When speaking of big data in fleet management, the first topic that will come to our mind is telematics. The foundation of telematics is the technology of collecting, storing, sending information between end users and vehicles through telecommunication devices. Big data use cases in telematics extends the usefulness of that data.
Without the platform of data mining and data analyzing, the achievements of monitoring the live location of vehicles, planning optimized routes, providing online or offline assistant to drivers, and supporting telematics-related industries (such as auto insurance) and so on could be unrealistic.
So, what types of valuable services can big data bring to the telematics world?
The number of possible routes that a truck could take is extremely large. Back in the day, for logistics companies, the process of planning a delivery path was done manually and was very time consuming. Besides, without real-time traffic conditions, it was difficult to provide accurate ETAs and save driving time on the road.
Highway traffic in Mexico City
To make the driving path more efficient and more optimal, big data relies on a number of key types of data to evaluate the best route based on the consuming time and fuel usage in a few seconds:
Then, the optimized route is generated and displayed for the driver.
Route optimization is a big deal for fleet management, not only because reduction of driving distance and time saves the business millions of dollars, but also because the decrease of fuel consumption brings less environment impact. Learn more about fleet optimization here.
Telematics devices collect an enormous amount of engine data, such as engine RPM, engine oil level, transmission, mileage driven, tire pressure, and more. Based on all the engine data and the historical records of maintenance and repair, big data predictive analysis could provide us with precautionary breakdown and maintenance notifications, as well as the recommended solutions.
By acquiring advance visibility into potential vehicle health issues, the fleet can make the appropriate arrangement between downtime and work-time for vehicles to reduce the likelihood of an unexpected breakdown on the road. Of course, this will save the company both money and energy.
It’s often said that people are a company’s most important resource. Driving safety and road safety have alway been and will always be the highest priority in the transportation field. Running a large, global fleet makes managing safety extra challenging. So, analyzing driver behavior is essential both for fleet management, as well as the drivers themselves.
It is commonly accepted that a better understanding of driving behavior is helpful in developing more appropriate safety policies, more intelligent driving guiding systems or coaching systems, and most importantly, reducing the rate of accidents, while protecting company property and reputation, and drivers’ lives.
In the past few years, more and more in-depth machine learning and artificial intelligence algorithms have been perform to investigate drivers’ driving behaviors and styles.
Help cultivate better drivers. A long list of driving behaviors could be detected and analyzed through designed intelligent algorithms.
Some driving behaviors measured by telematics include:
The results could be used to develop more targeted and effective driving coaching systems to instruct drivers on better driving habits. Read about the GO TALK in-vehicle verbal coaching tool for drivers.
Lower the risk of accidents. Telematics technologies give us the opportunity to collect unlimited data on driving behavior and accidents. Discovering the relationship between accidents and actual driving patterns helps pinpoint high risk drivers that could have potential accidents, providing an opportunity to take action before it’s too late.
Support risk management. Big data analysis on driving behaviors can also make a great contribution to fleet risk management. Insurance companies rely on traditional factors such as car exterior, number of tickets, living area, driving distance, etc to determine the insurance policy. However, building accurate risk models based on actual driving behavior could provide a more realistic and precise assessment for the whole fleet.
There are more and more benefits that big data can bring to telematics and the fleet world. However, as Gary King, Harvard University has said, “Big data is not about the data.”
To see examples of what else you can learn with big data and telematics, please see these special reports:
Predicting Traffic Congestion with Driving Behavior
Using Big Data for Road Safety: A Safety Analysis Based on Geotab Telematics Data
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