How AI Can Enhance Fleet Safety And Data Management (2024)

Originally, telematics provided basic functionalities like GPS tracking and simple data collection. Today, it has evolved into a sophisticated ecosystem integrating advanced features that have become industry standards, or "table stakes."

Facial recognition. Lane departure warnings. Real-time video monitoring. These are only a handful of the standard characteristics of what’s expected in this technology. However, this poses a serious challenge to fleet managers.

There is too much data from which to draw actionable insights. Fleet administrators are overwhelmed by the sheer volume of data and thereby struggle to identify patterns and make timely decisions.

Table-stake features can generate a massive amount of fleet safety data but are limited in their ability to draw meaningful conclusions. This requires an industry-leading solution that leverages AI to transform raw data into clear, actionable insights, enabling fleet managers to make informed decisions that enhance fleet safety.

Tourmo has the capability. It applies AI and machine learning models to detect patterns of driver misbehavior and deviations and predict future outcomes.

Now, companies can predict potential safety issues before they become critical to maximize fleet safety and performance. In this blog, we’ll investigate the impact of AI on fleet management, focusing on how it helps predict and prevent potential issues, ensuring safer and more efficient mobile workforce operations.

Establishing Industry Standards – Now Table Stakes

In 2022, the video telematics industry was valued at $810 million and is predicted to grow at a CAGR of 19% to 2027 (IoT Analytics, 2023). As the industry has expanded, certain features have become baseline requirements in video telematics:

• In-camera Advanced Driver-Assistance Systems (ADAS):These systems assist fleet drivers by providing alerts and support through in-camera technology.
• Driver Distraction Recognition: Technologies that identify when a driver is distracted, helping to mitigate fleet risks associated with inattentive driving behaviors.
• Facial Recognition:Used for driver identification and ensuring the right person is behind the wheel.
• Lane Departure Warnings:Alerts that notify fleet drivers when their vehicle begins to move out of its lane unless a turn signal is activated.

Fleet driver safety is paramount and non-negotiable. Consequently, these features are not expected, they’re demanded and have evolved to become baseline capabilities of modern fleet safety technology.

Fleet managers now have a vast array of data at their disposal, allowing them to make informed decisions that enhance driver safety.

Yes, in theory.

But in reality, fleet managers have so much data to sift through they struggle to identify patterns of misbehavior, and separate critical information from noise.

This introduces a new hurdle: data overload.

The Challenge of Data Overload

Video safety technologies gather information 24/7. A McKinsey study estimates that a connected fleet vehicle will create up to 25 gigabytes of data per hour (Automotive Fleet, 2020), the equivalent of nearly 30 hours of HD video playback.

Some commentators believe that autonomous vehicles of the future will create up to 4,000 gigabytes (GB) of data per day, the equivalent of over 1,400 terabytes of data per year, from a single vehicle (Information Age, 2018)

This fleet data is relentless, and it’s flooding fleet managers with an array of data that leaves them perplexed and overwhelmed. Here’s an example.

In vehicle A, the engine temperature might be rising at an alarming rate. Simultaneously, vehicle B could also be exhibiting erratic driving patterns. This is all happening whilst vehicle C’s fuel efficiency has dropped by 47%.

What action must the fleet safety manager take?

This is fleet data overload and critically unmanageable. Instead of addressing and overcoming each challenge immediately, fleet managers are forced to sift through mountains of data to find actionable insights.

Driver safety is compromised because delays in addressing urgent alerts can lead to severe road incidents. When managers are buried under data, critical warnings may go unnoticed or unaddressed until it’s too late.

What’s more, the time and resources spent managing this fleet data can detract from other essential duties, such as:

• Optimizing delivery schedules
• Conducting a driver training program and
• Performing regular vehicle maintenance.

This not only impacts operational efficiency but also increases the risk of accidents, breakdowns, and costly repairs.

Overcoming the data overload challenge requires fleet managers to adopt an AI-driven solution that can intelligently filter, prioritize, and provide actionable insights in real-time, ensuring both safety and efficiency are maintained. Identifying trends to predict the future has become a necessity, not a nice-to-have.

Using Tourmo to Gather Actionable Insights Via AI

Tourmo is an AI-driven solution that gathers fleet insights, analyzes them in real-time, and provides actionable recommendations to optimize fleet performance and safety. Not only does Tourmo’s advanced AI predict potential driver safety issues, but it also intervenes in real-time to prevent vehicle accidents.

Every fleet will have drivers who exhibit certain patterns of behavior that increase the likelihood of an accident occurring. Identifying this pattern and addressing it can dramatically improve overall fleet safety.

This was recognized by one of the largest package delivery companies in the world, UPS. They identified that left-turns were often more time-consuming than right-turns, especially at congested intersections where fleet drivers need to find a gap in the traffic. Left turns also involve crossing lanes of oncoming traffic, increasing the potential for collisions.

In response, they implemented a ‘no left-turn’ policy, restricting drivers from making left turns at intersections, barring a few exceptions. UPS claims that this fleet policy has led to:

• Using 10 million gallons less fuel annually
• Emitting 20,000 tonnes less carbon dioxide
• Delivering 350,000 more packages each year
• Cutting the number of trucks by 1,100
• Reducing the total distance traveled by 28.5 million miles

Source: The Independent, 2017

It’s a case in point of how clamping down on a single pattern of behavior can transform overall fleet performance and reduce operational risks.

Empowering Decision-Making with AI and Predictive Capabilities

The role of AI-driven pattern recognition is to transform raw data into actionable insights. With this capability, fleet safety managers can detect and diffuse risks before they arise.

Tourmo® AutoPilot accomplishes this entire process for you. Instead of losing yourself in a sea of fleet data, Tourmo will aggregate, contextualize, analyze, and make sense of it in real time.

Hidden patterns will be identified. Risky driving behavior will be pinpointed. And advice on what decisions to make will be provided. Here’s how Tourmo’s AI predictive capabilities enhance strategic decision-making:

• Proactive Safety Measures: Monitor driver behaviors such as lane drifting or erratic driving to detect signs of driver fatigue or distraction. Alerting fleet managers to these patterns allows them to take immediate corrective actions, such as scheduling rest breaks or additional safety training, thus preventing accidents before they happen.
• Optimized Route Planning:Analyzing traffic data and historical route performance to help fleet managers identify the most efficient routes, thus reducing travel time and fleet fuel consumption. This level of fleet data insight enables strategic route planning that minimizes risks and enhances fleet operational efficiency.
• Fleet Maintenance Scheduling: Forecasting vehicle maintenance needs by identifying patterns in engine performance, fuel efficiency, and wear and tear. This allows fleet managers to schedule maintenance proactively, preventing breakdowns and extending the fleet's lifespan.
• Driver Performance Improvement: Tourmo continuously monitors driver behavior, such as speed, braking patterns, and compliance with safety protocols. This fleet safety data provides insights into areas where drivers can improve, helping develop targeted training programs within the fleet safety program that enhance driver performance and safety.
• Regulatory Compliance:Proactively ensure fleets adhere to safety regulations by identifying potential compliance issues early and preventing violations. This approach not only helps fleets avoid fines but also ensures adherence to the fleet safety policy, fostering a robust safety record.

Using Tourmo, fleet managers will be proactive in anticipating and resolving fleet issues before they escalate. Yes, data is important because it can predict future outcomes. But the answers remain hidden within this sea of information. Tourmo’s AI-driven capabilities deliver these fleet safety insights faster and more accurately than a human can, making it an integral part of effective fleet safety programs.

Real-World Applications and Customer Success Stories

Tourmo’s modern capabilities have helped a myriad of companies enhance fleet safety. It’s a powerful, fast AI solution that enables fleet managers to make sense of their fleet data and make fast decisions. Let’s examine this in more detail below.

How Tourmo’s AI Solutions Improved Driving Behavior And Fleet Safety

A leading international public transport operator faced significant challenges managing fleet safety and operational efficiency due to fragmented telematics and IoT systems. These disjointed solutions led to inefficiencies, compliance issues, and inaccurate data.

The company adopted Tourmo’s AutoPilot Sync, Drive & Work modules to unify data streams, provide real-time actionable insights, and improve decision-making. Tourmo implemented three key AI solutions to overcome these challenges:

• Tourmo Sync: Reprocesses fleet data from various systems and devices to normalize and calibrate information, creating unified dashboards with accurate, actionable insights for stakeholders.
• Tourmo Drive: Improves driving behavior by contextualizing scores and providing positive reinforcement, resulting in substantial fleet fuel and maintenance savings.
• Tourmo Work:Automates communication of driving behavior analysis, compliance, and events directly to drivers, increasing fleet productivity and safety and improving relations with drivers and third-party operators.

With the fleet data gathered, the company quickly extracted key insights and made crucial data-driven decisions that enhanced its fleet's overall safety and efficiency.

Results

Find out how Tourmo’s innovative AI solutions addressed safety and operational challenges for a major public transport company. Read the complete case study here.

How a Leading Oil and Gas Company Enhanced Fleet Data Management With Tourmo

One of North America's top three oil and gas companies was facing challenges in managing its fleet's safety and operational efficiency due to fragmented telematics and IoT systems.

The fleet data it gathered was inaccurate and disjointed due to not having a unified platform. This led to:

• No access to actionable insights
• Too much time trying to make sense of the data
• Failure to aggregate data correctly

To overcome these challenges, the company adopted Tourmo’s AutoPilot Drive ∓ Work. This involved leveraging three Tourmo AI Platform solutions:

• Tourmo Sync: Reprocessed and normalized fleet data from various systems and devices. Provided unified dashboards for all stakeholders.
• Tourmo Drive:Improved driving behavior with contextualized scores and positive reinforcement. Empowered drivers with weekly scorecards and tips. Enhanced data sharing across third-party fleets via updated apps.
• Tourmo Work:Digitized and automated manual workflow processes for policy compliance. Assigned tasks based on location, behavior, and events. Improving productivity, compliance, and workflow transparency across departments.

Through Tourmo’s holistic AI fleet management capabilities, this oil and gas behemoth was able to take control of its fleet data management. Gathering, analyzing, and drawing conclusions from data at speed gave them the agility to implement strategic decisions that enhanced fleet safety and efficiency. Here are the results:

To learn more about how Tourmo transformed fleet management for a top oil and gas company, access the full case study here.

Tourmo: The AI Linchpin To Maximized Fleet Safety and Management

Fleet safety and efficiency hinge on speedy data-driven decisions. Data is problematic because we have so much of it. Fleet managers are often overwhelmed by its sheer volume, requiring them to invest huge amounts of time to uncover hidden patterns.

The time spent on this task can have collateral damage. Other patterns will be ignored, or the delayed analysis could result in missed opportunities to mitigate risks before they become serious problems. It’s a cumbersome process that leaves fleet managers heavy-footed in their decision-making.

Industry-standard software will have all the prerequisites of modern video telematics to gather data. However, it will not be enough to help managers make sense of the data.

This requires an AI fleet management solution that goes one step further - a sophisticated and powerful software to process, analyze, and interpret data at maximum efficiency. Tourmo provides all of these features and more. Its AI-driven capabilities identify critical patterns and deliver actionable insights in real-time.

All risky driver behaviors are laid on a canvas for fleet managers to assess, allowing them to intervene before these threats escalate into major incidents.