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A survey on next location prediction techniques, applications, and challenges
2022
EURASIP Journal on Wireless Communications and Networking
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It is challenging to analyze and mine trajectory data due to the complex characteristics reflected in human mobility, which is affected by multiple contextual information. ...
In next location prediction, trajectory is represented by a sequence of timestamped geographical locations. ...
A novel neural network model jointly models social networks and mobile user trajectories. First, a network embedding method adopted for social networks construction is a networking representation. ...
doi:10.1186/s13638-022-02114-6
fatcat:s2ixs3ftibaobighbik6ikgfce
A Neural Network Approach to Joint Modeling Social Networks and Mobile Trajectories
[article]
2017
arXiv
pre-print
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In order to better analyze and mine LBSN data, we present a novel neural network model which can joint model both social networks and mobile trajectories. ...
In specific, our model consists of two components: the construction of social networks and the generation of mobile trajectories. ...
ACKNOWLEDGMENTS The authors thank the anonymous reviewers for their valuable and constructive comments. ...
arXiv:1606.08154v2
fatcat:xinquhxbhnbczie5kcn5zmpppm
DeepMove
2018
Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
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In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility. ...
Then, we propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way, which effectively utilizes the periodicity nature to augment the recurrent neural ...
Then, they are jointly fed into a recurrent neural network to capture the complicated transition relationship. ...
doi:10.1145/3178876.3186058
dblp:conf/www/FengLZSMGJ18
fatcat:cfdvvcitizegnfgzhpyopz36xa
DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis
[article]
2020
arXiv
pre-print
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To address these challenges, we propose an unsupervised neural approach for mobility behavior clustering, called the Deep Embedded TrajEctory ClusTering network (DETECT). ...
Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence. ...
INTRODUCTION The rapid proliferation of mobile devices has led to the collection of vast amounts of GPS trajectories by locationbased services, geo-social networks and ride sharing apps. ...
arXiv:2003.01351v1
fatcat:wirelxw65rg77psfm6izezb6bm
Generative Adversarial Networks for Spatio-temporal Data: A Survey
[article]
2021
arXiv
pre-print
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Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation and time-series data imputation. ...
While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. ...
In GD-GAN [41] , Fernando et al. designed a GAN based pipeline to jointly learn features for both pedestrian trajectory prediction and social group detection. ...
arXiv:2008.08903v3
fatcat:pbhxbfgw65bodksjdmwazwo4dq
Papers by title
2020
2020 7th NAFOSTED Conference on Information and Computer Science (NICS)
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Integrated On-Silicon and On-Glass Antennas for Mm-Wave Applications
Integrating AMR to Neural Machine Translation Using Graph Attention Networks
J A B C D E F G H I J L M O P R S T U V
Jointly ...
to Background Noise in Crowd Counting Local Binary Pattern and Census, Which One is Better in Stereo Matching
Image Toward Image Socially Aware Trajectory Planning System for Autonomous Mobile Robots ...
doi:10.1109/nics51282.2020.9335873
fatcat:jccrnlis2rdijbwayzrht4lskq
Spatial-Temporal Block and LSTM Network for Pedestrian Trajectories Prediction
[article]
2020
arXiv
pre-print
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Pedestrian trajectory prediction is a critical to avoid autonomous driving collision. But this prediction is a challenging problem due to social forces and cluttered scenes. ...
Such human-human and human-space interactions lead to many socially plausible trajectories. In this paper, we propose a novel LSTM-based algorithm. ...
A host of researches such as object detection [28] and speech recognition [27] utilize convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders [29] to extract informative ...
arXiv:2009.10468v2
fatcat:sjjpthtx3rcgzbhn2iawuebwja
Learning Neural Models for Continuous-Time Sequences
[article]
2021
arXiv
pre-print
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With the research direction described in this work, we aim to study the properties of continuous-time event sequences (CTES) and design robust yet scalable neural network-based models to overcome the aforementioned ...
In this work, we model the underlying generative distribution of events using marked temporal point processes (MTPP) to address a wide range of real-world problems. ...
train a deep neural network. ...
arXiv:2111.07189v1
fatcat:itawzoq5qfca5lomqoae4cmml4
Scanning the Issue
2020
IEEE transactions on intelligent transportation systems (Print)
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Wireless sensor networks (WSNs) have a main role in this evolution as an essential part of data acquisition and the way in which WSNs are powered is one of the main challenges to face, if the industry ...
environment with two cellular LTE SOPs, and 3.6 m over a 345 m trajectory in a challenging urban environment with two cellular LTE SOPs. ...
Kim, and A. Oh The authors develop a neural network model to classify driving events and type of events. ...
doi:10.1109/tits.2020.3001822
fatcat:yk66ziu2kbgsfbit6mabxfwrdu
Deep CNN-Assisted Personalized Recommendation over Big Data for Mobile Wireless Networks
2019
Wireless Communications and Mobile Computing
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user trajectories, to construct a deep prediction model. ...
At present, to improve the accuracy and performance for personalized recommendation in mobile wireless networks, deep learning has been widely concerned and employed with social and mobile trajectory big ...
Acknowledgments The work was funded by the National Natural Science Foundation of China (Grants nos. 61702277 and 61872219). ...
doi:10.1155/2019/6082047
fatcat:62ul4n65bbhxhjp5pukc3enp6u
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
[article]
2019
arXiv
pre-print
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We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behaviour and encodes the semantics of visited locations by using geographical information from Location-Based Social ...
In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. ...
Finally, a variety of neural network approaches have been employed to model the problem of next location prediction. ...
arXiv:1807.08173v2
fatcat:4rpwx777kzhtjcbz3gn3zrqlfq
Deep Wide Spatial-Temporal Based Transformer Networks Modeling for the Next Destination According to the Taxi Driver Behavior Prediction
2020
Applied Sciences
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This paper uses a neural network approach transformer of taxi driver behavior to predict the next destination with geographical factors. ...
In our approach, the encoder and decoder are the transformer's primary units; with the help of Location-Based Social Networks (LBSN), we encode the geographical information based on visited semantic locations ...
Acknowledgments: Data were retrieved from Kaggle and FourSquare.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app11010017
fatcat:xcabnue4prf6joid4ayzgd3mti
Deep Learning for Spatio-Temporal Data Mining: A Survey
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1906.04928v1 fatcat:4cg55y675ba4zbx3hw3cdphdce
transportation, climate science, human mobility, location based social network, crime analysis, and neuroscience. ...
Recently, with the advances of deep learning techniques, deep leaning models such as convolutional neural network (CNN) and recurrent neural network (RNN) have enjoyed considerable success in various machine ...
To model the two aspects and mine their correlations, [155] proposed a neural network model to jointly learn the social network representation and the users' mobility trajectory representation. ...
arXiv:1906.04928v2
fatcat:4zrdtgkvirfuniq3rb2gl7ohpy
Trajectory-User Linking via Variational AutoEncoder
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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Existing works on mining mobility patterns often model trajectories using Markov Chains (MC) or recurrent neural networks (RNN) -- either assuming independence between non-adjacent locations or following ...
We tackle the TUL problem with a semi-supervised learning framework, called TULVAE (TUL via Variational AutoEncoder), which learns the human mobility in a neural generative architecture with stochastic ...
and HERE grant 30046005, and the Fundamental Research Funds for the Central Universities (No.ZYGX2015J072). ...
doi:10.24963/ijcai.2018/446
dblp:conf/ijcai/0002GTZZZ18
fatcat:nygjb5747rf3nect5clx46higu
Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation
[article]
2022
arXiv
pre-print
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The use of imitation learning and inverse reinforcement learning to social navigation for mobile robots, however, is currently hindered by a lack of large scale datasets that capture socially compliant ...
, odometry, visual and inertial information, collected on two morphologically different mobile robots a Boston Dynamics Spot and a Clearpath Jackal by four different human demonstrators in both indoor ...
Imitation Learning for Global and Local Planning 1) Approach and Implementation: To answer question 2, we apply the BC imitation learning algorithm [37] on SCAND to jointly train end-to-end a socially-aware ...
arXiv:2203.15041v2
fatcat:t7bjkt27sfdwbdxych7kyl5b7i
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