Joint Spatial-Temporal Trajectory Clustering Method for Mobile Social Networks

Tang, Ji; Liu, Linfeng; Wu, Jiagao; Zhou, Jian; Xiang, Yang

doi:10.1109/icpads47876.2019.00123

Cited by 3 publications

(1 citation statement)

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“…Furthermore, the problem of trajectory maps was also approached, almost simultaneously, by other communities, that is, data mining (Chen et al, 2011(Chen et al, , 2012Gaffney and Smyth, 1999;Li et al, 2010;Nanni and Pedreschi, 2006;Pelekis et al, 2009Pelekis et al, , 2017Tang et al, 2019Tang et al, , 2020, intelligent transportation systems (Atev et al 2010;Choong et al 2016Choong et al , 2017Li et al, 2006;Portugal et al, 2020;Song et al, 2017;Wang et al, 2019Wang et al, , 2020Won et al, 2009), air traffic (Enriquez and Kurcz, 2012), and maritime traffic (Dutt et al, 2018;Kontopoulos et al, 2021;Liu et al, 2014a;Li et al, 2017). The detailed discussion regarding relevant publications coming from these fields is presented in Section 7.…”

Section: Trajectorymentioning

confidence: 99%

Survey of maps of dynamics for mobile robots

Kucner

Magnusson

Mghames

et al. 2023

The International Journal of Robotics Research

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Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

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