Traditional machine learning (ML) models can be effectively integrated into modern traffic incident detection systems by leveraging decentralized optimization frameworks like Network Lasso. This approach allows for the training of conventional ML models in a distributed manner while ensuring global convergence and knowledge sharing among nodes. By utilizing techniques such as one-class support vector machines (OC-SVM), these traditional ML models can detect anomalies in traffic data, such as incidents, with high accuracy. The integration of traditional ML models with decentralized data governance ensures that local data sources are utilized efficiently without compromising on model performance.