What Technologies Are Driving Fleet Tracking & Logistics?

July 2022

Sustainability & Smart Cities

Cara Malone

Senior Research Analyst

The Fleet Management Ecosystem

Fleet vehicles are the driving force behind commerce and public mobility. Fleet managers have the important role of organising and overseeing vehicles for performance, maintenance, and tracking purposes. Telematics solutions collect, store, and analyse data that can be sent to fleet managers. This data helps fleet owners and managers evaluate vehicle maintenance, driver operation, and cargo management. IoT is transforming fleet management with the ability to connect vehicles and capture a wide range of data about vehicle performance, route, passengers, and cargo.

The Importance of AI

AI is able to detect a variety of risky road behaviours that can lead to distracted or drowsy driving, which in turn can potentially lead to accidents, including yawning, constant blinking, missing turns and exits, drivers drifting out of their lane, etc. AI can detect these actions and report them to the managers in real-time; allowing them to take corrective measures.

AI can also optimise the freight loads that are picked and delivered, reducing fleet expenses and increasing fuel efficiency. AI is able to provide managers with a better understanding of what current capacity looks like and how it changes over time. AI technology in transportation management systems uses machine learning models to predict customer demand to match available transport capacity and join some of the deliveries.

The Role of Machine Learning

Machine learning is a branch of AI which focuses on the use of data and algorithms to imitate the way humans learn, gradually being able to improve its accuracy. Machine learning technology allows fleets to learn from the data that is being collected over time and make adjustments based on that historical data. Resulting in smart systems in which AI can learn decision-making capabilities that enable more efficient and effective handling of situations. Machine learning can be used in all aspects of fleet management, streamlining processes and making them simpler. 

Safety the Primary Challenge

A major challenge for fleet companies or any fleet manager is keeping drivers safe. Ensuring safe driver behaviour is challenging when fleet managers are miles away from the moving vehicle. Fleet managers can use helpful tools, for example dash cams and vehicle tracking systems, to bring real-time visibility and provide evidence or materials to implement driver training.  

Driver shortages are a growing issue within the industry, with retention rates being low. Fleet managers need to focus on stabilising and increasing driver retention in order to increase the productivity of their fleets and reduce costs. This can be achieved through fleet management programmes, by monitoring speeding, harsh acceleration, over revving and engine idling by using GPS data, fuel consumption and AI dashcams.