Journal Article10.26599/BDMA.2018.9020010
Location prediction on trajectory data: A review
Ruizhi Wu,Guangchun Luo,Junming Shao,Ling Tian,Chengzong Peng +4 more
- 12 Apr 2018
- Vol. 1, Iss: 2, pp 108-127
106
TL;DR: This survey provides a comprehensive overview of location prediction, including basic definitions and concepts, algorithms, and applications, and identifies the potential challenges and future research directions in location prediction.
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Abstract: Location prediction is the key technique in many location based services including route navigation, dining location recommendations, and traffic planning and control, to mention a few. This survey provides a comprehensive overview of location prediction, including basic definitions and concepts, algorithms, and applications. First, we introduce the types of trajectory data and related basic concepts. Then, we review existing location-prediction methods, ranging from temporal-pattern-based prediction to spatiotemporal-pattern-based prediction. We also discuss and analyze the advantages and disadvantages of these algorithms and briefly summarize current applications of location prediction in diverse fields. Finally, we identify the potential challenges and future research directions in location prediction.
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Citations
Companion Mobility to Assist in Future Human Location Prediction
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TL;DR: Zhang et al. as mentioned in this paper proposed a two-phase framework for predicting an individual's future locations that fully benefits from spatio-temporal contexts embedded in that person and his/her companions' mobility.
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Proactive Load Balancing Through Constrained Policy Optimization for Ultra-Dense Networks
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GANs for Privacy-Aware Mobility Modeling
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2
Learning to predict diverse trajectory from human motion patterns
TL;DR: Zhang et al. as discussed by the authors investigated the natural motion of pedestrians and explored future motion patterns of pedestrians with similar observed trajectories, which provided a new perspective for multi-modal prediction of pedestrian trajectories.
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User Mobility Dataset for 5G Networks Based on GPS Geolocation
Siham Bouchelaghem,Hakim Boudjelaba,Mawloud Omar,Mourad Amad +3 more
- 02 Nov 2022
TL;DR: In this article, the authors present a novel mobility dataset generation method for 5G networks based on users' GPS trajectory data, aggregating the user's GPS trajectories and modeling his location history by a mobility graph representing the set of cell base stations he passed through.
2
References
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
An Introduction to Information Retrieval
Stefano Ceri,Alessandro Bozzon,Marco Brambilla,Emanuele Della Valle,Piero Fraternali,Silvia Quarteroni +5 more
- 01 Jan 2013
TL;DR: This chapter introduces information retrieval as a scientific discipline, providing a formal characterization centered on the notion of relevance and touches on some of its challenges and classic applications and then dedicate a section to its main evaluation criteria: precision and recall.
6.9K
Social LSTM: Human Trajectory Prediction in Crowded Spaces
Alexandre Alahi,Kratarth Goel,Vignesh Ramanathan,Alexandre Robicquet,Li Fei-Fei,Silvio Savarese +5 more
- 27 Jun 2016
TL;DR: This work proposes an LSTM model which can learn general human movement and predict their future trajectories and outperforms state-of-the-art methods on some of these datasets.
Friendship and mobility: user movement in location-based social networks
Eunjoon Cho,Seth A. Myers,Jure Leskovec +2 more
- 21 Aug 2011
TL;DR: A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance.
The scaling laws of human travel
TL;DR: It is shown that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and concluded that human travel on geographical scales is an ambivalent and effectively superdiffusive process.