URBAN TRAFFIC DATA COLLECTION AND TRAFFIC VIEW APPROACHES

Tóm tắt

Data quality is crucial in crowd-sourced traffic information systems. While expanding data collection methods is essential, ensuring accuracy and reliability is equally important. Conventional traffic information systems collect data through multiple methods such as web-based forms, speech data, and GPS sensors on mobile devices. This paper enhances these methods by introducing new approaches to traffic data collection from cameras that leverage existing city surveillance infrastructures to provide a continuous stream of real-time traffic data. A novel approach for background scheduling to update traffic status predicted from camera-based data applying AI models is proposed. Then we design necessary components to implement background data fetching scheme to insert traffic camera data into the existing traffic information system which is used to evaluate the proposed approaches in real-world scenarios. The results revealed the effectiveness and the efficiency of the proposed mechanisms showing that they are ready to be applied in real-world applications.