AI in Transport

The transportation sector is an integral part of our lives. It underpins the movement of people, goods, and services, facilitating economic activity and personal mobility—an infrastructure we can’t imagine our lives without!

On the one hand, transportation connects individuals to workplaces, schools, healthcare, and recreational activities. On the other, it fosters trade and market accessibility through the swift movement of products from manufacturers to consumers. Interestingly, advancements in transportation technology, particularly in Artificial Intelligence (AI), have enabled the industry to tackle some very important challenges, including:  

• Haywired public transportation schedules and routes
• Inefficiencies in logistics and supply chain management
• High rates of accidents due to human error or distracted driving

With the global AI in transportation market size valued at $4.48 billion in 2024 and predictions to touch $23.11 billion by 2032, the time has never been better to embrace this technology.

1. The future of transportation with autonomous vehicles

Autonomous vehicles rely on a combination of core technologies, which include:

Sensors: Examples such as Lidar, radar, and cameras provide comprehensive data about the vehicle’s surroundings. Machine Learning (ML): Its algorithms analyse these interpretations, enabling the vehicle to make real-time decisions and navigate safely. Computer vision: It processes this data to detect and interpret objects, lane markings, and traffic signals.

The potential benefits of self-driving cars are substantial. One of the primary advantages is increased driver and passenger safety, as AI systems can react faster than humans and aren’t susceptible to distractions or fatigue.

Secondly, reduced traffic congestion can be achieved through optimized driving patterns and coordinated vehicle movements. Improved accessibility is another significant benefit, offering urban mobility solutions for those unable to drive, such as the elderly or disabled. Tesla’s Autopilot, for example, uses a wide range of sensors and advanced AI to assist with steering, acceleration, and braking. It can navigate highways, change lanes, and even park vehicles autonomously, boosting service reliability and convenience.

2. Optimizing routes and fleet efficiency through AI

Route optimization is the backbone of efficient supply chain management—a must for logistics companies. When your routes are optimized, delivery costs are lowered, and orders are completed on time and in top condition.

AI algorithms study real-time traffic data and weather conditions to determine the most suitable route for delivery. They then select appropriate package delivery windows and assign a delivery executive based on driver schedules.

Approaches like this have helped UPS’ ORION system save an estimated 10 million gallons of fuel annually.

In addition, AI algorithms can power optimized fleet management for you by tracking delivery vehicle locations and automatically detecting any performance issues that could call for repairs or maintenance. This minimizes downtime from last-minute breakdowns or delays reduces idle time, and extends vehicle life for your whole fleet. It’s also how Amazon manages its fleet to fulfil its considerable delivery obligations worldwide.  The proprietary Amazon scanner tool Rabbit provides real-time data on road conditions and traffic flow to adjust routes accordingly. Having such information at their fingertips helps drivers plan and execute their deliveries more effectively.

3. AI-driven real-time traffic management

Imagine being able to navigate through city traffic with ease, knowing exactly where the congestion points are and effortlessly avoiding them. Sure, apps like Google Maps and Maze help a lot in this regard, but AI takes this to another level.

AI-powered applications analyse vast amounts of traffic data in real time. The technology can identify incidents, traffic jams, and other disruptions and suggest alternative routes to ensure smooth travel. For your transportation company, this means that delivery routes can be optimized on the fly, reducing delays and fuel consumption. The result is more reliable and efficient service, which in turn increases customer satisfaction and lowers operational costs.

Alphabet’s Sidewalk Labs, for example, uses AI to gather and analyze data from various sources, including cameras, sensors, and GPS devices, to create a comprehensive picture of urban traffic patterns.

The AI algorithms identify congestion points and predict potential incidents, allowing for proactive traffic flow analysis and management.

4. Optimizing fuel usage with AI

AI algorithms can study driving behaviours such as braking patterns, speed, acceleration, and routes taken to identify actions that could waste fuel and redirect your drivers to more fuel-efficient routes.

Scania does this with its AI-powered fuel optimization system, which offers real-time feedback to drivers and helps them adopt fuel-efficient driving patterns, brake and accelerate more smoothly, avoid unnecessary idling, and maintain sustainable speeds.  AI technology can also analyze vehicle performance data to ascertain that your vehicles are operating at peak fuel efficiency. This allows you to conduct repairs or part upgrades if necessary and further reduce your fuel costs.The International Transport Forum states that if transportation companies reduce fuel consumption even by 10%, they can save an average of USD 30,000 per vehicle every year considerably minimize their carbon footprint, and improve environmental impact. Now, data-driven decisions don’t look difficult anymore!

5. Staying ahead with predictive maintenance

Simply put, with predictive maintenance, AI algorithms can help you tackle issues proactively rather than reactively.

They do so by analyzing large volumes of historical data to identify the factors that could cause wear and tear or even unexpected breakdowns in your vehicle equipment, including engine temperature, vibration patterns, oil levels, and so on.

This information equips you to schedule repairs or tune up your vehicles before problems arise, reducing downtime and avoiding potential road safety hazards. Siemens, for instance, has used maintenance intelligence well.

It uses AI to study data gathered from smart sensors placed on railway trains and tracks and predict when and where repairs will be necessary. This reduces unscheduled depot stops for corrective maintenance by up to 30% and ensures fleet availability by up to 100%.