Reconstructing Uncertain Pedestrian Trajectories From Low-Sampling-Rate Observations

The ever-greater number of technologies providing location-based services has given rise to a deluge of trajectory data. However, most of these trajectories are low-sampling-rate and, consequently, many movement details are lost. Due to that, trajectory reconstruction techniques have been created to infer the missing movement details and reduce uncertainty. Nevertheless, most effort has been put into reconstructing vehicle trajectories. Therefore, we study the reconstruction of pedestrian trajectories by using road network information. We compare a simple technique that only uses road network information with a more complex technique that, besides the road network, uses historical trajectory data. Additionally, we use three different trajectory segmentation settings to analyze their influence over reconstruction. Our experiment results show that, with the limited pedestrian trajectory data available, a simple technique that does not use historical data performs considerably better than a more complex technique that does use it. Furthermore, our results also show that trajectories segmented in such a way as to allow a greater distance and time span between consecutive points obtain better reconstruction results in the majority of the cases, regardless of the technique used.