Reposted from: WeChat public account Geek Park
Leveraging big data and IoT technologies is the key to reducing systemic risks in trucking logistics.
Without a doubt, trucker is one of the most stressful occupations.
Overfatigue and danger have become key words of this profession. However, truckers are who this society relies on to keep 13 million logistics vehicles operating on roads and paths across China. The safety risk remains high in the rapidly expanding market in China, lingering as a pain point haunting the benign development of the logistics industry.
Technology has become the key to solving this worldwide problem. The evolving intelligent Internet of Things (IoT) and the improved computing infrastructure have increased the visualization and transparency of risk behaviors, enabling freight companies to embrace digitalization for smart safety management.
Values Delivered By Technologies
As a matter of fact, the trucking industry has long been plagued by the safety issue, since it has a direct bearing on corporate interests. A traffic accident not only deals a blow to the truck fleet, but also affects the insurers in follow-up procedures.
More critically, the trucker may lose his or her life as a result of the accident. According to G7 founder and CEO Zhai Xuehun, more than 20,000 truckers die in traffic accidents every year. This is far from a small figure considering the total heavy truck ownership of seven million.
A report released by PricewaterhouseCoopers (PwC) showed that, as of 2019, China's road freight industry recorded an accident rate of 3.7 accidents per 1 million kilometers, while this figure in the U.S. was around 0.1 accident per 1 million kilometers as early as 2014, as per the statistics of the U.S. Department of Transportation. Moreover, China's trucker mortality has been hovering around 1‰ for years, revealing a still-wide gap between China and developed markets in terms of road freight safety management.
The immediate causes of accidents always have something to do with the truckers. Zhai Xuehun mentioned that on the G7 Big Data platform, not a single accident was caused by objective reasons such as a brake failure. Rather, almost all accidents were related to driving behaviors, vehicle equipment, road conditions, and unexpected events among other factors.
This is something that a technology company can solve.
For example, the fatigue monitoring system for truckers is designed to send warnings to prevent a trucker from dozing off or losing focus while driving. But the system has a hard-to-ignore shortcoming in that even if the warning is sent promptly, an accident may happen as quickly as in mere seconds. In other words, it is impossible to cover all the high risk scenarios with just one system that is designed only for truckers.
As an IoT technology company, G7 looks farther into the future. G7 has released a "safety rating" feature based on its IoT Big Data and AI algorithms, which offers prediction on the overall and long-term risks for truckers and fleets.
G7 rates a trucker based on the historical data of the vehicle. The data is collected by various sensors on the vehicle in multiple dimensions, including the trucker's personal information and driving behavior, and the vehicle's driving environment, and then used to predict the trucker's risks of running into a future traffic accident. Such prediction is more than just about the danger in the next second, but about the risk in the following quarter or year.
G7's safety ratings on fleets ｜ G7
A combined perspective of the ratings of all truckers in a fleet can cast light on the overall risk level of the fleet. G7 divides risks into nine levels from high to low. The level has a bearing on the loss ratio of insurance services and can help refine the safety management of the fleet.
A report released by PwC disclosed that the insurance premiums of heavy trucks in China amount to more than 100 billion RMB. In addition to the increased spending of freight companies in insurance premiums, insurers usually compensate 80% of the losses. Proper safety management can reduce the overall loss ratio of accidents by 10% to 15%, and the risk management levels of upstream freight companies have a significant impact on the profitability of insurers. It is obvious that G7 intends to enter this market for a triple win among freight companies, insurance companies and technology companies.
According to Zhai Xuehun, new technologies can reduce the loss ratio for truck accidents by 20%-30%. An immediate benefit of this reduction is evidenced by the fact that, for the first time, technical costs were surpassed by the compensation reduction in the first quarter of 2020. In other words, financial gain created by the loss ratio reduction from the application of technologies has covered the cost of development for these technologies, demonstrating the most significant value that technologies can bring.
Data Is the Core
A universal problem with the logistics industry is that while some pain points are being solved, others surface. This is exactly how the safety issue of China's logistics and transportation industry emerged in the first place.
Zhai Xuehun has a clear view on China's market. He once said at the Geek Park IF Conference that safety solutions have become a new growth engine of G7 since
- Booming logistics companies purchase many trucks and hire many drivers. With their driving frequencies on the rise, safety risks are gradually piling up.
Accompanied by decreasing costs and increasing efficiency, the demand for safety management grows with accumulating risks, making safety solutions essential to freight companies. In an interview with Geek Park, G7 said that safety management solutions are what now customers knock on our door for, instead of being promoted like other products we offer.
How did G7 manage to achieve it? A large customer base is the basis for future development, but it is data that constitutes G7's core competitiveness when it comes to safety management.
Big Data and AI are nothing new. And after condensing the concept of AI, one gets modeling and machine learning. Although modeling and machine learning require profound techniques, they can be conquered with the help from technical professionals.
The most challenging part, however, is the access to data. G7 approached this problem from the GPS angle to get basic vehicle data by installing GPS devices on vehicles. As the number of sensors on a vehicle increases, the data collected also grows in size. In 2017, G7 launched G7 Safety 1.0 to assist vehicle safety management through technical means.
G7 founder and CEO Zhai Xuehun ｜ A photo from the scene
However, a driver fatigue monitoring system can only cover 20% of risk scenarios, as mentioned above. The safety technologies have evolved on from the GPS to the IoT. Unlike the former, the IoT features multi-dimensional sensors and other hardware devices that can collect all kinds of data on vehicles, truckers and the surrounding environment of vehicles. With the IoT, G7 can get more information to ensure safety.
Apart from data-collecting sensors, G7 also needs to pool all data into a shared network for systematic structuring. Xiang Wei, R&D vice president of G7, told Geek Park that they value two dimensions for data acquisition, namely data depth and data breadth.
Data depth means that data is not only collected, but also analyzed to deliver a result. In the past year, G7 has engaged in deep collaboration with the insurance sector and built access to the precise data of more than 10,000 accidents.
Precise accident data means more details. For example, the data includes the time when the accident happened, the specific location of the accident, such as on a national highway or other highways, and whether the driver stepped on the brake one minute before the accident. The cause of an accident can be deduced with the above result data, which lies hidden in the huge amount of data collected from the vehicle. Ultimately, accident patterns can be identified from the data of "causes" and "results".
Data breadth means that the sample cannot be biased. Individual truckers, medium-sized fleets, or small-sized fleets face varying situations. The data collected must embrace all facts including but not limited to the driving status of the vehicle, so as to cover this precise prediction system.
More importantly, G7 Safety has clarified its result-oriented goals as a safety solution. Changes in accident rate, death rate, and loss ratio are all results. There is the fundamental difference between a result and a technology. Only a service that combines the strength of software, hardware, algorithms, and operations can deliver a result.
