AI in Transportation: Common Use Cases Shaping the Industry

The evolution of the transportation sector greatly relies on technology. The invention of IoT, cloud technology, and digitalization aided the industry in making a huge leap. Now that AI in transportation is transitioning from theoretical concepts to practical applications, businesses are in new competitive dynamics. Undoubtedly, the opinions on AI vary, and critics would argue against the decision to jump into spending their budgets on costly solutions. So, does it mean that artificial intelligence will pass someone by without any impact? Let’s discover the state of the market, the future of AI in transportation, and whether such investment truly pays off.

The transportation industry is an economically significant sector encompassing a broad range of activities and stakeholders. It’s not only a substantial contributor to global economies but a major employer. In the EU alone, this sector provides jobs for 10 million people and accounts for 5% of the GDP. In the US, the transport sector’s contribution to GDP reached a staggering 561.90 billion USD in the first quarter of 2023. On a household level, about 13.2% of a family’s budget is typically allocated to transport expenses. It’s also important to mention that this sector relies on fossil fuels and contributes to CO2 emissions, which contradicts the world’s sustainability and environmental friendliness efforts. Where does artificial intelligence in transportation stand? Well, artificial intelligence in transportation market is expected to be $3.5 billion in 2023, indicating the growing interest in this technology.

Artificial intelligence in transportation: balancing promise and complexity

When it comes to transportation artificial intelligence is a tricky offer. Before choosing your strategy, you need to find the balance between the promise of this technology and its complexity. There is an advantage to progress, and all new technologies, yet not all companies can afford to implement them, and oftentimes, they pass on such decisions. Indeed, when it comes to AI in transportation, developing your model or even training it may not be in the best interest of a business. But it is not a reason for declining the idea for good. If you want to get a couple of impactful use cases for your company, book our AI strategy workshop, and you will get a clear roadmap in 2-3 hours.

Leveraging advanced solutions offered in the AI industry can be a highly effective strategy for transportation businesses. Tech giants like AWS and Google offer a range of tools and platforms backed by extensive research and continuous updates. In this way, companies can bypass the resource-intensive process of building AI systems in-house.

Let’s start with the benefits of AI in transportation. They are numerous, but some are universal and beneficial for many stakeholders, namely transportation companies, vehicle manufacturers, urban planners, municipal authorities, insurance companies, and transportation and logistics staff.

What are the benefits of AI for transportation industry players?

Artificial intelligence, along with machine learning in transportation, reinforces multiple existing functionalities. It fits perfectly into the technological landscape and promises a new tech revolution.

Along with predictive analytics, real-time video monitoring, and other technologies, AI in transportation can find gaps in safety and prevent more incidents on the road, within a warehouse, and in other locations. For example, in autonomous vehicles, AI is used to detect and react to hazards faster than a driver.

AI also succeeds in managing traffic flow and reducing congestion. Siemens Mobility plans to develop “digital twins” for infrastructures across the globe using AI. One of their projects, digitalizing the Norwegian railway expects completion in 2034, showcasing the potential of artificial intelligence in transportation and urban mobility.

Here are more benefits of AI in transportation:

Use cases of AI in transportation: key examples and applications

The examples of AI in the transport industry include not only autonomous vehicles and robots. AI has a greater impact on smaller processes in operations. Predictive maintenance, automation, and route optimization are the driving forces behind the AI revolution in the transportation industry. Here are the most popular examples of AI in transportation we are witnessing now:

Predictive maintenance

Predictive maintenance using AI is a practice of utilizing artificial intelligence technologies to address the maintenance needs of assets before they lead to failure. It has been on the radar for transportation businesses for ages. It is a constant struggle for fleet owners to find spare parts, maintain the state of their equipment, and ensure that its capacity meets their business ambition. Technology has been a part of the solution to maintenance problems of transportation, namely IoT, predictive analytics, and, recently, artificial intelligence.

It’s important to remember that all the technologies mentioned rely heavily on data. They require specific preparations before implementation. System requirements will be among the challenges that transportation businesses will encounter when trying to streamline and enhance maintenance procedures will be system requirements. If you want to implement predictive maintenance using AI or IoT sensors, the chances are high that your system will need legacy modernization or updates. The next step will be introducing data collection and management tools.

According to IBM recommendations, predictive maintenance is suitable for expensive assets that are hard and costly to replace. When it comes to cheaper ones, it’s better to look for replacement solutions. So, what does it mean for the transportation sector? The benefits of AI in transportation are available to everyone. So is predictive maintenance. It can be combined with other technologies, and it does not have to be a sophisticated, custom-made solution. Tech giants like Amazon and Microsoft offer companies the opportunity to test out tools that require minimum effort. Let’s look at a real example of AI-powered predictive maintenance.

Deutsche Bahn has successfully implemented AI into their operations and the results are impressive. They claim to have improved customer service, fewer delays, and smooth maintenance. AI tools have been tested and installed in Rhine-Main and Stuttgart. They have been used to digitalize maintenance in the following ways:

• Condition-based maintenance for trains decreased the workloads and reduced the time spent on tasks like ICE roof inspections to minutes instead of hours.
• AI technology automatically evaluates camera images and sensor data for maintenance needs.
• Another tool in development will be used to forecast optimal times for servicing vehicle parts, for example, for wheel replacement.

Maintenance protocols seek to extend the lifespan of an asset and prevent costly failures for the organization and AI is another tool that helps with it.

Object detection and tracking

Object detection and tracking is a deep learning application where a system identifies objects and assigns them unique identifiers. As the objects move across different frames, the system follows them. This feature is similar to RFID asset tracking. However, the difference is that AI-driven object detection and tracking often use visual data and focus on real-time, dynamic identification and monitoring of the objects. In contrast, RFID asset tracking is more about tracking the presence of physical objects using radio waves.

Let’s say it’s a fleet. When transport leaves the parking space, the system assigns the titles “Car 1” and “Truck 1” to them. In addition to monitoring the route of the vehicles in real time, the system can also trace driver behavior. Practical applications of object detection technology are not new, and even AI-driven tools already gained some popularity.
For example, object detection is a part of Volvo’s advanced driver assistance system (ADAS).
It helps with collision avoidance, pedestrian detection, and lane-keeping. Another example is Tesla, a leader in incorporating AI into autonomous driving vehicles. Object detection is integrated into various aspects of the Tesla car operations, like:

• Adaptive cruise control
• Speed limit adherence
• Pedestrian safety
• Navigating roundabouts and complex intersections

Another application of AI in the transportation industry is represented by AI-powered drone monitoring. Inspecting bridges, roads, and railways, as well as traffic watches, are just a couple of examples. Drones combined with IoT sensors, and AI/ML algorithms change the whole industry. The experimenting with AI-powered drone technology started with Amazon warehouses. Later on, DHL launched an urban drone delivery service, and now businesses are finding more and more use cases of drones, especially powered by the smart capabilities of artificial intelligence. Port of Rotterdam is actively using drones and their vision of the future of this technology in the port includes the following use cases:

• Monitoring of operations and security
• Inspection of terminal installations
• Damage control
• Deployment at water pollution, and fire incidents.

Even though drone technology and others, such as IoT, were around and worked well before artificial intelligence stepped into play, it’s clear that pairing them with AI only boosts their abilities.

License plate recognition

AI-driven license plate recognition is a technology that uses artificial intelligence algorithms to read and identify vehicle license plates. The technology is widely used for traffic law enforcement, parking, traffic flow analysis, etc. Police across many cities are using LPR to find stolen vehicles or those associated with wanted individuals. The significance of the technology was proven by the case that happened in New York. The AI LPR they were using was installed on 480 cameras and scanned 16 million license plates per week. As a result, based on years of historical data, AI identified the suspicious behavior of a driver. He was following the typical drug dealer route, so the system alerted the police and the criminal was stopped, searched, and arrested. It’s also worth mentioning, that the system notes not only license plates but the model and color of the car which helps law enforcement greatly.

Other applications of LPR include:

• Toll collection. As an example, E-ZPass in the USA. LPR is used for electronic toll collection on highways. With this system, vehicles do not need to stop at toll points since it automatically reads license plates and charges accounts. As a result, the flow is improved and traffic congestion is reduced.
• Warehouse management. Amazon uses LPR to track inbound and outbound vehicles. It helps them manage dock assignments and optimize loading and unloading operations.

AI-powered license plate recognition is a versatile technology that can be integrated into businesses across industries to meet specific operational needs. Whether you need to enhance security, streamline logistics, or manage parking, facilities, LPR is a flexible and efficient solution for that.

Driver monitoring

The driver monitoring system market is expected to go above a staggering $9.3 billion by 2033. Among the reasons for such a surge was the rise of autonomous vehicles. On one hand, they helped businesses deal with the fuel prices, on the other hand, they increased safety concerns on the highways and country roads. Hence, businesses, insurers, and even governments pushed for driver monitoring systems (DMS) to be installed. When it comes to human operators, the driver behavior monitoring need is even more dire:

• The US statistics show that 12% of all car accidents involve cell phones.
• The average time spent on the phone while driving is 1:38 minutes per every hour.
• Distracted driving accounted for the reason for fatal accidents in 8% of cases in 2020.

These are just a few numbers that showcase the situation. A driver monitoring system is a solution to these and more problems that any business dealing with transport faces. Enhanced by AI, it can revolutionize the operations of even the smallest business. Here are common benefits and features of AI driver monitoring systems:

• Fatigue detection
• Distraction monitoring
• Eye tracking
• Head position monitoring
• Hand position detection
• Seat belt detection
• Data recording and analysis

Israeli company Cipia revealed that Chery, a Chinese automobile manufacturer plans to implement Cipia’s AI-driven driver monitoring in six of its car models. The system will recognize unsafe behaviors, including fatigue, distraction, non-use of seatbelt, smoking, and even the use of phones. The system alerts are supposed to prevent accidents and enhance road safety.

Vehicle telematics

AI-driven vehicle telematics involves the integration of artificial intelligence algorithms with telematics systems used in vehicles for monitoring and collecting data. With such a combination, traditional telematics capabilities improve significantly, offering enhanced insights into vehicle performance, driver behavior, and predictive analytics. One of the key advantages of this technology is its ability to work alongside traffic management systems, optimizing route selection and helping drivers navigate congested roads more efficiently. Additionally, AI-powered telematics can improve road safety by detecting risky driving patterns, issuing real-time alerts, and recommending corrective actions to reduce accidents. The benefits of AI integration into vehicle telematics include:

• Integration with other systems (ERP, CRM, etc)
• Predictive insights (vehicle maintenance needs, or part servicing)
• Real-time monitoring and alerts (deviations from planned routes, unauthorized vehicle use)
• Tailored insurance premiums (insurance packages based on actual driving behavior lower costs for safe drivers)
• Better compliance (telematics guarantees compliance with regulatory requirements, for example, Hours of Service)

A case of a cloud-based IoT solution for driver monitoring and vehicle telematics

Telematics in fleet management is used for safety, cost reduction, and insights into operations. It allows driver monitoring and risk assessment. Euristiq worked on a case of a fleet insurance company based in London. The company based its risk analysis on non-visual telematics and concluded that video telematics would be more precise and, hence effective.

Euristiq has an extensive knowledge of cloud development and for this client, we developed a cloud-based IoT platform with video telematics from cameras. As a result, the client received the telematics software for storing, analyzing, and categorizing visual telemetry data and driver monitoring for insurance purposes.

Telematics Fleet Management Software

Leveraging the power of telematics fleet management to optimize insurance

Safer Driving with Road Traffic Monitoring

IoT-based traffic monitoring software with automatic incident detection

Better Intelligence for Business Insurance

Intelligence software for business insurance in transportation and logistics

Route optimization

Route optimization in a traditional way was about path selection by dynamically adjusting to real-time data. With the integration of AI into route planning, various factors like traffic patterns, weather conditions, and road closures are analyzed to determine the most efficient route. The benefits of AI route optimization are:

• Efficient scheduling for fleet management. Artificial intelligence and machine learning enhance scheduling to ensure that fleet operators use their resources better.
• Lower operational costs. Optimized routing will cut the costs of fuel and maintenance resulting in reduced operational expenses.
• Environmental sustainability. With the improved routes and less fuel spent, carbon emissions will go down.
• Data-driven insights. Collection and analysis of historical data on trips helps in improving future planning and transportation strategies.

UPS developed a platform ORION (On-Road Integrated Optimization and Navigation) and launched it in 2012. Initially, it was used to guide UPS drivers across the delivery and pickup routes in the US, Canada, and Europe. Nowadays, ORION uses artificial intelligence and machine learning to optimize routes. UPS concluded that the technology saves 100 million miles and 10 million gallons of fuel per year and the upcoming advancements are going to boost its efficiency even more.

Delivery and last-mile optimization

Last-mile delivery is the final step of the process, where a package is transferred from a transportation hub to its final destination. At this point, it is marked as “out of delivery” and for the customer, in many cases, it marks a long time of waiting. It happens due to multiple stops and delays that happen in this final shipping stage. This issue is even more exacerbated in rural areas with considerable distances between delivery stops. In urban areas, conversely, traffic congestion causes the same problem. Last-mile delivery constitutes about 53% of the total shipping. It explains its importance in the economics of shipping and delivery operations.

The leader in handling last-mile problems along with other problems of logistics, delivery, and supply chain is Amazon. It managed to optimize these processes with advanced logistics networks, and innovative technologies including AI-driven route optimization. As a result, the company received enhanced customer satisfaction and reduced delays. It’s safe to say that they set the standard high for the competitors in the industry and a part of their competitive advantage is artificial intelligence.

The following table presents a comprehensive overview of examples of AI in transportation, along with their respective benefits, and some of the leading technology providers in the AI market.

AI in logistics: optimizing transportation for the future

AI applications in logistics and transportation, make supply chains more efficient, cost-effective, and resilient. The global AI in logistics market was valued at USD 17.96 billion in 2024 and is projected to grow to USD 707.75 billion by 2034, expanding at an impressive CAGR of 44.40% from 2025 to 2034. This rapid adoption is driven by AI-powered automation, predictive analytics, and real-time fleet tracking, helping businesses streamline operations, reduce delays, and improve decision-making.

The role of AI in logistics extends across inventory management, route optimization, warehouse automation, and fraud detection. Businesses benefit from AI’s ability to analyze vast datasets, ensuring faster deliveries, reduced costs, and optimized stock levels. AI-driven predictive maintenance enhances fleet performance by anticipating mechanical failures before they happen, minimizing downtime and repair costs.

Here are some notable examples of AI in logistics:

• Warehouse Automation
• Predictive Maintenance
• Supply Chain Visibility
• Autonomous Freight Transport
• Demand Forecasting
• Route Optimization
• Fraud Detection
• Inventory Management
• Customer Service Chatbots
• Last-Mile Delivery

How to build an AI strategy for transportation businesses?

As the industry evolves, businesses are seeking an AI strategy. Many want it for the sake of competition, and many see the true value in cost-saving, and improving efficiency. The issue of investment arises, and not all businesses are prepared to sacrifice large budgets for a recent innovation. However, not all solutions are expensive, and the right approach can bring more benefits than one could imagine. AI/ML is a crucial part of business digitalization that has been happening for some time and it is essential to embrace technologies and use them to your advantage.

Another important tip is that AI/ML should be handled by professionals and there is potentially a knowledge gap that businesses struggle with. It is better to delegate such tasks to a team of professionals, either in-house or outsourced.

If you want to explore AI applications in transportation and see how they fit into your business, consider the following points before you start:

Business Objectives:

• What are your key goals in integrating AI technology into your business, and do you have manual processes that could be replaced or enhanced by AI for improved quality and reduced errors?
• How do you envision AI validating and elevating your operational processes to achieve your primary business objectives?

Data Readiness:

• How would you rate your company’s ability to collect, store, and manage data necessary for AI?
• Have you pinpointed potential data sources and the challenges you might face in integrating data?

Internal Expertise:

• Do you have AI/ML experts on your team?
• What resources do you have to support AI/ML implementation?

Integration with Existing Systems:

• Have you assessed how AI technologies will fit with your current IT setup and software systems?
• Do you foresee any potential integration or compatibility challenges that might arise with the implementation of AI solutions?

Scalability and Future Growth:

• Can your current infrastructure handle the growing demands and workloads of AI as your business expands?
• Have you thought about how scalable your AI solutions are in line with the anticipated growth of your business?

Embrace the future of AI in transportation

The future of AI in transportation holds revolutionary ways of transporting people and goods. But AI is not just about introducing new technologies; it’s about reinforcing and revitalizing existing functions, making them more accurate, faster, and cheaper. The integration of AI in IoT, cloud computing, blockchain, AR, and VR will create a different tech landscape. However, this future comes with the condition: the necessity to update legacy systems. This step will be crucial for businesses to remain competitive advantage and adopt next-generation solutions. That’s where AI-native development comes in — building systems with AI at the core from the start, so they’re ready to adapt as new tech emerges.

In this context, connected mobility plays a significant role in addressing specific transportation challenges through integrated, sustainable transportation networks. AI tools and algorithms are instrumental in improving traffic management systems by connecting vehicles and infrastructure, enhancing safety, and reducing congestion. For instance, in AI development, specialists are leveraging generative AI to build predictive traffic management and smart parking systems, optimizing transportation space and efficiency within cities. With AI systems embedded in transportation, professionals can better create adaptive, resilient networks that meet evolving urban demands, driving the future of artificial intelligence and transportation.

Euristiq AI consulting service offers transportation businesses an opportunity to harness a wide range of telemetry data from speed to fuel consumption, vehicle idle time, and engine diagnostics. Our expert guidance will help you gather data most relevant to your operational needs and benefit from it effectively. We integrate ready AI solutions into your transportation business and turn your data into actionable insights, driving innovation and growth.

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