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NTRODUCTION

The adoption of AI in transportation is growing rapidly, with the global AI in transportation market size expected to reach $10.3 billion by 2030. Advances in Artificial Intelligence (AI) have made the days of designing a better log or a better spreadsheet increasingly irrelevant. Fleets who choose not to adopt new technologies like AI risk losing their competitive space in the market to reduce risk and lower losses. Solutions like Idelic’s Safety Suite Platform provide a comprehensive approach to identifying risky drivers along with the corresponding coaching tools to supercharge the effectiveness and efficiency of safety teams like never before.

Artificial Intelligence (AI) Basics


AI is an umbrella term for a computer software’s ability to perceive its environment, attempt to mimic human behavior, and learn to accomplish a given goal. This broad terminology has opened the door for many companies to oversell their solution’s AI capability. Often, what is marketed as AI might merely be traditional systems employing basic algorithms, such as straightforward if-then rules or decision trees, lacking the sophistication typically associated with true AI technologies

AI can be a highly technical and powerful tool for analyzing and gaining insights from vast amounts of data. Machine Learning (ML), the foundation of advanced AI, is often used interchangeably but is vastly different and the actual technology that’s profoundly impacting the way safety teams perform day-to-day operations.

Machine Learning

Artificial Intelligence (AI) is a broad discipline aimed at creating machines capable of tasks requiring human intelligence, including problem-solving and language understanding. Machine Learning (ML), a subset of AI, employs algorithms and statistical models for computers to learn from data and improve task performance without explicit programming. While AI seeks to replicate human cognitive abilities comprehensively, ML focuses on enabling machines to autonomously analyze data, make decisions, and predict outcomes.

Artificial Intelligence (AI) and Machine Learning (ML) are used interchangeably but they are not the same thing. Let’s break it down through a couple of analogies.

AI: Like a Versatile Truck Driver

Think of AI as a versatile truck driver. This driver is skilled not just because of the rules of the road they’ve learned, but also because of their ability to handle a variety of situations. AI systems are designed to solve problems and perform tasks that would typically require human intelligence. This includes understanding language, recognizing patterns, or making decisions. AI encompasses a broad range of technologies, some of which may use simple predefined rules, while others, use more sophisticated technologies like ML to learn from data.

ML: Like a Smart GPS System

Machine Learning, a subset of AI, is akin to a smart GPS navigation system. Unlike a standard GPS that relies on fixed maps and routes, a smart GPS learns and adapts. It analyzes real-time data, such as traffic flows, road conditions, and construction updates to suggest the most efficient routes. ML works in a similar manner. It processes vast amounts of data, learns from this data over time, and makes predictions or decisions based on its learning. This makes ML dynamic and responsive, constantly improving its performance based on new data.

In essence, while AI provides the framework for creating systems that can perform intelligent tasks, ML gives these systems the ability to learn from data and improve over time. For the trucking industry, this means more than just following the ‘rules of the road’, it’s about adapting to the ever-changing conditions of real-world logistics, leading to smarter decisions, enhanced safety, and more efficient operations.

Identifying Metrics That Cause Accidents

ML uses vast data to predict outcomes by recognizing behavior patterns. It requires two historical data sets:

  • Input data (like past accidents, highway observations, Hours of Service etc…) 
  • Outcomes (such as at-risk drivers, preventable accidents, liability costs)

The ‘training’ process distinguishes vital predictors. For transportation safety, ML assesses driver behaviors and runs numerous simulations, adjusting each data point’s impact to optimize results. Ultimately, the sophisticated model identifies at-risk drivers. Achieving such deep analysis without advanced computing is unattainable, showcasing ML’s true strength.

Creating a Machine Learning Model

Deploying an ML model involves training and production stages. During training, data scientists use historical data, converting raw inputs into computer-readable forms via natural language processing. After processing, they select an appropriate model type, like neural networks, based on the data’s characteristics and desired outcomes. Though training can be time-consuming, the finalized model processes new data quickly. Its accuracy is contingent on data quality, feature extraction, and algorithm selection.

What This Means for Fleets


Fleets do not need to employ their own data science team to reap the benefits of ML. Idelic’s data science team is fully equipped to handle meticulous backend work, which gives you instant results based on your past data. Having quality systems that capture all behavioral information around drivers help you predict accidents without any extra effort on your end.

Deep Learning

In trucking, fleets harness integrated data to analyze past events, discern patterns, and predict future accidents. As more data is incorporated, prediction accuracy can improve, but the complexity also rises, sometimes surpassing traditional statistical methods. This necessitates advanced techniques like deep learning. One notable architecture, recurrent neural networks (RNNs), lets models assess a driver’s accident history without needing preliminary data summarization methods like natural language processing.


What This Means for Fleets


Fleets working with systems that utilize deep learning processes can start gaining valuable insights from their data without having to convert it into a format that a model can understand. Idelic will work with you to ensure accurate fast data-driven insights are at your fingertips.

📈  Advantages of Deep Learning:


Enhanced Feature Extraction: Deep learning excels in automatically identifying and extracting relevant features or signals from data that might be missed by human experts or more simple models. This is a significant advancement over traditional methods that require extensive manual feature engineering.

Superior Scalability: Deep learning has capacity to not just handle, but thrive on large volumes of data. As the dataset grows, deep learning models often show improved performance. This scalability is crucial in our data-rich world, where the volume, velocity, and variety of data are continuously expanding. 

Versatility in Data Handling: Deep learning demonstrates remarkable adaptability in processing various types of data, including unstructured and diverse datasets like images, text, and audio.

Continuous Learning and Improvement: With the right infrastructure, deep learning models can continuously learn from new data, refining their accuracy and effectiveness over time.

Predict Accidents with Machine Learning


Idelic’s Safety Suite Platform uses ML algorithms to predict which drivers are most likely to get into an accident within the next 90 days. While AI, ML, and other predictive analytics may seem confusing, integration and automation can simplify a fleet’s ability to identify risk and proactively coach drivers to avoid unnecessary losses.

Integrated Systems

Idelic’s ML utilizes driver data that fleets already have. Integrating safety systems into one place, with Idelic’s Safety Suite Platform, provides clean and consistent data for natural language processing to occur, which makes it possible for ML to measure behaviors and predict accidents. Whether a fleet has very few data points or a significant number of disparate systems, Idelic has the tools to make an ML model tailored to the specific needs of each fleet.


Additional useful metrics that the Idelic Safety Suite Platform integrates include learning and training management, background checks, drug and alcohol tests, asset management, and sleep apnea compliance. Once all of this data is captured and integrated, ML methods can then be applied.

🧑🏻‍💻  Data sets that are the most valuable for data scientists include:


  • Accident RecordsPast accident records give essential information needed for the models to function as accurately as possible.
  • TelematicsUsing telematics allows for further insights on in-cab behaviors and issues that persist behind the wheel.
  • Onboard Video SystemsSupplemental to telematics, onboard video systems are used to reinforce conclusions derived from the data and eliminate any noise from non-coachable events.
  • HR SystemsHR systems capture driver tenure, policy violations, and feedback or complaints that result from a driver’s behavior.
  • CSA ViolationsSevere violations are a reliable indicator of an at-risk driver and improve the accuracy of the model.

 

Algorithms Based on Past Data

Predicting at-risk drivers starts with analyzing pre-accident data. The model estimates drivers prone to accidents and measures accuracy by comparing predictions to actual outcomes. Based on the results, it adjusts inputs and recalculates. Through countless iterations, the model ‘learns’, refining its focus on events leading to accidents.

Safety managers can modify these predictions considering specific situations. For instance, if an incident involving a driver wasn’t their fault, managers can reclassify that event, and the model adapts. If the driver’s response, though not perfect, was their best effort, adjusting the data’s classification alters its significance in the model.

As fleets collect more data, the model perpetually refines itself. Real-time driver data feeds into this model, alerting safety teams about potential risks before an accident occurs, giving safety teams the time needed to address risky behavior and get drivers back on the road safely.

Driver Watch List

Attract, Retain and Recognize with World-Class Coaching

Once the integrated data runs through the ML models, the system determines which drivers are at-risk for an accident and assigns them a score. The Idelic Watch List is simple, intuitive, and includes details on a driver’s safety events, risk level, and breakdown of their score. These processes occur behind-the-scenes so that safety teams can leave the technical jargon aside and easily make sense of the data.

ML processes enable safety managers to pinpoint who their at-risk drivers are and which safety events are causing a low score. With proprietary ML techniques, this type of data can be easily displayed by percentages, which are a powerful tool for understanding the cause of the driver’s increased risk and analyzing the best course of action for coaching.

Performance Improvement & Accident Prevention

The actions taken to prevent accidents are just as critical as the ML Watch Lists that are predicting at-risk drivers. Driver Watch Lists not only display the most at-risk drivers but also help identify potential behaviors that need correction before an accident occurs.

Assigning targeted Professional Development Plans (PDP) based on specific driver events and behaviors is vital to preventing accidents. The Safety Suite Platform allows users to assign targeted training and coaching, manage dates and timelines efficiently, and carefully track driver improvement. 

When a group of drivers incurs similar safety events, PDP templates allow safety managers to quickly implement training and improve driver safety across an entire fleet. If a driver has a problem unique from the rest, tweaking plans for specific training needs is easy. The ML model’s continuous analysis of driver risk scores allows for the most up-to-date and accurate Driver Watch List.

Benefits of Machine Learning and Predictive Analytics 

🎯 More Accurate Driver Risk Assessment


The top fleets leverage ML for enhanced safety and observe tangible benefits. Traditional scorecard systems evaluate a driver’s telematics speeding events within set timeframes, possibly comparing them to other event types. However, Idelic’s ML model comprehensively analyzes such an event in context with all other factors: driver’s tenure, speeding violations, accident history, and more, even uncovering less obvious risk indicators. By juxtaposing a new event with these factors, ML discerns intricate risk connections that a conventional scorecard might miss. 

🤖 Reduced Human-Error


People often seek information confirming their beliefs, a tendency called confirmation bias. This can lead to inaccurate conclusions. Safety managers, being human, might inadvertently focus on specific data, overlooking major issues. Idelic’s Safety Suite Platform ML capabilities help reduce bias providing more meaningful insights.

📉 Reduced Accidents


A practical and compelling example of ML in fleet safety is predicting at-risk drivers and potential accidents without any bias. With rich and complete sets of integrated data compiled into predictive models, ML allows safety managers to assign appropriate and necessary training by automatically flagging at-risk drivers at the click of a button.

⚡️ Efficient Operations


Safety teams often operate with limited resources and need to maximize their efficiency. Idelic’s Safety Suite Platform integrates disparate data sources into a single system providing real-time insights into driver behavior, enabling proactive driver safety intervention and risk identification. By integrating data from cameras, telematics, training and more, fleets can reduce manual data entry and the need to sign into multiple different systems to understand fleet risk, thus reducing errors and saving valuable time. Automated workflows and predictive analytics empower fleet managers to make informed decisions swiftly to reduce accident rates and optimize fleet operations.

🚚 Improved Driver Retention


With high turnover rates and an ongoing driver shortage, it’s increasingly difficult for fleets to keep experienced and trustworthy drivers. Idelic’s Safety Suite Platform enables safety teams to partner with drivers through customized multi-week coaching plans. This approach is more effective than punitively addressing individual incidents because drivers have the ability to self-correct their behavior before an accident occurs, helping to build a culture of trust and teamwork.

💪 Stronger Safety Culture


The best Safety Award Programs incentivize driver safety and take a proactive approach to help at-risk drivers become award winners. Using current fleet data (telematics, cameras, violations/citations, incidents, HR, and other safety metrics) in conjunction with an ML model to monitor drivers, fleets can see drivers who need additional coaching as well as low risk drivers. This information can be combined with other performance indicators to better determine who is eligible for recognition and awards.

⬇️ Lower Losses


Safer fleets naturally see lower losses. Fleets that adopt ML and predictive analytics to efficiently surface and address risky behavior see a reduction in crashes almost immediately. By using ML to identify behaviors that result in crashes, quickly correcting those behaviors, and preventing accidents before they occur, fleets are seeing a positive impact to their bottom line.

The Safest Fleets are Rapidly Adopting ML and Predictive Analytics


With the correct implementation of ML and predictive analytics fleets can significantly mitigate risk, prevent accidents, and lower losses. 

Fleets using Idelic’s Safety Suite Platform are ahead of the curve ensuring the safety of their drivers and maximizing the efficiency of their teams. With 75+ industry leading integrations, Idelic helps fleets make the data they already have exponentially more valuable. 

Idelic’s AI and ML uncover new driver behavior insights hidden within complex fleet data, making proactive intervention before an accident occurs a reality. Fleets who continue to use outdated methods of identifying risky drivers and training to incidents that have already occurred will ultimately find themselves behind. 

Ready to learn more about Idelic’s risk prediction capabilities and driver coaching solutions? We’d love to speak with you, contact our team.