AI in transportation: use cases,
trends, real-world examples & challenges

Voice-based virtual assistants use speech recognition and synthesis technology to interact with drivers in spoken language. These tools can understand users' requests and perform a variety of tasks, such as initiating a call, switching radio stations, or providing information on vehicle conditions.

Autonomous vehicles (AVs) represent the most advanced form of ADAS, as they rely on AI to process live traffic data from cameras, LiDAR, and radar sensors and automate the driving experience, thereby reducing fatigue and lowering shipping costs. Leading examples include Tesla’s Autopilot and Waymo’s robotaxi services. Other major types of self-driving vehicles include self-driving trucks, truck platooning systems (the coordinated movement of multiple trucks at close range), and autonomous navigation for container vessels.

The adoption of AI in public transit opens up multiple opportunities for network optimization. For instance, service providers can analyze average travel times and the factors impacting them, such as road conditions and restrictions, to adjust routes, stops, connections, and schedules and fully meet customer demand for maximum passenger convenience. AI-based analytics systems can also be combined with passenger apps to provide users with real-time recommendations on the best lines and boarding times, helping redistribute commuters to less crowded routes, especially during rush hour.

Powered by natural language processing and generative AI, chatbots provide customers with a convenient way to access information and receive assistance, while enabling transportation companies to automate and scale their service operations. For instance, these conversational AI solutions can answer common questions about transportation options and routes, help users schedule rides or purchase tickets, provide real-time updates on delays or arrivals, and gather valuable customer feedback.

AI-powered traffic management systems rely on networks of sensors and cameras to oversee road and traffic conditions, identify car crashes, and make traffic predictions. This allows authorities to intervene promptly in the event of traffic accidents, speed up road repair and maintenance operations, and optimize traffic light management based on vehicle density. Organizations can also leverage GenAI to create realistic simulations of traffic flow under various conditions, allowing transportation engineers to test the impact of new road layouts or traffic policies before implementing them.

ANPR solutions encompass HD cameras mounted on street poles, infrared sensors for 24/7 monitoring, and image processing software used to identify vehicle registration plates. These systems are useful for a variety of management and security tasks, including journey time analysis and road infrastructure planning, the identification of vehicles violating road rules, and electronic payments enablement for cashless tolling lanes.

AI-powered solutions facilitate parking management both in indoor car parks and outdoor urban areas by automating vehicle counting and detecting vacant spots. These systems support license plate recognition for seamless billing and identify suspicious activity for parking lot security, while also significantly reducing urban traffic congestion.

Predictive maintenance relies on ML-based anomaly detection to forecast asset failure. By analyzing real-time sensor data and historical performance patterns of vehicles and transport infrastructures such as railway overhead lines, companies can identify signs of impending asset breakdowns before they lead to service disruptions.