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DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation
[article]
2022
arXiv
pre-print
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We first design movement, route, and global features generated from the trajectory data and urban functional zones and then analyze them respectively with the attention encoder or feed forward network. ...
The attention fusion module incorporates route features with movement features to create a better spatial-temporal embedding. ...
manner in order to complete the trajectory-user linking task. ...
arXiv:2205.08356v1
fatcat:66qsragxmzbyzotchfrybtred4
Multi View Spatial-Temporal Model for Travel Time Estimation
[article]
2021
arXiv
pre-print
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Experiments on large-scale taxi trajectory data show that our approach is more effective than the novel method. ...
Therefore, we propose a Multi-View Spatial-Temporal Model (MVSTM) to capture the dependence of spatial-temporal and trajectory. ...
[5] used convolution neural network to process map image trajectory information, and combined traffic mode recommendation with travel time estimation recommendation. ...
arXiv:2109.07402v2
fatcat:mmjbisapezba7d47hlsghsnkba
An Attentional Recurrent Neural Network for Personalized Next Location Recommendation
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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A recurrent neural network is then adopted to model the sequential regularity by capturing various contexts that govern user mobility. ...
To this end, we propose an Attentional Recurrent Neural Network (ARNN) to jointly model both the sequential regularity and transition regularities of similar locations (neighbors). ...
Following previous work (Yao et al. 2017) , users with fewer than 5 trajectories and trajectories with fewer than 3 check-ins are removed. ...
doi:10.1609/aaai.v34i01.5337
fatcat:nfd3l2eu25bnncqc3fhlkcxmoy
A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
2021
Telecom
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We deploy a vector autoregression (VAR) model and a gated recurrent unit (GRU) to predict the future trajectory of a user. ...
In this paper, we propose a hybrid user mobility prediction approach for handover management in mobile networks. ...
In [13] , the authors proposed a mobility model based on the recurrent neural network variations. They proposed to deploy line simplification techniques to simplify the user trajectory. ...
doi:10.3390/telecom2020013
fatcat:75ldp5lhmffq7njntuolc4fzri
Mutual Distillation Learning Network for Trajectory-User Linking
[article]
2022
arXiv
pre-print
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Trajectory-User Linking (TUL), which links trajectories to users who generate them, has been a challenging problem due to the sparsity in check-in mobility data. ...
Specifically, MainTUL is composed of a Recurrent Neural Network (RNN) trajectory encoder that models sequential patterns of input trajectory and a temporal-aware Transformer trajectory encoder that captures ...
hierarchical semantics of trajectory in RNN [Zhou et al., 2018] . ( 7 ) DeepTUL -a recurrent network with attention mechanism for TUL task [Miao et al., 2020] . ...
arXiv:2205.03773v1
fatcat:ai3svbax25bqdpeumyyprtk4ru
Adversarial Mobility Learning for Human Trajectory Classification
2020
IEEE Access
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INDEX TERMS Trajectory user linking, adversarial model, autoencoder, attention mechanism, human mobility. ...
Recently, a user mobility mining task called Trajectory User Linking (TUL) has become an essential and popular topic, aiming at identifying user identities through exploiting their mobility patterns. ...
A practical topic in understanding human mobility is Trajectory User Linking (TUL), which aims to link human mobility to their user identities who generate them. ...
doi:10.1109/access.2020.2968935
fatcat:qtoljzt3ircstncdwsictuofae
A survey on next location prediction techniques, applications, and challenges
2022
EURASIP Journal on Wireless Communications and Networking
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Heterogeneous data generated from different sources, users' random movement behavior, and the time sensitivity of trajectory data are some of the challenges. ...
AbstractNext location prediction has recently gained great attention from researchers due to its importance in different application areas. ...
A trajectory is then linked to the user who has the most similar movement pattern with it. ...
doi:10.1186/s13638-022-02114-6
fatcat:s2ixs3ftibaobighbik6ikgfce
Using deep learning for trajectory classification in imbalanced dataset
2021
Proceedings of the ... International Florida Artificial Intelligence Research Society Conference
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In this paper, we perform the experiments with three real datasets from LBSN (Location-Based Social Network) trajectories to identify who is the user of a sub-trajectory (similar to the Trajectory-User ...
Linking problem). ...
More recent Deep Learning models emerged to link trajectories to their generating users. ...
doi:10.32473/flairs.v34i1.128368
fatcat:mbh5i63w35fhpmwi434br2ei4q
Deep Learning based Urban Vehicle Trajectory Analytics
[article]
2021
arXiv
pre-print
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The urban vehicle trajectory analytics offers unprecedented opportunities to understand vehicle movement patterns in urban traffic networks including both user-centric travel experiences and system-wide ...
In this dissertation, we focus on the 'urban vehicle trajectory,' which refers to trajectories of vehicles in urban traffic networks, and we focus on 'urban vehicle trajectory analytics.' ...
"Attention-based recurrent neural network for urban vehicle trajectory prediction." Procedia Computer Science 151 (2019): 327-334. ...
arXiv:2111.07489v1
fatcat:zanf5aj7unfb5joey3f7lzhtbm
DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation
[article]
2020
arXiv
pre-print
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In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner. ...
In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR. ...
[31] developed an attentional recurrent network for mobility prediction from lengthy and sparse user trajectories. Gao et al. ...
arXiv:2004.12161v1
fatcat:7ymnb4z4kndbrfkjwy35sy67aq
A Neural Network Approach to Joint Modeling Social Networks and Mobile Trajectories
[article]
2017
arXiv
pre-print
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We first adopt a network embedding method for the construction of social networks: a networking representation can be derived for a user. ...
The accelerated growth of mobile trajectories in location-based services brings valuable data resources to understand users' moving behaviors. ...
(b) A trajectory generated by a user is a sequence of chronologically ordered check-in records.
Fig. 3 . 3 An illustrative architecture of recurrent neural networks with GRUs. ...
arXiv:1606.08154v2
fatcat:xinquhxbhnbczie5kcn5zmpppm
Learning Efficient Representations of Mouse Movements to Predict User Attention
2020
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
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We investigate different representations of mouse cursor movements, including time series, heatmaps, and trajectory-based images, to build and contrast both recurrent and convolutional neural networks ...
that can predict user attention to direct displays, such as SERP advertisements. ...
Arapakis acknowledges the support of NVIDIA Coorporation with the donation of a Titan Xp GPU used for this research. ...
doi:10.1145/3397271.3401031
dblp:conf/sigir/ArapakisL20
fatcat:3jbs3i2pfng27lkl6w3rrdwwrq
Learning Efficient Representations of Mouse Movements to Predict User Attention
2020
arXiv
pre-print
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We investigate different representations of mouse cursor movements, including time series, heatmaps, and trajectory-based images, to build and contrast both recurrent and convolutional neural networks ...
that can predict user attention to direct displays, such as SERP advertisements. ...
Arapakis acknowledges the support of NVIDIA Coorporation with the donation of a Titan Xp GPU used for this research. ...
arXiv:2006.01644v1
fatcat:5qqjysljwbfcbcqao3oh3yavb4
Personalized Route Recommendation with Neural Network Enhanced A* Search Algorithm
2021
IEEE Transactions on Knowledge and Data Engineering
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First, we employ attention-based Recurrent Neural Networks (RNN) to model the cost from the source to the candidate location by incorporating useful context information. ...
Given a road network, the PRR task aims to generate user-specific route suggestions for replying to users' route queries. ...
More recently, Recurrent Neural Network (RNN) together with its variant Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have been widely used for modeling sequential trajectory data. ...
doi:10.1109/tkde.2021.3068479
fatcat:t7pf5eyi2jbyvmezdr6c5h5l34
FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention
[article]
2020
arXiv
pre-print
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To solve this problem, we propose a novel, brief and effective framework mainly based on feed-forward network (FFN) for ETA, FFN with Multi-factor self-Attention (FMA-ETA). ...
Nowadays, deep learning based methods, specifically recurrent neural networks (RNN) based ones are adapted to model the ST patterns from massive data for ETA and become the state-of-the-art. ...
It divides the trajectory into several links and intersections. ...
arXiv:2006.04077v1
fatcat:b3meecb3pfhuxac4clhf6tm5j4
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