AI System that predicts traffic conditions

UNIST scientists have recently developed an interactive visual analytics system that enables traffic congestion exploration, surveillance, and forecasting based on vehicle detector data. Their system can predict traffic conditions for the next 5 to 15 minutes at an error rate of fewer than four kilometers an hour. This intelligent visual analytics system empowers traffic congestion exploration, observation, and determining dependent on vehicle detector information. Through domain expert collaboration, we have extricated task requirements, consolidated the Long-Short Term Memory (LSTM) model for congestion forecasting, and designed a weighting technique for distinguishing the reasons for congestion and congestion propagation directions. The system then visualized the traffic situation for easier comprehension: Congestion levels and average driving speed, for instance, are described using colors and shapes.