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.

Self-driving vehicles represent the most advanced form of ADAS, as they rely on AI to completely automate the driving experience. Most embodiments of this technology are still within the realm of prototyping and experimentation, or require human supervision by law in order to be operated. However, companies like Tesla have pioneered self-driving cars with promising results. Other major types of self-driving vehicles include robotaxis, autonomous trucks and truck platooning systems (the coordinated movement of multiple trucks at close range), and autonomous navigation for container vessels through video object recognition and LIDAR.

AI-based solutions can help logistics companies optimize the supply chain by coordinating fleets of vehicles, ships, and planes. Their operation is based on a blend of GPS, sensors, computer vision-powered cameras, and other interconnected IoT devices deployed to gather data regarding weather, traffic, blockages, or accidents. These devices are used in combination with AI-based analytical systems to process this information, identify recurring traffic patterns via machine learning algorithms, and generate valuable route recommendations or forecast potential traffic congestion.

Furthermore, transportation companies are turning to generative AI and large language models for logistics simulation, creating virtual scenarios to assess how demand spikes, vehicle breakdowns, or other factors would impact their network and adjust their contingency plans accordingly.

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.

Artificial intelligence systems can be deployed both in indoor car parks and outdoor urban areas to facilitate parking management. These solutions can be helpful in several ways, including vehicle counting and free slot detection connected with parking availability indicators, license plate matching to detect unauthorized parking, and time tracking for easier ticket billing and payment. AI-powered cameras are also used to identify suspicious activities for parking lot security.

This approach relies on machine learning-based anomaly detection to predict failures before they actually occur. A machine learning system can be trained to understand the normal operation of vehicles and transport infrastructures (such as railway overhead lines) by "feeding" it with sensor data relating to their standard performance. Once the system has learned to recognize the ideal operating patterns of the mechanical and electronic components powering a vehicle or an infrastructure, it can also detect any outlier that can be a sign of impending breakdown.