Log in

Relevant bibliographies by topics / Mobility prediction / Journal articles

To see the other types of publications on this topic, follow the link: Mobility prediction.

Journal articles on the topic 'Mobility prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Mobility prediction.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Burbey, Ingrid, and Thomas L. Martin. "A survey on predicting personal mobility." International Journal of Pervasive Computing and Communications 8, no. 1 (2012): 5–22. http://dx.doi.org/10.1108/17427371211221063.

Full text

Abstract:

PurposeLocation‐prediction enables the next generation of location‐based applications. The purpose of this paper is to provide a historical summary of research in personal location‐prediction. Location‐prediction began as a tool for network management, predicting the load on particular cellular towers or WiFi access points. With the increasing popularity of mobile devices, location‐prediction turned personal, predicting individuals' next locations given their current locations.Design/methodology/approachThis paper includes an overview of prediction techniques and reviews several location‐predi

APA, Harvard, Vancouver, ISO, and other styles

2

Kareem Mhalhal, Nabaa, and Suhad Faisal Behadili. "Mobility Prediction Based on LSTM Multi-Layer Using GPS Phone Data." Iraqi Journal for Electrical and Electronic Engineering 21, no. 2 (2025): 284–92. https://doi.org/10.37917/ijeee.21.2.25.

Full text

Abstract:

Precise Prediction of activity location is an essential element in numerous mobility applications and is especially necessary for the development of tailored sustainable transportation systems. Next-location prediction, which involves predicting a user's future position based on their past movement patterns, has significant implications in various domains, including urban planning, geo-marketing, disease transmission, Performance wireless network, Recommender Systems, and many other areas. In recent years, various predictors have been suggested to tackle this issue, including state-of-the-art

APA, Harvard, Vancouver, ISO, and other styles

3

Guo, Bao, Hu Yang, Fan Zhang, and Pu Wang. "A Hierarchical Passenger Mobility Prediction Model Applicable to Large Crowding Events." Journal of Advanced Transportation 2022 (June 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/7096153.

Full text

Abstract:

Predicting individual mobility of subway passengers in large crowding events is crucial for subway safety management and crowd control. However, most previous models focused on individual mobility prediction under ordinary conditions. Here, we develop a passenger mobility prediction model, which is also applicable to large crowding events. The developed model includes the trip-making prediction part and the trip attribute prediction part. For trip-making prediction, we develop a regularized logistic regression model that employs the proposed individual and cumulative mobility features, the num

APA, Harvard, Vancouver, ISO, and other styles

4

Sánchez-Rada, J. Fernando, Raquel Vila-Rodríguez, Jesús Montes, and Pedro J. Zufiria. "Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph." Algorithms 17, no. 4 (2024): 166. http://dx.doi.org/10.3390/a17040166.

Full text

Abstract:

Predicting vehicle mobility is crucial in domains such as ride-hailing, where the balance between offer and demand is paramount. Since city road networks can be easily represented as graphs, recent works have exploited graph neural networks (GNNs) to produce more accurate predictions on real traffic data. However, a better understanding of the characteristics and limitations of this approach is needed. In this work, we compare several GNN aggregated mobility prediction schemes to a selection of other approaches in a very restricted and controlled simulation scenario. The city graph employed re

APA, Harvard, Vancouver, ISO, and other styles

5

Cadger, Fraser, Kevin Curran, Jose Santos, and Sandra Moffet. "Opportunistic Neighbour Prediction Using an Artificial Neural Network." International Journal of Advanced Pervasive and Ubiquitous Computing 7, no. 2 (2015): 38–50. http://dx.doi.org/10.4018/ijapuc.2015040104.

Full text

Abstract:

Device mobility is an issue that affects both MANETs and opportunistic networks. While the former employs conventional routing techniques with some element of mobility management, opportunistic networking protocols often use mobility as a means of delivering messages in intermittently connected networks. If nodes are able to determine the future locations of other nodes with reasonable accuracy then they could plan ahead and take into account and even benefit from such mobility. Location prediction in combination with geographic routing has been explored in previous literature. Most of these l

APA, Harvard, Vancouver, ISO, and other styles

6

Yu, Zhiyong, Zhiwen Yu, and Yuzhong Chen. "Multi-hop Mobility Prediction." Mobile Networks and Applications 21, no. 2 (2015): 367–74. http://dx.doi.org/10.1007/s11036-015-0668-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Guo, Bao, Kaipeng Wang, Hu Yang, Fan Zhang, and Pu Wang. "A New Individual Mobility Prediction Model Applicable to Both Ordinary Conditions and Large Crowding Events." Journal of Advanced Transportation 2023 (June 27, 2023): 1–14. http://dx.doi.org/10.1155/2023/3463330.

Full text

Abstract:

Accurate prediction of individual mobility is crucial for developing intelligent transportation systems. However, while previous models usually focused on predicting individual mobility under ordinary conditions, the models that are applicable to large crowding events are still lacking. Here, we employ the smart card data of 6.5 million subway passengers of the Shenzhen Metro to develop a Markov chain-based individual mobility prediction model (i.e., SCMM) applicable to both ordinary and anomalous passenger flow situations. The proposed SCMM model improves the Markov chain model by incorporati

APA, Harvard, Vancouver, ISO, and other styles

8

Teixeira, Douglas Do Couto, Aline Carneiro Viana, Jussara M. Almeida, and Mrio S. Alvim. "The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility." ACM Transactions on Spatial Algorithms and Systems 7, no. 4 (2021): 1–24. http://dx.doi.org/10.1145/3459625.

Full text

Abstract:

Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a predictio

APA, Harvard, Vancouver, ISO, and other styles

9

Boukhedouma, H., A. Meziane, S. Hammoudi, and A. Benna. "ON THE CHALLENGES OF MOBILITY PREDICTION IN SMART CITIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W2-2020 (September 15, 2020): 17–24. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w2-2020-17-2020.

Full text

Abstract:

Abstract. The mass of data generated from people’s mobility in smart cities is constantly increasing, thus making a new business for large companies. These data are often used for mobility prediction in order to improve services or even systems such as the development of location-based services, personalized recommendation systems, and mobile communication systems. In this paper, we identify the mobility prediction issues and challenges serving as guideline for researchers and developers in mobility prediction. To this end, we first identify the key concepts and classifications related to mobi

APA, Harvard, Vancouver, ISO, and other styles

10

Memon, Ambreen, Sardar M. N. Islam, Muhammad Nadeem Ali, and Byung-Seo Kim. "Enhancing Energy Efficiency of Sensors and Communication Devices in Opportunistic Networks Through Human Mobility Interaction Prediction." Sensors 25, no. 5 (2025): 1414. https://doi.org/10.3390/s25051414.

Full text

Abstract:

The proliferation of smart devices such as sensors and communication devices has necessitated the development of networks that can adopt device-to-device communication for delay-tolerant data transfer and energy efficiency. Therefore, there is a need to develop opportunistic networks to enhance energy efficiency through improved data routing. A sensor device equipped with computing, communication, and mobility capabilities can opportunistically transfer data to another device, either as a direct recipient or as an intermediary forwarding data to a third device. Routing algorithms designed for

APA, Harvard, Vancouver, ISO, and other styles

11

Comito, Carmela. "Human Mobility Prediction Through Twitter." Procedia Computer Science 134 (2018): 129–36. http://dx.doi.org/10.1016/j.procs.2018.07.153.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Soares, Natália, Paul Walther, and Martin Werner. "Experiments on Geospatial Data Modelling for Long-Term Trajectory Prediction of Aircrafts." AGILE: GIScience Series 6 (June 9, 2025): 1–7. https://doi.org/10.5194/agile-giss-6-46-2025.

Full text

Abstract:

Abstract. While predicting human and vehicle trajectories is a deeply investigated field of research, predicting aircraft trajectories remains a less explored frontier. Still, the long-term prediction of aircraft movements is a fundamental challenge in aviation, influencing Air Traffic Management (ATM), operational efficiency, and flight safety. Traditional trajectory prediction models are often primarily focused on a 2D prediction.With this work, we evaluate different data representation methods in the field of long-term aircraft trajectory prediction using a state-of-the-art mobility predict

APA, Harvard, Vancouver, ISO, and other styles

13

Hua, Chen, Wencheng Zhang, Hanghao Fu, et al. "The Prediction Method and Application of Off-Road Mobility for Ground Vehicles: A Review." World Electric Vehicle Journal 16, no. 1 (2025): 47. https://doi.org/10.3390/wevj16010047.

Full text

Abstract:

With the rapid advancement of technologies related to unmanned ground systems, ground vehicles are being widely deployed across various domains. However, when operating in complex, soft terrain environments, the low bearing capacity of such terrains poses a significant challenge to vehicle mobility. This paper presents a comprehensive review of mobility prediction methods for ground vehicles in off-road environments. We begin by discussing the concept of vehicle mobility, followed by a systematic and thorough summary of the primary prediction methods, including empirical, semi-empirical, numer

APA, Harvard, Vancouver, ISO, and other styles

14

Zhang, Yunke, Fengli Xu, Tong Li, Vassilis Kostakos, Pan Hui, and Yong Li. "Passive Health Monitoring Using Large Scale Mobility Data." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 1 (2021): 1–23. http://dx.doi.org/10.1145/3448078.

Full text

Abstract:

In this paper, we investigate the feasibility of using mobility patterns and demographic data to predict hospital visits. We collect mobility traces from two thousand users for around two months. We extract 16 mobility features from these passively collected mobility traces and train an XGBoost model to predict users' hospital visits. We demonstrate that the designed mobility features can significantly improve prediction accuracy (p < 0.01, AUC = 0.79). We further analyze how these mobility features affect the prediction results and measure their importance by using Shapley additive explana

APA, Harvard, Vancouver, ISO, and other styles

15

Erfani, Abdolmajid, and Vanessa Frias-Martinez. "A fairness assessment of mobility-based COVID-19 case prediction models." PLOS ONE 18, no. 10 (2023): e0292090. http://dx.doi.org/10.1371/journal.pone.0292090.

Full text

Abstract:

In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might

APA, Harvard, Vancouver, ISO, and other styles

16

Jang, Hee-Seon, and Jang-Hyun Baek. "Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks." Applied Sciences 12, no. 3 (2022): 1252. http://dx.doi.org/10.3390/app12031252.

Full text

Abstract:

Many registration schemes have been proposed to reduce the signaling cost required for user’s mobility management in wireless communication networks. Various results on mobility management schemes to minimize the total signaling cost have been reported. The objective of this study was to analyze a registration scheme that could deal with mobility prediction and corresponding flexible tracking area list (TAL) forming. In this scheme, based on mobility prediction and corresponding TAL forms, a new TAL was constructed such that the registration cost could be minimized. In addition, a semi-Markov

APA, Harvard, Vancouver, ISO, and other styles

17

Teotia, Shashiraj, and Dr Sohan Garg. "An Effective and Optimal Mobility Model and its Prediction in MANETs." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (2017): 1634–42. http://dx.doi.org/10.31142/ijtsrd8240.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Wu Xiaohua, and Li Jianping. "Routing Algorithm based on Mobility Prediction." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 2 (2012): 218–26. http://dx.doi.org/10.4156/aiss.vol4.issue2.27.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Jeong, Jaeseong, Dinand Roeland, Jesper Derehag, et al. "Mobility Prediction for 5G Core Networks." IEEE Communications Standards Magazine 5, no. 1 (2021): 56–61. http://dx.doi.org/10.1109/mcomstd.001.2000046.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

F.Hassanin, Mohammad, and Amr Badr. "Mobility Prediction using Modified RBF Network." International Journal of Computer Applications 118, no. 25 (2015): 1–4. http://dx.doi.org/10.5120/20959-3410.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Akbari Torkestani, Javad. "Mobility prediction in mobile wireless networks." Journal of Network and Computer Applications 35, no. 5 (2012): 1633–45. http://dx.doi.org/10.1016/j.jnca.2012.03.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Trasarti, R., R. Guidotti, A. Monreale, and F. Giannotti. "MyWay: Location prediction via mobility profiling." Information Systems 64 (March 2017): 350–67. http://dx.doi.org/10.1016/j.is.2015.11.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Abbas, Shatha, Mohammed J. F. Alenazi, and Amani Samha. "Mobility Prediction of Mobile Wireless Nodes." Applied Sciences 12, no. 24 (2022): 13041. http://dx.doi.org/10.3390/app122413041.

Full text

Abstract:

Artificial intelligence (AI) is a fundamental part of improving information technology systems. Essential AI techniques have revolutionized communication technology, such as mobility models and machine learning classification. Mobility models use a virtual testing methodology to evaluate new or updated products at a reasonable cost. Classifiers can be used with these models to achieve acceptable predictive accuracy. In this study, we analyzed the behavior of machine learning classification algorithms—more specifically decision tree (DT), logistic regression (LR), k-nearest neighbors (K-NN), la

APA, Harvard, Vancouver, ISO, and other styles

24

Mohammad, Al-Hattab, and Hamada Nuha. "Prediction of nodes mobility in 3-D space." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3229–40. https://doi.org/10.11591/ijece.v11i4.pp3229-3240.

Full text

Abstract:

Recently, mobility prediction researches attracted increasing interests, especially for mobile networks where nodes are free to move in the threedimensional space. Accurate mobility prediction leads to an efficient data delivery for real time applications and enables the network to plan for future tasks such as route planning and data transmission in an adequate time and a suitable space. In this paper, we proposed, tested and validated an algorithm that predicts the future mobility of mobile networks in three-dimensional space. The prediction technique uses polynomial regression to model the

APA, Harvard, Vancouver, ISO, and other styles

25

Terroso-Saenz, Fernando, and Andres Muñoz. "Human Mobility Prediction with Region-based Flows and Road Traffic Data." JUCS - Journal of Universal Computer Science 29, no. 4 (2023): 374–96. http://dx.doi.org/10.3897/jucs.94514.

Full text

Abstract:

Predicting human mobility is a key element in the development of intelligent transport systems. Current digital technologies enable capturing a wealth of data on mobility flows between geographic areas, which are then used to train machine learning models to predict these flows. However, most works have only considered a single data source for building these models or different sources but covering the same spatial area. In this paper we propose to augment a macro open-data mobility study based on cellular phones with data from a road traffic sensor located within a specific motorway of one of

APA, Harvard, Vancouver, ISO, and other styles

26

Terroso-Saenz, Fernando, and Andres Muñoz. "Human Mobility Prediction with Region-based Flows and Road Traffic Data." JUCS - Journal of Universal Computer Science 29, no. (4) (2023): 374–96. https://doi.org/10.3897/jucs.94514.

Full text

Abstract:

Predicting human mobility is a key element in the development of intelligent transport systems. Current digital technologies enable capturing a wealth of data on mobility flows between geographic areas, which are then used to train machine learning models to predict these flows. However, most works have only considered a single data source for building these models or different sources but covering the same spatial area. In this paper we propose to augment a macro open-data mobility study based on cellular phones with data from a road traffic sensor located within a specific motorway of one of

APA, Harvard, Vancouver, ISO, and other styles

27

Miyazawa, S., X. Song, R. Jiang, Z. Fan, R. Shibasaki, and T. Sato. "CITY-SCALE HUMAN MOBILITY PREDICTION MODEL BY INTEGRATING GNSS TRAJECTORIES AND SNS DATA USING LONG SHORT-TERM MEMORY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2020 (August 3, 2020): 87–94. http://dx.doi.org/10.5194/isprs-annals-v-4-2020-87-2020.

Full text

Abstract:

Abstract. Human mobility analysis on large-scale mobility data has contributed to multiple applications such as urban and transportation planning, disaster preparation and response, tourism, and public health. However, when some unusual events happen, every individual behaves differently depending on their personal routine and background information. To improve the accuracy of the crowd behavior prediction model, understanding supplemental spatiotemporal topics, such as when, where and what people observe and are interested in, is important. In this research, we develop a model integrating soc

APA, Harvard, Vancouver, ISO, and other styles

28

Wei, Shuyue, Yuanyuan Zhang, Zimu Zhou, Tianlong Zhang, and Ke Xu. "FedSM: A Practical Federated Shared Mobility System." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4445–48. http://dx.doi.org/10.14778/3685800.3685896.

Full text

Abstract:

Shared mobility leverages under-utilized vehicles to offer on-demand transport services by sharing vehicles among users. It strives to match supply with demand via a series of data-intensive operations such as supply prediction and task assignment. However, its full potential is often compromised in practice as most shared mobility platforms operate in isolation, leading to sub-optimal resource utilization. In this demonstration, we advocate a federated approach to shared mobility, which enhances its effectiveness by enabling optimizations across platforms while retaining their autonomy. We de

APA, Harvard, Vancouver, ISO, and other styles

29

Wang, Yao, Zhongzhao Zhang, Lin Ma, and Jiamei Chen. "SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/395212.

Full text

Abstract:

Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification

APA, Harvard, Vancouver, ISO, and other styles

30

Rashid, Sami AbdulJabbar, Mustafa Maad Hamdi, Aymen Jalil AbdulElah, Yasir Jasim Ahmed Rajab, and Khalid AbdulHakeem Zaaile. "Link stability based multipath routing and effective mobility prediction in cognitive radio enabled vehicular ad hoc network." Bulletin of Electrical Engineering and Informatics 13, no. 1 (2024): 215–21. http://dx.doi.org/10.11591/eei.v13i1.5222.

Full text

Abstract:

Vehicular ad hoc networks (VANETs) provide a robust infrastructure for intelligent transportation system (ITS) applications. VANET communication involves vehicle-to-vehicle and vehicle-to-infrastructure connections, primarily with roadside units (RSUs). Analyzing cognitive radio (CR)-VANET studies revealed two key performance issues: high energy consumption and latency. To address these challenges, we propose a novel approach: link stability and mobility prediction-based clustered CR-VANETs, known as LMCCR-VANET. LMCCR-VANET consists of four main components: CR-VANET construction, clustering m

APA, Harvard, Vancouver, ISO, and other styles

31

Asad, Syed Muhammad, Jawad Ahmad, Sajjad Hussain, Ahmed Zoha, Qammer Hussain Abbasi, and Muhammad Ali Imran. "Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning." Sensors 20, no. 9 (2020): 2629. http://dx.doi.org/10.3390/s20092629.

Full text

Abstract:

Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway

APA, Harvard, Vancouver, ISO, and other styles

32

Durachman, Yusuf. "Analysis of Learning Techniques for Performance Prediction in Mobile Adhoc Networks." International Innovative Research Journal of Engineering and Technology 6, no. 2 (2020): IS—46—IS—53. http://dx.doi.org/10.32595/iirjet.org/v6i2.2020.141.

Full text

Abstract:

Current advancements in cellular technologies and computing have provided the basis for the unparalleled exponential development of mobile networking and software availability and quality combined with multiple systems or network software. Using wireless technologies and mobile ad-hoc networks, such systems and technology interact and collect information. To achieve the Quality of Service (QoS) criteria, the growing concern in wireless network performance and the availability of mobile users would support a significant rise in wireless applications. Predicting the mobility of wireless users an

APA, Harvard, Vancouver, ISO, and other styles

33

Yan, An, and Bill Howe. "Fairness-Aware Demand Prediction for New Mobility." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1079–87. http://dx.doi.org/10.1609/aaai.v34i01.5458.

Full text

Abstract:

Emerging transportation modes, including car-sharing, bike-sharing, and ride-hailing, are transforming urban mobility yet have been shown to reinforce socioeconomic inequity. These services rely on accurate demand prediction, but the demand data on which these models are trained reflect biases around demographics, socioeconomic conditions, and entrenched geographic patterns. To address these biases and improve fairness, we present FairST, a fairness-aware demand prediction model for spatiotemporal urban applications, with emphasis on new mobility. We use 1D (time-varying, space-constant), 2D (

APA, Harvard, Vancouver, ISO, and other styles

34

Wang, Huandong, Yong Li, Depeng Jin, and Zhu Han. "Attentional Markov Model for Human Mobility Prediction." IEEE Journal on Selected Areas in Communications 39, no. 7 (2021): 2213–25. http://dx.doi.org/10.1109/jsac.2021.3078499.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Chen, Jiamei. "Improved Markov Mobility Prediction Mechanism for HetNets." Journal of Information and Computational Science 11, no. 17 (2014): 6129–39. http://dx.doi.org/10.12733/jics20104999.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Wang, Huandong, Sihan Zeng, Yong Li, Pengyu Zhang, and Depeng Jin. "Human Mobility Prediction Using Sparse Trajectory Data." IEEE Transactions on Vehicular Technology 69, no. 9 (2020): 10155–66. http://dx.doi.org/10.1109/tvt.2020.3002222.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Fan, Zipei, Xuan Song, Renhe Jiang, Quanjun Chen, and Ryosuke Shibasaki. "Decentralized Attention-based Personalized Human Mobility Prediction." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, no. 4 (2019): 1–26. http://dx.doi.org/10.1145/3369830.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Garg, Neeraj, Sanjay K. Dhurandher, Petros Nicopolitidis, and J. S. Lather. "Efficient mobility prediction scheme for pervasive networks." International Journal of Communication Systems 31, no. 6 (2018): e3520. http://dx.doi.org/10.1002/dac.3520.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Daoui, M., A. M’zoughi, M. Lalam, M. Belkadi, and R. Aoudjit. "Mobility prediction based on an ant system." Computer Communications 31, no. 14 (2008): 3090–97. http://dx.doi.org/10.1016/j.comcom.2008.04.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Yan, Xiao-Yong, Chen Zhao, Ying Fan, Zengru Di, and Wen-Xu Wang. "Universal predictability of mobility patterns in cities." Journal of The Royal Society Interface 11, no. 100 (2014): 20140834. http://dx.doi.org/10.1098/rsif.2014.0834.

Full text

Abstract:

Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers

APA, Harvard, Vancouver, ISO, and other styles

41

Zhan, Yuting, Hamed Haddadi, and Afra Mashhadi. "Privacy-Aware Adversarial Network in Human Mobility Prediction." Proceedings on Privacy Enhancing Technologies 2023, no. 1 (2023): 556–70. http://dx.doi.org/10.56553/popets-2023-0032.

Full text

Abstract:

As mobile devices and location-based services are increasingly developed in different smart city scenarios and applications, many unexpected privacy leakages have arisen due to geolocated data collection and sharing. User re-identification and other sensitive inferences are major privacy threats when geolocated data are shared with cloud-assisted applications. Significantly, four spatio-temporal points are enough to uniquely identify 95% of the individuals, which exacerbates personal information leakages. To tackle malicious purposes such as user re-identification, we propose an LSTM-based adv

APA, Harvard, Vancouver, ISO, and other styles

42

Hong, Jinyu, Fan Zhou, Qiang Gao, Ping Kuang, and Kunpeng Zhang. "Mobility Prediction via Sequential Trajectory Disentanglement (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16230–31. http://dx.doi.org/10.1609/aaai.v37i13.26975.

Full text

Abstract:

Accurately predicting human mobility is a critical task in location-based recommendation. Most prior approaches focus on fusing multiple semantics trajectories to forecast the future movement of people, and fail to consider the distinct relations in underlying context of human mobility, resulting in a narrow perspective to comprehend human motions. Inspired by recent advances in disentanglement learning, we propose a novel self-supervised method called SelfMove for next POI prediction. SelfMove seeks to disentangle the potential time-invariant and time-varying factors from massive trajectories

APA, Harvard, Vancouver, ISO, and other styles

43

Zeng, Chengbo, Jiajia Zhang, Zhenlong Li, et al. "Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis." Journal of Medical Internet Research 23, no. 4 (2021): e27045. http://dx.doi.org/10.2196/27045.

Full text

Abstract:

Background Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. Objective The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South

APA, Harvard, Vancouver, ISO, and other styles

44

Jiang, Peng, Geng Wu, Yi-Chung Hu, Xue Zhang, and Yining Ren. "Novel Fractional Grey Prediction Model with the Change-Point Detection for Overseas Talent Mobility Prediction." Axioms 11, no. 9 (2022): 432. http://dx.doi.org/10.3390/axioms11090432.

Full text

Abstract:

Overseas students constitute the paramount talent resource for China, and, hence, overseas talent mobility prediction is crucial for the formulation of China’s talent strategy. This study proposes a new model for predicting the number of students studying abroad and returning students, based on the grey system theory, owing to the limited data and uncertainty of the influencing factors. The proposed model introduces change-point detection to determine the number of modeling time points, based on the fractional-order grey prediction model. We employed a change-point detection method to find the

APA, Harvard, Vancouver, ISO, and other styles

45

Dhandapani, Sridhar, and Chandrasekar Chelliah. "Markov Renewal Prediction and Radial Kronecker Neural Network Based Handover for Seamless Mobility." Instrumentation Mesure Métrologie 21, no. 5 (2022): 179–87. http://dx.doi.org/10.18280/i2m.210503.

Full text

Abstract:

Prevailing personal mobile network architectures make use of streamlined mobility control system, where the complete understanding is concentrated on single-end that results in scarce of dynamic mobility support when data volume is found to be large. The present-day networks necessitate seamless connections regardless of node position and connectivity that has to be accomplished between personal are network (PAN). In this work, a novel method called, Markov Renewal Prediction and Radial Kronecker Neural Network (MRP-RKNN) based optimized handover for seamless mobility in PAN is proposed. By em

APA, Harvard, Vancouver, ISO, and other styles

46

Hicham Hachemi, Mohammed, Sidi Mohammed Hadj Irid, Miloud Benchehima, and Mourad Hadjila. "Pedestrian mobility management for heterogeneous networks." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1530. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1530-1540.

Full text

Abstract:

<p>Pending the arrival of the next generation of 5G which is not yet deployed in<br />some countries like Algeria, 4G LTE remains one of the main mobile networks to ensure adequate quality services. Mostly, the deployment of femtocells to support the macrocell structure is crucial in the handover decision process. This paper presents a new approach called the epsilon Kalman Filter with normalized least-mean-square (ϵKFNLMS) to realize the handoff triggering in two-tier long-term evolution networks to ensure communication continuity to the pedestrian UE and improve mobility manageme

APA, Harvard, Vancouver, ISO, and other styles

47

Wang, Huandong, Qiaohong Yu, Yu Liu, Depeng Jin, and Yong Li. "Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 4 (2021): 1–24. http://dx.doi.org/10.1145/3494993.

Full text

Abstract:

With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application service providers (ASPs). On the other hand, the rising paradigm of knowledge graph (KG) provides us a promising solution to extract structured "knowledge" from massive trajectory data. In this paper, we focus on modeling users' spatio-temporal mobility patterns based on knowledge graph techniques, and predicting users' future movement based on the "knowledge" extracted from multiple sources in a cohesive manner. Specifica

APA, Harvard, Vancouver, ISO, and other styles

48

ZHU, HUAMIN, QINGHAI YANG, and KYUNG SUP KWAK. "LOCATION-AIDED RESOURCE RESERVATION FOR HANDOFF BASED ON MOBILITY PREDICTION." Journal of Circuits, Systems and Computers 16, no. 03 (2007): 403–20. http://dx.doi.org/10.1142/s0218126607003745.

Full text

Abstract:

In this paper, a location-aided resource reservation scheme for handoff based on mobility prediction is proposed for wireless cellular networks. We analyze the performance of the proposed scheme and propose a two-dimensional random walk model for simulation. Performance evaluation is done by computing several key performance criteria, i.e., prediction accuracy, average number of reservation per call, reservation efficiency, and reservation time overhead. The influences of threshold distance, average distance where the mobile station moves along a straight line, location error and sample time a

APA, Harvard, Vancouver, ISO, and other styles

49

Moumen, Idriss, Rabie Mahdaoui, Fatima Zahra Raji, Najat Rafalia, and Jaafar Abouchabaka. "Distributed Multi-Intersection Traffic Flow Prediction using Deep Learning." E3S Web of Conferences 477 (2024): 00049. http://dx.doi.org/10.1051/e3sconf/202447700049.

Full text

Abstract:

Efficient traffic flow prediction is paramount in modern urban transportation management, contributing significantly to energy efficiency and overall sustainability. Traditional traffic prediction models often struggle in complex urban traffic networks, especially at multi-intersection junctions. In response to this challenge, this research paper presents a pioneering approach that not only enhances traffic flow prediction accuracy but also indirectly supports energy efficiency. This study leverages deep learning techniques, specifically the Gated Recurrent Unit (GRU), to analyze traffic patte

APA, Harvard, Vancouver, ISO, and other styles

50

Arvanitis, Athanasios, Irini Furxhi, Thomas Tasioulis, and Konstantinos Karatzas. "Prediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data." Journal of Decision Analytics and Intelligent Computing 1, no. 1 (2021): 1–21. http://dx.doi.org/10.31181/jdaic1001202201f.

Full text

Abstract:

This paper demonstrates how a short-term prediction of the effective reproduction number (Rt) of COVID-19 in regions of Greece is achieved based on online mobility data. Various machine learning methods are applied to predict Rt and attribute importance analysis is performed to reveal the most important variables that affect the accurate prediction of Rt. Work and Park categories are identified as the most important mobility features when compared to the other attributes, with values of 0.25 and 0.24, respectively. Our results are based on an ensemble of diverse Rt methodologies to provide non

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!