Unveiling the complexity of human mobility by querying and mining massive trajectory data

By Fosca Giannotti, Mirco Nanni, Dino Pedreschi, Fabio Pinelli, C. Renso, Salvatore Rinzivillo, Roberto Trasarti. 2011

335 citations recorded by the source corpus.

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

How researchers see this paper

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

In Geography, Planning and Development

1. Joseph Ferreira Jr.
2. Shan Jiang
3. Jiawei Han
4. Marta C. González

In Transportation

1. Marta C. González
2. Shan Jiang
3. Joseph Ferreira Jr.
4. Mudhakar Srivatsa
5. Filippo Simini
6. Yingxiang Yang
7. Albert-László Barabási
8. Fosca Giannotti

In Signal Processing

1. Fosca Giannotti
2. Mudhakar Srivatsa
3. Joseph Ferreira Jr.
4. Shan Jiang
5. Jiawei Han
6. Desheng Zhang
7. Tian He
8. Dino Pedreschi

Citations in the corpus

• Smart cities of the future (2012, neutral): “More than just examples, these questions are paradigmatic representatives of the analysts need to disentangle the huge diversity of individual locations and tracks to discover the subgroups of travel characterised by common behaviour and purpose [22].”
• A review of urban computing for mobile phone traces: current methods, challenges and opportunities (2013, background): “The recently improved ability to capture, store, and understand massive amounts of data is changing the methods for inferring human behavior [13].”
• Exploring human mobility with multi-source data at extremely large metropolitan scales (2014, background): “Recently, the study of human mobility has gained significant attention, thanks to the ubiquity of human location tracking devices, e.g., on-board GPS devices [8], cellphones [11], and Automatic Fare Collection (AFC) devices deployed by urban transit (e.g., subways [12], buses [3], and taxicabs [16]).”
• Returners and explorers dichotomy in human mobility (2015, background): “…over the k -th most frequented locations L 1 , y , L k ; r ð k Þ cm is the centre of mass computed on the k-th most frequented locations (Supplementary Note 7, Supplementary Figs 8 and 9); N k is the sum of the weights assigned to the k-th most frequented locations ( r ð k Þ g ¼ r g if k Z N ).”
• Inferring human mobility patterns from taxicab location traces (2013, background): “Human mobility patterns have been well studied in the past [7, 11, 16, 18, 20, 6, 22].”