Corridor Learning Using Individual Trajectories
Nikolaos Zygouras, Dimitrios Gunopulos · 2018
The rapid development and commercialization of location acquisition technologies generates large trajectory datasets, that trace moving objects' trips. In this work, we propose a new trajectory mining algorithm, for discovering paths that are frequently followed by the given trajectories, named as corridors. We claim that the moving objects follow common paths-corridors. Detecting corridors from a collection of trajectories is extremely challenging due to the nature of the data (low sampling rates, different speeds, noisy measurements etc.). In this work we propose and evaluate a pipelined algorithm that abstracts from trajectories their underlying frequent paths.