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AutoNavi releases a large model for safe travel to improve online ride-hailing safety management capabilities
AutoNavi releases a large model for safe travel to improve online ride-hailing safety management capabilities
Amap recently announced the launch of a safe travel model, which uses Amap’s advanced technologies such as map big data, location big data, navigation big data and intelligent decision-making systems to Help online car-hailing platforms improve safety management capabilities and reduce safety risks
Safe Travel Big Model uses AutoNavi's big data technology to conduct risk identification, risk warning, real-time protection, normal governance, etc. for online car-hailing platforms full support. By identifying various risk scenarios such as dangerous traffic environments, poor driving, abnormal itineraries, and drunk driving, the safe travel model can provide real-time risk warnings to online ride-hailing platforms and remind drivers to avoid risks in a timely manner through various methods.
According to reports, the safe travel model has been successfully connected to more than 100 online ride-hailing platforms, and can complete more than 10 million road safety warnings every day, reducing driver speed limits by 18.4%. In addition, based on the driving behavior portrait formed by the large model, the online car-hailing platform can systematically manage drivers who frequently exhibit bad driving behavior, and organize more than 1,000 driver training sessions every month to urge improvements in driving behavior
Amap not only helps online ride-hailing platforms improve their safety management capabilities, but also provides passengers with a number of safety measures. These measures include 110 one-click alarm, emergency contact, itinerary sharing, IVR prompts and other real-time protection functions, as well as the comprehensively upgraded "Fare Bodyguard"
"Fare Bodyguard" is Gaode's response to "Dare to sit and dare" An important upgrade of the "compensation" service commitment, providing users with multiple guarantees such as real-time escort, second-level processing and advance compensation. It intelligently identifies and automatically intercepts orders with abnormal fares, and synchronizes the online ride-hailing platform to prompt the driver to review the fare, discovering unreasonable fares before passengers do, and automatically reducing them. When passengers discover fare questions on their own and provide feedback in the Amap App, Fare Bodyguard can make judgments in seconds, compensate unreasonable fares in seconds, and provide fee explanations in seconds for orders with normal charges.
According to reports, AutoNavi has now been able to handle 90% of fare issues within seconds, and promises to make advance compensation within 36 hours. This puts Amap in a leading position in the industry. In order to ensure that relatively complex fare issues can be handled in a timely manner, AutoNavi promises to complete the processing of all fare issues within 36 hours. If the processing times out, no matter who is responsible, compensation will be paid in advance.
Amap’s efforts in ensuring users’ travel safety are worthy of our recognition. I believe this will also push the entire online ride-hailing industry to pay more attention to and strengthen safety management, providing users with safer and more reliable travel services.
The above is the detailed content of AutoNavi releases a large model for safe travel to improve online ride-hailing safety management capabilities. For more information, please follow other related articles on the PHP Chinese website!
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