to meet customers’ dynamic requirements over the past years, bringing in intelligent processes that constantly enrich current operations by factoring in a wide range of data metrics and predictive analytics. AI and ML enable the transportation industry to automatically generate deep insights from data and user behavior and take pre-determined actions that work best in the situation for the operational process and the end-user. GPS-enabled driving directions and self-driving cars are examples of how AI and ML can profoundly enrich the way the transportation industry operates.
Autonomous vehicles have self-driving capabilities. They use AI-powered object detection techniques from sensors to feed intelligent systems that interpret the visual data and perform driving actions such as turning, braking, slowing down, etc. They also have traffic detection techniques and can interpret and act on traffic lights, making them less prone to accidents and delays.
AI-powered Autopilot mode enables planes to run autonomously for most of the flying time, except for takeoffs, flight landings, or any critical scenarios. This is used by almost all commercial airplanes in the current times. This is a significant usage of AI in the transportation industry for the wide range of data that needs to be analyzed while piloting an aircraft.
GPS Apps provide real-time updates on traffic conditions and driving directions, enabling users to get the best possible route to reach their destination. They are widely integrated with other mobility and delivery Apps, apart from being an integral part of autonomous vehicles.
AI-based Traffic Management systems analyze traffic patterns, identify congestions, and automatically provide appropriate signaling and diversions to decongest the chokepoints.
Flight and Train delays can be accurately predicted by AI-powered systems that analyze the traffic and other parameters and give the precise ETA. This helps the passengers by keeping them informed of the exact delay in arrival and creates a good customer experience.
Location Trackers are widely used in freight management to give the exact details of the cargo being transported and enable easy tracking with AI-powered GPS capabilities.
Apart from the above functional features, AI and ML are also widely used for generating operational insights, predictive analytics, and forecasting for better decision making. AI and ML-powered solutions are hosted on the cloud due to the enormous amount of data they require to process.
Microsoft platforms support the use of AI and ML in solutions with its vast range of products.
AI Builder is a capability in the Power Platform suite that enables AI features to be included in Power Apps and Power Automate. It doesn’t require technical or data science skills to build an AI-powered App and includes pre-built models for common business scenarios.
The Azure IoT transportation and intelligent mobility solutions suite uses Azure IoT Edge, Hub, ML, and Maps to enable smart transportation by assessing traffic conditions, optimizing delivery routes, tracking shipments, and connected vehicles. It uses real-time data and alerts to respond to delays and provides alternates on the go. This is of vital importance to the supply chain and logistics sector as it gives a complete insight into cargo management with track and trace capabilities.
Azure Maps enables the creation of embedded location capabilities in web, IoT, and mobile Apps using APIs, SDKs, and geospatial services. It enables the creation of rich data visualizations for locations such as heatmaps, layers and markers and can also be integrated with open-source map controls. Azure Maps include address search capabilities, traffic monitoring features, routing suggestions, and help to move between time zones.
The Azure Machine Learning Studio enables rapid development of Machine Learning models with leading Machine Learning Operations (MLOps) by open-source integrations and built-in tools. It enables easy management and governance of ML models with complete security and compliance.
Azure Data Lake Analytics is a service for easy management of infrastructure and code in a distributed model. It dynamically allocates resources and facilitates working on big data to generate the required analytics. Azure Data Lake Analytics supports programming languages such as U-SQL, Python, R, and .NET. It can be scaled instantly and processes petabytes of data on demand.
With the vast set of solutions for developing AI and ML-enriched applications, the Microsoft platform has become a preferred choice for achieving digital transformation in the transportation industry. The dynamic and fast-paced needs of the industry can best be met by a cohesive, secure, and robust platform that offers a unified environment for all operational requirements-both for current sustenance and future growth.