With the development of the Internet of Things, the real-time demand for massive amounts of information in all industries will also be enhanced. According to a consulting firm, the application of edge computing in transportation has the highest percentage of industry applications.
Ⅰ.Edge computing applications in transportation
As a huge system project, intelligent transportation covers various aspects such as vehicle-road cooperation, signal control, driverless, law enforcement monitoring and road maintenance.
Edge computing can be used for location-based applications in the field of transportation, such as the operation of intelligent public transport and the management of transport facilities in cities, where real-time processing and data collection based on location status can be carried out.
And in the application of video surveillance, edge computing can establish the computational model of video surveillance and improve the intelligent processing capability of the front-end camera of the video surveillance system, so as to optimize the early warning system and disposal mechanism.
Ⅱ. The advantages of edge computing in transportation in intelligent transportation are mainly reflected in the following three aspects.
1. Improve the local processing capability. The application of edge computing will significantly improve the intelligence and humanization of traffic management methods. Edge computing can complete data processing tasks independently and quickly in the field, thus meeting the high requirements for real-time in the field of transportation.
2. It will reduce the burden on networks and cloud computing platforms. Edge computing can autonomously process data locally and send the final processing results to the cloud computing platform, which not only speeds up data processing efficiency, but also reduces the burden of network transmission and the processing pressure on the cloud computing platform.
3. Data boundaries. Another important role played by edge computing in intelligent transportation is the data boundary. As a large amount of data generated by traffic conditions is not required for long-term storage, such as video surveillance footage, the discarding of such data can be done directly after data processing is completed, thus reducing the storage pressure.
There is no doubt that global enterprises are firmly adopting a more flexible, cost-effective and cloud-connected infrastructure as a key solution to simplify their digital transformation journey. concepts such as SD-WAN, SASE, ZTNA and Multi-cloud are becoming the hottest topics in the industry.
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