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%.

Monitoring roundabouts and signalized intersections in a road network is important, e.g., to reduce travel time and greenhouse gas emissions. The monitoring of such junctions is a challenging problem, and current approaches mainly use high-cost solutions for a selected few. In this work, we present a framework for the automated identification and monitoring of all junctions in a road network. The framework utilizes detailed trajectory data or high-level segment-based data to compute travel time and energy consumption for all turn directions. These metrics are then aggregated per junction to enable a fair comparison between roundabouts and intersections. The aggregated metric is used to provide an overview of all junctions and to pinpoint those performing poorly. An analysis of 1,394 junctions using 334,081 trajectories quantifies the different benefits of roundabouts and intersections, e.g., the travel time in roundabouts varies little, and turns are 21% to 155% more energy-consuming than going straight in intersections. Further, the aggregated junction metric makes it simple to monitor all analyzed junctions and detect the worst-performing. The analysis also clearly shows the benefits of trajectory data over segment-based data for junction monitoring.