Real-time road traffic fusion and prediction with GPS and fixed-sensor data

摘要

GPS devices offer new opportunities for short-term traffic prediction, especially in arterial road networks where traditional fixed-location sensors are sparse or unavailable. However, GPS data is often sparse both temporally and spatially. On its own, it is often insufficient for real-time traffic prediction. Hence, we consider the fusion of two types of data for the purpose of real-time traffic fusion and prediction: GPS data that is provided as point speeds, rather than trajectories, as well as (non real-time) traffic data such as is available from fixed sensors.