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DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation [article]

Han Wang, Zhou Huang, Xiao Zhou, Ganmin Yin, Yi Bao
2022 arXiv   pre-print
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]

ZiChuan Liu, Zhaoyang Wu, Meng Wang, Rui Zhang
2021 arXiv   pre-print
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

Qing Guo, Zhu Sun, Jie Zhang, Yin-Leng Theng
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
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

Nasrin Bahra, Samuel Pierre
2021 Telecom  
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]

Wei Chen and Shuzhe Li and Chao Huang and Yanwei Yu and Yongguo Jiang and Junyu Dong
2022 arXiv   pre-print
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

Qiang Gao, Fengli Zhang, Fuming Yao, Ailing Li, Lin Mei, Fan Zhou
2020 IEEE Access  
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

Ayele Gobezie Chekol, Marta Sintayehu Fufa
2022 EURASIP Journal on Wireless Communications and Networking  
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

Nicksson Ckayo Arrais de Freitas, Ticiana L. Coelho Da Silva, José Antônio Fernandes De Macêdo, Leopoldo Melo Júnioer
2021 Proceedings of the ... International Florida Artificial Intelligence Research Society Conference  
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]

Seongjin Choi
2021 arXiv   pre-print
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]

Liwei Huang, Yutao Ma, Yanbo Liu, Keqing He
2020 arXiv   pre-print
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]

Cheng Yang, Maosong Sun, Wayne Xin Zhao, Zhiyuan Liu, Edward Y.Chang
2017 arXiv   pre-print
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

Ioannis Arapakis, Luis A. Leiva
2020 Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval  
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

Ioannis Arapakis, Luis A. Leiva
2020 arXiv   pre-print
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

Jingyuan Wang, Ning Wu, Xin Zhao
2021 IEEE Transactions on Knowledge and Data Engineering  
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]

Yiwen Sun, Yulu Wang, Kun Fu, Zheng Wang, Ziang Yan, Changshui Zhang, Jieping Ye
2020 arXiv   pre-print
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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