Quality of Hybrid GNSS Sampling Methods

Today it is simple to collect and transmit GNSS data from vehicles with a high frequency. However, there is a storage and processing cost related to handling the data. Further, some data has limited value, e.g., redundant GNSS data from a vehicle stopped at an intersection. In this paper, sampling methods for GNSS data focusing on time, distance, speed, and heading changes are systematically analyzed. The goal is to retain only valuable data. A set of metrics is proposed to quantify the value of the data, e.g., no redundancy and retention of the spatial and temporal distributions. An existing commercial approach to GNSS-based travel time computation in road networks is used to measure if the sampled GNSS is accurate for this important purpose. The results show that sampling methods using individual properties, such as time, space, or speed, have their own strengths and weaknesses. However, with hybrid methods, it is possible to retain the strengths and eliminate most weaknesses. Using a large, real-world GNSS dataset, we show that a hybrid method that retains only 20 % of the original data can achieve travel time estimation with an error of just 1.0 – 1.3%.