Semantic trajectory mining for location prediction

By J. Ying, Wang-Chien Lee, Tz-Chiao Weng, Vincent Shin-Mu Tseng. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2011

335 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/58113e14c0e844e7ac63040d8cd92efd2205db90

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Transportation

1. Bruno Martins
2. Wesley Mathew
3. Ruben Raposo
4. Yong Li
5. Depeng Jin

In Signal Processing

1. Bruno Martins
2. Wesley Mathew
3. Ruben Raposo

Citations in the corpus

• DeepMove: Predicting Human Mobility with Attentional Recurrent Networks (2018, methodology): “Pattern-based methods: Pattern-based methods [25, 26, 34, 40, 42] first discover the popular sequential mobility patterns from the trajectory, and then try to predict the mobility based on these popular patterns.”
• Predicting future locations with hidden Markov models (2012, methodology): “proposed an approach for predicting the next location of an individual based on both geographic and semantic features of trajectories, using a clusterbased prediction strategy which evaluates the next locations with basis on the frequent behaviors of similar users in the same cluster, as determined ”