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Journal articles on the topic 'Mobility prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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Burbey, Ingrid, and Thomas L. Martin. "A survey on predicting personal mobility." International Journal of Pervasive Computing and Communications 8, no. 1 (March 30, 2012): 5–22. http://dx.doi.org/10.1108/17427371211221063.

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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‐prediction projects comparing the raw location data, feature extraction, choice of prediction algorithms and their results.FindingsA new trend has emerged, that of employing additional context to improve or expand predictions. Incorporating temporal information enables location‐predictions farther out into the future. Appending place types or place names can improve predictions or develop prediction applications that could be used in any locale. Finally, the authors explore research into diverse types of context, such as people's personal contacts or health activities.Originality/valueThis overview provides a broad background for future research in prediction.

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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 (February 28, 2025): 284–92. https://doi.org/10.37917/ijeee.21.2.25.

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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 ones that utilize deep learning techniques. This study introduces a robust Model for predicting the future location path of a user based on their known previous locations. The study proposes the use of a Long Short-Term Memory (LSTM) prediction scheme, which is well-suited for learning from sequential data; then a fully connected neuron is employed to decrease the sparsity of the data, resulting in accurate predictions for the path of the user's next location. The suggested strategy demonstrates superior prediction accuracy compared to a state-of-the-art method, with improvements of up to a loss error of 0.002 based on real-life datasets (Geolife). The results demonstrate that the reliability of forecasts is excellent, indicating the accuracy of the predictions.

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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.

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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 number of potential trips, and the trip generation index. For trip attribute prediction, we develop an n -gram model incorporating a new feature, the trip attraction index, for each cluster of subway passengers. The incorporation of the three new features and the clustering of passengers considerably improves the accuracy of passenger mobility prediction, especially in large crowding events.

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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 (April 19, 2024): 166. http://dx.doi.org/10.3390/a17040166.

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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 represents roads as directed edges and road intersections as nodes. Individual vehicle mobility is modeled as transitions between nodes in the graph. A time series of aggregated mobility is computed by counting vehicles in each node at any given time. Three main approaches are employed to construct the aggregated mobility predictors. First, the behavior of the moving individuals is assumed to follow a Markov chain (MC) model whose transition matrix is inferred via a least squares estimation procedure; the recurrent application of this MC provides the aggregated mobility prediction values. Second, a multilayer perceptron (MLP) is trained so that—given the node occupation at a given time—it can recursively provide predictions for the next values of the time series. Third, we train a GNN (according to the city graph) with the time series data via a supervised learning formulation that computes—through an embedding construction for each node in the graph—the aggregated mobility predictions. Some mobility patterns are simulated in the city to generate different time series for testing purposes. The proposed schemes are comparatively assessed compared to different baseline prediction procedures. The comparison illustrates several limitations of the GNN approaches in the selected scenario and uncovers future lines of investigation.

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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 (April 2015): 38–50. http://dx.doi.org/10.4018/ijapuc.2015040104.

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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 location prediction schemes have made simplistic assumptions about mobility. However more advanced location prediction schemes using machine learning techniques have been used for wireless infrastructure networks. These approaches rely on the use of infrastructure and are therefore unsuitable for use in opportunistic networks or MANETs. To solve the problem of accurately predicting future location in non-infrastructure networks, the authors have investigated the prediction of continuous numerical coordinates using artificial neural networks. Simulation using three different mobility models representing human mobility has shown an average prediction error of less than 1m in normal circumstances.

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Yu, Zhiyong, Zhiwen Yu, and Yuzhong Chen. "Multi-hop Mobility Prediction." Mobile Networks and Applications 21, no. 2 (December 19, 2015): 367–74. http://dx.doi.org/10.1007/s11036-015-0668-2.

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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.

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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 incorporating the station-level anomalous passenger flow index and the collective mobility patterns of similar passengers. Compared with the benchmark models, the SCMM model achieves the highest prediction accuracy in both ordinary conditions and large crowding events. Our results highlight the importance of combining an individual’s own historical mobility data with collective mobility data and suggest the appropriate weights of individual and collective information considered in individual mobility modeling.

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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 (June 21, 2021): 1–24. http://dx.doi.org/10.1145/3459625.

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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 prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.

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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.

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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 (February 26, 2025): 1414. https://doi.org/10.3390/s25051414.

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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 such opportunistic networks aim to increase the probability of successful message transmission by leveraging area information derived from historical data to forecast potential encounters. However, accurately determining the precise locations of mobile devices remains highly challenging and necessitates a robust prediction mechanism to provide reliable insights into mobility encounters. In this study, we propose incorporating a random forest regressor (RFR) to predict the future location of mobile users, thereby enhancing message routing efficiency. The RFR utilizes mobility traces from diverse users and is equipped with sensors for computing and communication purposes. These predictions improve message routing performance and reduce energy and bandwidth resource utilization during routine data transmissions. To evaluate the proposed approach, we compared the predictive performance of the RFR against existing benchmark schemes, including the Gaussian process, using real-world mobility data traces. The mobility traces from the University of Southern California (USC) were employed to underpin the simulations. Our findings demonstrate that the RFR significantly outperformed both the Gaussian process and existing methods in predicting mobility encounters. Furthermore, the integration of mobility predictions into device-to-device (D2D) communication and traditional internet networks showed potential energy consumption reductions of up to one-third, highlighting the practical benefits of the proposed approach. The contribution of this research is that it highlights the limitations of existing mobility prediction models and develops new resource optimization and energy-efficient opportunistic networks that overcome these limitations.

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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.

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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 mobility prediction. We then, focus on challenges in mobility prediction from a deep literature study. These classifications and challenges are for serving further understanding, development and enhancement of the mobility prediction vision.

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Hua, Chen, Wencheng Zhang, Hanghao Fu, Yuhao Zhang, Biao Yu, Chunmao Jiang, Yuliang Wei, Ziyu Chen, and Xinkai Kuang. "The Prediction Method and Application of Off-Road Mobility for Ground Vehicles: A Review." World Electric Vehicle Journal 16, no. 1 (January 19, 2025): 47. https://doi.org/10.3390/wevj16010047.

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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, numerical simulation, and machine learning approaches. The strengths and weaknesses of these methods are compared and analyzed in detail. Subsequently, we explore the application scenarios of mobility prediction in military operations, subsea work, planetary exploration, and agricultural activities. Finally, we address several existing challenges in current mobility prediction methods and propose exploratory research directions focusing on key technologies and applications, such as real-time mobility prediction, terrain perception, path planning on deformable terrain, and autonomous mobility prediction for unmanned systems. These insights aim to provide valuable reference points for the future development of vehicle mobility prediction methods.

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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 (March 19, 2021): 1–23. http://dx.doi.org/10.1145/3448078.

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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 explanation values. We discover that users with less mobility activity, less visit diversity, and few sports facilities, bountiful entertainment around their visited locations are more likely to visit hospitals. Moreover, we conduct predictions on the populations with different demographic features, which achieves meaningful and insightful results, i.e. maintaining a high mobility activity is crucial for older people's health, while fast food store more substantially affects younger people's health; visit patterns can indicate females' health, while the neighborhood environment is more indicative of males, etc. These results shed light on how to use and understand large scale mobility data in health monitoring and other health-related applications in practice.

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

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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 lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models’ performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.

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Jang, Hee-Seon, and Jang-Hyun Baek. "Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks." Applied Sciences 12, no. 3 (January 25, 2022): 1252. http://dx.doi.org/10.3390/app12031252.

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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 process model was newly presented for the registration scheme considering mobility prediction and corresponding flexible TAL forming for two different environments: urban and rural. Simulation studies were also performed to validate the accuracy of the semi-Markov process model. Numerical results showed that analytical and simulation results were very close (average relative error of 1.4%). The registration cost decreased as the moving probability (q) to the predicted direction increased. The performance of the proposed scheme was superior to distance-based registration (DBR) or TAL-based scheme especially when q was high. When call-to-mobility ratio was less than or equal to 1 corresponding to current small cell configurations, the proposed scheme outperformed the DBR or TAL-based scheme.

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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 (December 31, 2017): 1634–42. http://dx.doi.org/10.31142/ijtsrd8240.

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Wu Xiaohua, and Li Jianping. "Routing Algorithm based on Mobility Prediction." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 2 (February 15, 2012): 218–26. http://dx.doi.org/10.4156/aiss.vol4.issue2.27.

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Jeong, Jaeseong, Dinand Roeland, Jesper Derehag, Ake Ai Johansson, Venkatesh Umaashankar, Gordon Sun, and Goran Eriksson. "Mobility Prediction for 5G Core Networks." IEEE Communications Standards Magazine 5, no. 1 (March 2021): 56–61. http://dx.doi.org/10.1109/mcomstd.001.2000046.

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F.Hassanin, Mohammad, and Amr Badr. "Mobility Prediction using Modified RBF Network." International Journal of Computer Applications 118, no. 25 (May 28, 2015): 1–4. http://dx.doi.org/10.5120/20959-3410.

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

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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.

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Abbas, Shatha, Mohammed J. F. Alenazi, and Amani Samha. "Mobility Prediction of Mobile Wireless Nodes." Applied Sciences 12, no. 24 (December 19, 2022): 13041. http://dx.doi.org/10.3390/app122413041.

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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), latent Dirichlet allocation (LDA), Gaussian naive Bayes (GNB), and support vector machine (SVM)—when using different mobility models, such as random walk, random direction, Gauss–Markov, and recurrent self-similar Gauss–Markov (RSSGM). Subsequently, classifiers were applied in order to detect the most efficient mobility model over wireless nodes. Random mobility models (i.e., random direction and random walk) provided fluctuating accuracy values when machine learning classifiers were applied—resulting values ranged from 39% to 81%. The Gauss–Markov and RSSGM models achieved good prediction accuracy in scenarios using a different number of access points in a defined area. Gauss–Markov reached 89% with the LDA classifier, whereas RSSGM showed the greatest accuracy with all classifiers and through various samples (i.e., 2000, 5000, and 10,000 steps during the whole experiment). Finally, the decision tree classifier obtained better overall results, achieving 98% predictive accuracy for 5000 steps.

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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 (August 1, 2021): 3229–40. https://doi.org/10.11591/ijece.v11i4.pp3229-3240.

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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 spatial relation of a set of points along the mobile node’s path and then provides a time-space mapping for each of the three components of the node’s location coordinates along the trajectory of the node. The proposed algorithm was tested and validated in MATLAB simulation platform using real and computer generated location data. The algorithm achieved an accurate mobility prediction with minimal error and provides promising results for many applications.

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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 (April 28, 2023): 374–96. http://dx.doi.org/10.3897/jucs.94514.

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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 the mobility areas in the study. The results show that models trained with the fusion of both types of data, especially long short-term memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, provide a more reliable prediction than models based only on the open data source. These results show that it is possible to predict the traffic entering a particular city in the next 30 minutes with an absolute error less than 10%. Thus, this work is a further step towards improving the prediction of human mobility in interurban areas by fusing open data with data from IoT systems.

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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) (April 28, 2023): 374–96. https://doi.org/10.3897/jucs.94514.

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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.

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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 social network service (SNS) data into the human mobility prediction model as background information of the mobility. We employ multi-modal deep learning models using Long short-term memory (LSTM) architecture to incorporate SNS data to a human mobility prediction model based on Global Navigation Satellite System (GNSS) data. We process anonymized interpolated GNSS trajectories from mobile phones into mobility sequence with discretized grid IDs, and apply several topic modeling methods on geo-tagged data to extract spatiotemporal topic features in each spatiotemporal unit similar to the mobility data. Thereafter, we integrate the two datasets in the multi-modal deep learning prediction models to predict city-scale mobility. The experiment proves that the models with SNS topics performed better than baseline models.

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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 (August 2024): 4445–48. http://dx.doi.org/10.14778/3685800.3685896.

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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 develop privacy-preserving operators and incentive mechanisms dedicated to supply prediction and task assignment in shared mobility and implement generic interfaces that support diverse prediction and assignment algorithms. We showcase the shared mobility system with real-world ride-hailing applications.

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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.

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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 of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements.

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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 (February 1, 2024): 215–21. http://dx.doi.org/10.11591/eei.v13i1.5222.

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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 model, speed-based mobility prediction, and link-based multipath routing. Initially, we establish cluster-based CR-VANETs to analyze and mitigate spectrum scarcity and power utilization problems in VANETs. Mobility prediction evaluates vehicle speed variations and predictions. Finally, employing link stability-based multipath routing (LSMR) in conjunction with the fuzzy interference model and ad hoc on-demand multipath distance vector (AOMDV) routing protocol ensures stable and efficient routing. Experimental results showcase the superiority of LMCCR-VANET. It exhibits enhanced energy efficiency, delivery rates, reduced energy consumption, end-to-end latency, and routing overhead when compared to recent works such as SCCR-VANET, CFCR-VANET, and MMCR-VANET.

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Durachman, Yusuf. "Analysis of Learning Techniques for Performance Prediction in Mobile Adhoc Networks." International Innovative Research Journal of Engineering and Technology 6, no. 2 (December 30, 2020): IS—46—IS—53. http://dx.doi.org/10.32595/iirjet.org/v6i2.2020.141.

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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 and systems performs an important role in the effective strategic decision making of wireless network bandwidth service providers. Furthermore, related to the defect-proneness, self-organization, and mobility aspect of such networks, new architecture problems occur. This paper proposes to predict and simulate the mobility of specific nodes on a mobile ad-hoc network, gradient boosting devices defined for the system will help. The proposed model not just to outperform previous mobility prediction models using simulated and real-world mobility instances, but provides better predictive accuracy by an enormous margin. The accuracy obtained helps the suggested mobility indicator in Mobile Adhoc Networks to increase the average level of performance.

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Yan, An, and Bill Howe. "Fairness-Aware Demand Prediction for New Mobility." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1079–87. http://dx.doi.org/10.1609/aaai.v34i01.5458.

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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 (space-varying, time-constant) and 3D (both time- and space-varying) convolutional branches to integrate heterogeneous features, while including fairness metrics as a form of regularization to improve equity across demographic groups. We propose two spatiotemporal fairness metrics, region-based fairness gap (RFG), applicable when demographic information is provided as a constant for a region, and individual-based fairness gap (IFG), applicable when a continuous distribution of demographic information is available. Experimental results on bike share and ride share datasets show that FairST can reduce inequity in demand prediction for multiple sensitive attributes (i.e. race, age, and education level), while achieving better accuracy than even state-of-the-art fairness-oblivious methods.

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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 (July 2021): 2213–25. http://dx.doi.org/10.1109/jsac.2021.3078499.

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Chen, Jiamei. "Improved Markov Mobility Prediction Mechanism for HetNets." Journal of Information and Computational Science 11, no. 17 (November 20, 2014): 6129–39. http://dx.doi.org/10.12733/jics20104999.

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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 (September 2020): 10155–66. http://dx.doi.org/10.1109/tvt.2020.3002222.

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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 (December 11, 2019): 1–26. http://dx.doi.org/10.1145/3369830.

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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 (January 22, 2018): e3520. http://dx.doi.org/10.1002/dac.3520.

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

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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 (May 5, 2020): 2629. http://dx.doi.org/10.3390/s20092629.

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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 operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (<0.01), entropy (>7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity (<0.3) and energy (<0.01) prove the efficacy of the proposed encryption scheme.

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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 (November 6, 2014): 20140834. http://dx.doi.org/10.1098/rsif.2014.0834.

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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 universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. By contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements.

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

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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 adversarial mechanism with representation learning to attain a privacy-preserving feature representation of the original geolocated data (i.e., mobility data) for a sharing purpose. These representations aim to maximally reduce the chance of user re-identification and full data reconstruction with a minimal utility budget (i.e., loss). We train the mechanism by quantifying privacy-utility trade-off of mobility datasets in terms of trajectory reconstruction risk, user re-identification risk, and mobility predictability. We report an exploratory analysis that enables the user to assess this trade-off with a specific loss function and its weight parameters. The extensive comparison results on four representative mobility datasets demonstrate the superiority of our proposed architecture in mobility privacy protection and the efficiency of the proposed privacy-preserving features extractor. We show that the privacy of mobility traces attains decent protection at the cost of marginal mobility utility. Our results also show that by exploring the Pareto optimal setting, we can simultaneously increase both privacy (45%) and utility (32%).

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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 (June 26, 2023): 16230–31. http://dx.doi.org/10.1609/aaai.v37i13.26975.

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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, which provides an interpretable view to understand the complex semantics underlying human mobility representations. To address the data sparsity issue, we present two realistic trajectory augmentation approaches to help understand the intrinsic periodicity and constantly changing intents of humans. In addition, a POI-centric graph structure is proposed to explore both homogeneous and heterogeneous collaborative signals behind historical trajectories. Experiments on two real-world datasets demonstrate the superiority of SelfMove compared to the state-of-the-art baselines.

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Zeng, Chengbo, Jiajia Zhang, Zhenlong Li, Xiaowen Sun, Bankole Olatosi, Sharon Weissman, and Xiaoming Li. "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 (April 13, 2021): e27045. http://dx.doi.org/10.2196/27045.

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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 Carolina. Methods This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. Results Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. Conclusions Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

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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 (August 26, 2022): 432. http://dx.doi.org/10.3390/axioms11090432.

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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 change points for determining the model length, based on the principle of new information priority, and used a fractional order accumulated generating operation to construct a grey prediction model. The two real data sets, the annual number of students studying abroad and returning students, were employed to verify the superiority of the proposed model. The results showed that the proposed model outperformed other benchmark models. Furthermore, the proposed model has been employed to predict the tendencies of overseas talent mobility in China by 2025. Further, certain policy recommendations for China’s talent strategy development have been proposed, based on the prediction results.

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Dhandapani, Sridhar, and Chandrasekar Chelliah. "Markov Renewal Prediction and Radial Kronecker Neural Network Based Handover for Seamless Mobility." Instrumentation Mesure Métrologie 21, no. 5 (December 15, 2022): 179–87. http://dx.doi.org/10.18280/i2m.210503.

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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 employing a Markov Renewal Prediction model for Seamless Mobility along with the two-hop network architecture, in this paper, we propose a transition probabilities (TP) function to mitigate the persistent handover issue in conventional wireless communication systems. The proposed Markov Renewal Prediction model for Seamless Mobility significantly reduces handover execution time and seamless mobility handover accuracy with efficient transition probabilities. In PANs, the unavoidable deployment of low power sink nodes permits the mobile nodes with many issues in terms of Quality of Service (QoS) due to complication of recurrent handovers due to high mobility. Addressing this issue of handover optimization in the deployment of PAN, this work proposes a model called to optimize the handovers in a cost-efficient manner. In this work, Radial Kronecker Delta Neural Network is utilized for handling frequent handovers based on received signal strength and cost metrics. Here, the resultant desired output is obtained using the Radial Kronecker function being a function of two variables with which optimized handover is performed. Simulation results presented in the study exhibits the performance and prediction rate of the proposed method in terms of handover execution time, seamless mobility prediction accuracy, mobility handover cost and packet loss rate.

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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 (December 1, 2022): 1530. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1530-1540.

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Pending the arrival of the next generation of 5G which is not yet deployed in 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 management. ϵKFNLMS uses a two-step process: a tracking process and a prediction process, to produce an optimal future state estimate at ”t+p”, where ”p” is the prediction footstep. The tracking process is performed by the Kalman filter, known for its precision in the state of the signal at time ”t”. It perfectly reduces the estimation error, injected afterward in the variable step-size NLMS algorithm (VSS-NLMS). While the prediction process is performed by the VSS-NLMS algorithm, an adaptive filter known for its prediction of the future state at ”t+p”. Thus, the goal is to achieve a faster convergence with a steady-state. ϵ value provides a precise setting of the handover trigger. Through different numerical simulations in several indoor environments, the results show that the performance and effectiveness of the proposed approach (ϵKFNLMS) provide lower mean square error (MSE), stable physical appearance in the prediction process (convergence with a steady-state), and excellent speed of convergence compared to the classical Normalized LMS (NLMS) and Li-NLMS adaptive filters.

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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 (December 27, 2021): 1–24. http://dx.doi.org/10.1145/3494993.

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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. Specifically, we propose a new type of knowledge graph, i.e., spatio-temporal urban knowledge graph (STKG), where mobility trajectories, category information of venues, and temporal information are jointly modeled by the facts with different relation types in STKG. The mobility prediction problem is converted to the knowledge graph completion problem in STKG. Further, a complex embedding model with elaborately designed scoring functions is proposed to measure the plausibility of facts in STKG to solve the knowledge graph completion problem, which considers temporal dynamics of the mobility patterns and utilizes PoI categories as the auxiliary information and background knowledge. Extensive evaluations confirm the high accuracy of our model in predicting users' mobility, i.e., improving the accuracy by 5.04% compared with the state-of-the-art algorithms. In addition, PoI categories as the background knowledge and auxiliary information are confirmed to be helpful by improving the performance by 3.85% in terms of accuracy. Additionally, experiments show that our proposed method is time-efficient by reducing the computational time by over 43.12% compared with existing methods.

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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.

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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 patterns simultaneously at multiple intersections within a city. By treating the entire traffic network as a distributed system, the model provides real-time predictions, allowing for better traffic management and reduced fuel consumption. Moreover, the incorporation of data fusion techniques, which integrate data from various sources, including traffic sensors and historical traffic information, bolsters the accuracy and robustness of predictions. By predicting traffic flows with precision, this research aids in optimizing traffic signal timing, reducing congestion, and ultimately promoting more efficient transportation systems, which, in turn, reduces fuel wastage and emissions. This study, therefore, advances intelligent transportation systems and offers a promising pathway toward improved energy efficiency in urban mobility.

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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 (June 2007): 403–20. http://dx.doi.org/10.1142/s0218126607003745.

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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 are investigated, and the effectiveness of mobility prediction is also evaluated. Performance comparison reveals that the proposed scheme performs better than a solely distance-based scheme without mobility prediction.

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Al-Hattab, Mohammad, and Nuha Hamada. "Prediction of nodes mobility in 3-D space." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (August 1, 2021): 3229. http://dx.doi.org/10.11591/ijece.v11i4.pp3229-3240.

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Recently, mobility prediction researches attracted increasing interests, especially for mobile networks where nodes are free to move in the three-dimensional 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 spatial relation of a set of points along the mobile node’s path and then provides a time-space mapping for each of the three components of the node’s location coordinates along the trajectory of the node. The proposed algorithm was tested and validated in MATLAB simulation platform using real and computer generated location data. The algorithm achieved an accurate mobility prediction with minimal error and provides promising results for many applications.

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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 (December 18, 2021): 1–21. http://dx.doi.org/10.31181/jdaic1001202201f.

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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-precautious and non-indulgent predictions. Random Forest algorithm achieved the highest R2 (0.8 approximately), Pearson’s and Spearman’s correlation values close to 0.9, outperforming in all metrics the other models. The model demonstrates robust results and the methodology overall represents a promising approach towards COVID-19 outbreak prediction. This paper can help health-related authorities when deciding on non-nosocomial interventions to prevent the spread of COVID-19.

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