Let’s start by discussing what Machine Learning is. The term was added to our vocabulary in 1959 by Arthur Samuel, a pioneer in computer gaming and artificial intelligence (AI). Machine learning is a branch of AI founded on the premise that systems can learn from data, identify patterns and provide effective and reliable predictions with minimum human intervention.
Machine Learning is often confused with Deep Learning which a subset of Machine Learning that is inspired by the information processing patterns of the human brain. Deep Learning is often thought of as the next step in Machine Learning as it can learn from its own method of computing and be more accurate in things such as image recognition and natural language processing.
Machine Learning has been around for a long time. But it has truly exploded over the last couple of years thanks to advancements in computer processing and cloud computing which has allowed for the scaling and analysis of massive amounts of data. Today Machine Learning exists in every corner of our lives; ever listened to a song recommended by Spotify? Used a virtual personal assistance like Alexa? Or planned a trip with a GPS navigation service or transported goods for an e-commerce customer? Then you have made use of Machine Learning.
Machine Learning is everywhere and trucking is no different. Given the massive amount of data the industry generates through connected vehicles, electronic logging devices, sensors and more, it’s likely to be gain even more traction. Some areas where Machine Learning is already being applied in trucking include back-office automation, route optimization, predictive maintenance and driver development. It is also a key component in the development of technologies like platooning and digital load matching platforms.
But this is just scratching the surface of what Machine Learning can do for the trucking industry. Looking into the future, the technology will continue to evolve, make better predictions in increasingly complex environments and solve the industry’s biggest challenge. Here is a look at some areas where Machine Learning will generate massive value for transport operators:
Optimized freight routing and bundling
Empty miles account for 20% of road freight traffic in Europe, a number that can go up to 40% in China. A major cause for this is inefficient dispatching systems where trucks are travelling to a pick-up destination without any load. Machine Learning can reduce the number of empty miles travelled by for instance predicting the arrival time of different vehicles and freight and clustering deliveries based on geographical location and destination. The outcome is not just the better utilization of assets and fewer vehicles on the road but reduction in delivery costs by up to 25 % and emissions by up to 30%. Uber and Lyft use the principle to move people, and new business models are emerging to extend this to the transport of goods.
Traffic flow forecasting
Today city planners and other decision-makers often plan transportation infrastructure without sufficient information about traffic patterns resulting in problems like congestion. At the same time, there is a growing volume of data sources like from GPS navigation, satellite imagery and even social media check-ins that can be analyzed by Machine Learning technology to make live traffic predictions and recommendations. Through the use of automated traffic signals that operate on of data gleaned from cameras, sensors and satellite imagery, traffic flows could be re-directed to ease congestion, particularly on city roads. The city of Hangzhou is already testing the grounds with the use of Alibaba’s City Brain project which coordinates more than 1,000 road signals with the aim of preventing or easing gridlock in the city.
Autonomous vehicles
Autonomous driving has been a central concern of the trucking industry for quite some time. Autonomous vehicles wouldn’t be possible without the help of Machine Learning which, among other things, continuously renders the surrounding environment of the self-driving vehicle and predicts possible changes to those surroundings. Vehicle autonomy is already happening though a timeline for full-scale adoption remains elusive due. What is clear though is that the technology offers the potential to lower costs, improve productivity and address the challenges that come with new modes of consumption like e-commerce.
These are just some of the ways in which Machine Learning is and will impact the trucking industry. There are of course many other ways in which the technology will make the logistics industry more proactive, predictive, automated, and personalized.
As a transport provider it will be crucial for your business to adapt and incorporate digital technologies to your everyday operations. Though this might seem like a tough undertaking, it really starts with taking a few practical steps which I have outlined in a guide available for download.
This guide will help you:
- Determine the digitalization needs of your business
- Understand how and where digitalization may add value
- Understand what insights and data you could get from your OEM
- Grasp how to narrow down digital solution options based on key considerations like company size, security and ease of use
