Discovering popular routes from trajectories

By Zaiben Chen, Heng Tao Shen, Xiaofang Zhou. IEEE International Conference on Data Engineering, 2011

390 citations recorded by the source corpus.

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Constructing popular routes from uncertain trajectories (2012, methodology): “In [3], the authors investigated the problem of popular route planning without road network information.”
Trajectory Data Mining (2015, background): “[20] identify turning points from each raw trajectory, clustering these turning points into groups.”
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “[217] first leverage existing trips to build a tour network by linking hot areas with routes and then discover popular routes from the network with a traffic flow detection algorithm.”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “e top-k trajectories that pass through popular regions. Rather deriving search results using only existing trips, generating new trip plans without keywords or locations is also possible. Chen et al. [42] discovered popular routes from spatial-only trajectories by constructing a transfer network fro”
Deep Representation Learning for Trajectory Similarity Computation (2018, background): “Quantifying the similarity between two trajectories is a fundamental research problem and is a foundation for many trajectory based applications, such as tracking migration patterns of animals [1], mining hot routes in cities [2], trajectory clustering [3] and moving group discovery [4], [5].”