A Survey on Trajectory Data Mining: Techniques and Applications

Feng, Zhenni; Zhu, Yanmin

doi:10.1109/access.2016.2553681

Cited by 205 publications

(93 citation statements)

References 82 publications

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“…Spatial indexing is widely used for trajectory data management and supporting effective trajectory retrieval. Feng et al [25] summarize seven kinds of queries, which mainly focuses on two categories [77]: Range queries and K-Nearest-Neighborhoods (KNN) queries.…”

Section: Efficient Trajectory Queriesmentioning

confidence: 99%

A Natural-language-based Visual Query Approach of Uncertain Human Trajectories

Huang

Zhao

Chen

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. (a) The query condition specification view. (b) The relevance tree with the spatial keyword 'tourist attractions'. (c) The drop-down menu for changing the type of the input keyword. (d) The semantics view shows that the major region functional topic is 'residential related'. (e) The map view shows that the queried trajectories are mainly distributed in the northwest (named 'Jiangxin island'), the middle, and the east (named 'Wuhua building') of the city. (f) Most trajectories land the island from its east through ferry. (g) The region functional topics. (h) The rendering parameter widget. (i) The temporal graph view. (j) The detail result view of the queried trajectories. (k) Detail study of urban areas.Abstract-Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate geographic coordinates, e.g., positions of a mobile phone only refer to the regions (i.e., mobile cell stations) in which it resides, instead of accurate GPS coordinates. On the other hand, domain experts and general users prefer a natural way, such as using a natural language sentence, to access and analyze massive movement data. In this paper, we propose a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data. It is built up on encoding massive, spatially uncertain trajectories by the semantic information of the POIs and regions covered by them, and then storing the trajectory documents in text database with an effective indexing scheme. The visual interface facilitates query condition specification, situation-aware visualization, and semantic exploration of large trajectory data. Usage scenarios on real-world human mobility datasets demonstrate the effectiveness of our approach.

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Section: Efficient Trajectory Queriesmentioning

confidence: 99%

A Natural-language-based Visual Query Approach of Uncertain Human Trajectories

Huang

Zhao

Chen

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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“…Those spatiotemporal patterns include frequent routes [4,10,5,19], clusters of common sub-trajectories [15,17], frequently colocating moving objects [11,13,16,22,30], to name a few, each of which itself has produced a branch of works as a sub topic of trajectory pattern mining. A systematic review on all those sub topics is beyond the scope of this paper, and full survey articles are available [31,9]. In the rest of this section, we focus on the topic of semantic trajectory pattern mining and regional co-location pattern mining that are closely related to our mining model.…”

Section: Related Workmentioning

confidence: 99%

Efficient mining of regional movement patterns in semantic trajectories

2017

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Semantic trajectory pattern mining is becoming more and more important with the rapidly growing volumes of semantically rich trajectory data. Extracting sequential patterns in semantic trajectories plays a key role in understanding semantic behaviour of human movement, which can widely be used in many applications such as location-based advertising, road capacity optimisation, and urban planning. However, most of existing works on semantic trajectory pattern mining focus on the entire spatial area, leading to missing some locally significant patterns within a region. Based on this motivation, this paper studies a regional semantic trajectory pattern mining problem, aiming at identifying all the regional sequential patterns in semantic trajectories. Specifically, we propose a new density scheme to quantify the frequency of a particular pattern in space, and thereby formulate a new mining problem of finding all the regions in which such a pattern densely occurs. For the proposed problem, we develop an e cient mining algorithm, called RegMiner (Regional Semantic Trajectory Pattern Miner), which e↵ec-tively reveals movement patterns that are locally frequent in such a region but not necessarily dominant in the entire space. Our empirical study using real trajectory data shows that RegMiner finds many interesting local patterns that are hard to find by a state-of-the-art global pattern mining scheme, and it also runs several orders of magnitude faster than the global pattern mining algorithm.

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“…The trajectory prediction based on data mining was studied to calculate out the possible movement trajectory by predicting the dynamic behavior of mov ing objects [13][14][15] . Paper [16] reviewed existing trajectory data mining techniques that required an enormous amount of computing and storage space, which cannot be applied at one time.…”

Section: Related Workmentioning

confidence: 99%

NUCLEI: A New Unmanned Aerial Vehicle Early Warning Method for the No-fly Zone

Wang¹,

Yu²,

Ma³

et al. 2018

Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018)

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Abstract-In order to monitor the unmanned aerial vehicle(UAV) around the no-fly zone, we proposed a new unmanned aerial vehicle Early warning method for the no-fly zone (NUCLEI) which gives an alarm to the UAV around the nofly zone in real time. We utilize the least square method to match with the flight trajectory of the UAV in the current period, and then we use the minimum external rectangle (MERT) method to obtain the MERT of the electronic fence. Finally, we calculate whether the tangent line at the current point of the flight path fits curve will intersect with the electronic fence to make the prediction for if the UAV will enter the no-fly zone.

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A Survey on Trajectory Data Mining: Techniques and Applications

Cited by 205 publications

References 82 publications

A Natural-language-based Visual Query Approach of Uncertain Human Trajectories

A Natural-language-based Visual Query Approach of Uncertain Human Trajectories

Efficient mining of regional movement patterns in semantic trajectories

NUCLEI: A New Unmanned Aerial Vehicle Early Warning Method for the No-fly Zone

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