Data sampling pattern is inherently determined by the deployment of sensors and the recording intervals. The deployment of sensors is determined by the road characteristics. In the domain of short-term traffic flow prediction, existing approaches often indirectly learn the sampling pattern from the changes of observation or modeling the sampling pattern using static spatial definition of road networks. The existing approach may not be optimum in the newer datasets with multiple sampling patterns. We argue learning the sampling patterns directly from the road characteristics is more effective. We conducted a case study on spatial temporal traffic flow prediction model. Findings from experiments show 2% to 4% improvement over prediction metrics and some interesting prediction characteristics are found at different prediction horizons.

Public buildings such as shopping arcades and railway stations are environments in which pedestrian movement is of significance to many smart building applications. The data-driven approach of pedestrian trajectory prediction is effective in learning a reliable model that can represent complex human movement. Pedestrian trajectories are highly linked to the locations of facilities and services inside a building as pedestrians move towards these destinations for engagement. This paper suggests that the notion of destination is a strong predictor of pedestrian trajectories and proposes a novel enhancement of the data-driven approach for pedestrian tracking in public buildings. The method of destination-driven pedestrian trajectory prediction (DDPTP) first evaluates the most likely destinations of the pedestrian using the destination classifier (DC) and then predicts the future trajectories with the destination-specific trajectory model (DTM). The proposed solution has been evaluated on the NYGC and the ATC datasets and found to outperform state-of-the-art models. The notion of destination can be further developed into a region of interest of which the within-region and out-of-region features can be factored out for more effective learning.