The 2020s are embracing Artificial Intelligence (AI) across many industries and it has already transformed commercial transportation toward greater efficiency. Not only is AI streamlining fleets in supply chains, but it’s also contributing to an evolving ecosystem of mobile IoT sensors connected with a centralized database and control center. Here is ways AI is improving the trucking industry today and on the road ahead.
Revolutionizing Commercial Transportation with AI
The trucking industry is steadily adopting autonomous trucks that can be connected in platooning strategies, driven by AI. Truck connectivity within convoys automatically keeps trucks at a calculated close distance between each other on a programmed and monitored route. It can be used to support drivers during segments of a journey.
Another way AI is advancing the trucking industry is through fleet monitoring technology that detects technical issues early. This results in trucks being repaired quickly with minimal downtime. All the data the driver sees on the dashboard can be seen by headquarters in real time, due to mobile connectivity, GPS and IoT devices embedded in the truck that monitor automotive processes.
The massive amount of data collected by IoT sensors on trucks is too much for one person to analyze. With machine learning software, we can scan millions of data points to instantly deliver answers to questions about company trucks and drivers, including geolocation, routes, scheduling, fuel status and a long list of other fleet concerns.
AI tools can improve driver behavior by monitoring driving speed and sharp turns to ensure the driver is practicing safe driving techniques. Transportation companies rely on drivers to follow traffic laws and arrive safely to their destination. If a driver is speeding, the company can automatically alert him or her to slow down to avoid penalties. Inspiring good driving behavior helps the company prevent violations and accidents, while minimizing vehicle wear and tear.
How Fleet Managers Use AI
Fleet management is evolving to include AI in multiple ways, often through a mobile app connecting all vehicles and branch offices within headquarters. Trucking companies can use AI to connect with drivers and monitor the vehicle’s performance: mechanical components, driving behavior and miles per gallon. They can monitor driving behavior through connected IoT devices to determine which drivers need more training.
The more heavily funded transportation companies are investing in self-driving trucks. The combination of using autonomous trucks that can be controlled remotely at the trucking company’s headquarters, as well as platooning helps cut costs and time. Several states have now passed laws that allow for autonomous trucks to roll together through connected smart technology.
AI has become an asset for backroom capabilities that may be too tedious for one person to complete manually, such as running system audits. Machine learning software is useful for making financial forecasts, including budgeting and predicting the life cycle of a truck.
Not only can AI speed up tasks, but it processes information without errors, thus ridding of manual methods for accounting or scheduling. Deep learning software is a step further of advanced solution drawing data from multiple resources. This solution is capable of recognizing shipping patterns to predict pricing within the supply chain.
Inside the World of Machine Learning
Two of the main forms of AI already used by trucking firms are natural image processing and predictive analysis. Natural image processing software mines documents for data, reducing the need to input data into another system manually. Predictive analysis scans diverse data sources to make future predictions. Traditional billing is steadily being replaced by AI systems to generate thousands of invoices exponentially faster without human error. Invoice verification with suppliers through AI helps eliminate last century’s common problem: duplicate invoicing.
Studying driving records and behavior data allows a machine-learning-program to generate a list predicting which drivers are most likely to be in an accident. HR departments and supervisors can use this information for employee evaluations and determining which drivers to schedule for specific routes. The software can make predictions of when an employee will quit based on their company loyalty, satisfaction, and engagement.
Another use of predictive analytics is for recruiting, in which the computer can narrow down the list of candidates applying for a position based on experience and other customized parameters. Predictive analytics can further be used to forecast what the weather will be in the future on a given route.
Machine learning basically compares new data with an enormous archive of historical data, to detect difficult patterns for humans to discover. The computer’s “brain” can then use patterns to calculate the probability of frequency in reoccurring events. It can also analyze data for rerouting for certain types of weather. In this sense, the machine learning software can make recommendations to achieve greater efficiency and is also useful for developing training sessions.
Future of AI in the Transportation Industry
Thanks to Artificial Intelligence, trucking companies are venturing into AI for a growing range of reasons. Larger and advanced trucking companies are adopting self-driving trucks and connecting them through platooning. Competition is heating up in the electric vehicle (EV) manufacturing industry with pressure from industrial firms to speed up development of self-driving trucks.
The pandemic has triggered a wave of investing toward EV makers, as the future points to longer lasting batteries. Although EVs currently rely on lithium batteries, the future looks promising for solid state batteries to be the cleaner, more durable and powerful solution. The combination of a more sustainable battery, integrated with automation and connectivity, will allow trucking companies to track fleet data more accurately, detect and repair vehicles quicker, and enjoy higher profit margins.
AI generally is embraced in the transportation industry to improve efficiency in terms of cost, fuel, vehicle performance, and travel time. Investments in transportation AI are projected to grow significantly, from the low billions to about $10 billion by 2030. The largest segment of this investment is expected to be in deep learning technologies.
One of the keys for trucking firms to streamline operations with AI-enabled devices will be to partner with IT teams and cloud service providers that offer customized solutions such as designing a proprietary app. IT experts can help transform a trucking company’s infrastructure to a centralized digital hub that connects with mobile fleets and all departments. In essence, computers -to-computer communication will drive the evolution of automotive self-diagnostics.
The Road Ahead
Trucking companies that embrace Artificial Intelligence are poised to be the survivors of the next decade. Using AI empowers a trucking company to use automated processes, predict travel times, monitor inventory details and control trucks remotely, all in real time. The more data a firm can collect on its operations, drivers, and vehicle performance, the more it will be able to improve safety, fuel efficiency, and satisfaction among customers and supply chains.
