Relying on decentralized data governance for traffic incident detection has several implications. Firstly, it allows for local nodes to train their own detection models using locally accumulated data, which promotes better generalization across diverse traffic regions. Decentralized data governance also enables swift and precise incident detection by leveraging knowledge sharing among neighboring nodes within a networked graph structure. This approach reduces the reliance on centralized servers and minimizes communication delays or failures between nodes.
Furthermore, decentralized data governance ensures that each node retains control over its own data while still benefiting from insights shared by other nodes in the network. This not only enhances privacy and security but also facilitates collaborative learning without compromising individual node autonomy.