Prediction of Wide Area Road State Using Measurement Sensor Data and Meteorological Mesh Data

In this paper, prediction of wide area road state using actual measurement data on on-board environment sensors and meteorological mesh data are introduced. Actual sensor data are sampled from running vehicles gathered to roadside edge computer. On the other hand, those meteorological mesh data are collected into the cloud computing from Meteorological Agency as open data. By integrating those actual data and the meteorological mesh data and applying Kalman filter technology, temporal and spatial prediction of wide area road state can be realized and visualized on GIS viewer system. In this paper, the fundamental system configuration and both temporal and spatial prediction algorithms are discussed.