With the development of road transportation in China, the focus is shifting towards intelligent infrastructure. However, due to variations in information systems between roads and incomplete network coverage, key monitoring points are not optimally placed, and the value of road traffic data remains untapped. The absence of an efficient and advanced unified video monitoring platform has resulted in disorderly and chaotic conditions. Smart highways are crucial for national development, and the transition from fragmented smart transportation to integrated systems is an inevitable trend.

Smart transportation encompasses a wide range of data, including real-time bus data, elevated ramp operations data, and real-time traffic congestion data. When referring to big data in smart transportation, we are mainly addressing the Traffic Integrated Command Platform. As one of the four major information platforms in the technological and informational planning of the Traffic Police Department, it aims to enhance the level of scientific and modern management systems for traffic law enforcement. By integrating road traffic monitoring, decision-making, control, and services into a unified command platform, it enables intelligent perception of road traffic conditions, dynamic traffic analysis and dissemination, and efficient deployment and command of law enforcement personnel, thereby enhancing comprehensive traffic command capabilities.

Challenges in Massive Data Storage for Smart Transportation

Typically, digital monitoring systems for road traffic consist of three main components: road surveillance front-end, transmission networks and port devices, and road monitoring command centers. The front-end devices include cameras, supporting pan-tilt-zoom mechanisms, and servers. Among these, the network video server acts as the core device, connecting to cameras via ports, decoder or alarm sensors via RS232 ports, and network port devices via RJ45 ports. It captures video images from the road scenes, compresses and encodes them, and then uploads them to the road monitoring command center. However, with the transition from analog to IP and AI cameras, data volume has exponentially increased. All videos and images captured by high-definition front-end cameras need to be stored and processed, which puts a heavy burden on the storage system.

Challenges in Structured Analysis of Road Video Data

Road traffic video data can be classified into structured and unstructured data. Structured data refers to information extracted from video frames, such as people, vehicles, objects, colors, numbers, and other attributes. After being structured, the data is stored in dedicated repositories, significantly reducing storage requirements. By using AI cameras for front-end analysis, it becomes possible to effectively identify vehicles, non-motorized transport, pedestrians, and gather information on traffic flow. Such enforcement data can be stored in the storage system, assisting law enforcement agencies with inspection, control, incident handling, analysis, situational awareness, emergency command, and information dissemination.

Gooxi Intel Whitley Dual-socket Standard Server

Gooxi Intel Whitley dual-socket standard server, designed with 100% availability, proves to be an ideal choice for storage servers:

——All hardware components have achieved ultimate redundancy, and the underlying RAID adopts cutting-edge virtualization technology, enabling fast data recovery.

——In terms of expansion, it supports a maximum of 7 high-speed PCIe expansion slots, providing ample scalability.

——Regarding performance, it supports 1/2 third-generation Intel® Xeon® Scalable processors, with 16 DDR4 memory slots, and a built-in M.2 slot for high-speed caching.

——In terms of storage, the 4U36 drive bay offers a maximum storage capacity of 724TB, effectively accommodating massive data storage.

Gooxi's Intel Whitley standard servers are designed for storing a large number of small files in specific scenarios such as traffic violations, speeding, and traffic accidents. They also support new business systems that require big data retrieval and analysis. With a distributed data and metadata storage architecture, integrated intelligent data processing engine, and features like video structuring, image processing, and facial recognition, the servers handle intelligent processing internally, simplifying application development and significantly improving efficiency.

Gooxi's data storage solution addresses the challenges of high-capacity storage, structured storage of surveillance videos and traffic data, and the difficulty of system expansion in traffic monitoring systems. Gooxi aims to provide a solid foundation for China's smart transportation development by continuously innovating its technological capabilities and combining specific scenarios with intelligent technologies to contribute to the digital transformation of road systems.