Academic literature on the topic 'Mobility prediction'
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Journal articles on the topic "Mobility prediction"
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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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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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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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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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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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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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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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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, 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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Bahra, Nasrin, and Samuel Pierre. "A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks." Telecom 2, no. 2 (May 6, 2021): 199–212. http://dx.doi.org/10.3390/telecom2020013.
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Mobile networks are expected to face major problems such as low network capacity, high latency, and limited resources but are expected to provide seamless connectivity in the foreseeable future. It is crucial to deliver an adequate level of performance for network services and to ensure an acceptable quality of services for mobile users. Intelligent mobility management is a promising solution to deal with the aforementioned issues. In this context, modeling user mobility behaviour is of great importance in order to extract valuable information about user behaviours and to meet their demands. In this paper, we propose a hybrid user mobility prediction approach for handover management in mobile networks. First, we extract user mobility patterns using a mobility model based on statistical models and deep learning algorithms. We deploy a vector autoregression (VAR) model and a gated recurrent unit (GRU) to predict the future trajectory of a user. We then reduce the number of unnecessary handover signaling messages and optimize the handover procedure using the obtained prediction results. We deploy mobility data generated from real users to conduct our experiments. The simulation results show that the proposed VAR-GRU mobility model has the lowest prediction error in comparison with existing methods. Moreover, we investigate the handover processing and transmission costs for predictive and non-predictive scenarios. It is shown that the handover-related costs effectively decrease when we obtain a prediction in the network. For vertical handover, processing cost and transmission cost improve, respectively, by 57.14% and 28.01%.
Dissertations / Theses on the topic "Mobility prediction"
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Dong, Fang. "Moving Object Trajectory Based Spatio-Temporal Mobility Prediction." Thesis, KTH, Geodesi och geoinformatik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-99033.
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Bergh, Andre E. "Prediction assisted fast handovers for seamless IP mobility." Master's thesis, University of Cape Town, 2006. http://hdl.handle.net/11427/5248.
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Word processed copy.Includes bibliographical references (leaves 94-98).
This research investigates the techniques used to improve the standard Mobile IP handover process and provide proactivity in network mobility management. Numerous fast handover proposals in the literature have recently adopted a cross-layer approach to enhance movement detection functionality and make terminal mobility more seamless. Such fast handover protocols are dependent on an anticipated link-layer trigger or pre-trigger to perform pre-handover service establishment operations. This research identifies the practical difficulties involved in implementing this type of trigger and proposes an alternative solution that integrates the concept of mobility prediction into a reactive fast handover scheme.
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Venkatachalaiah, Suresh, and suresh@catt rmit edu au. "Mobility prediction and Multicasting in Wireless Networks: Performance and Analysis." RMIT University. Electrical and Computer Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070301.130037.
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Handoff is a call handling mechanism that is invoked when a mobile node moves from one cell to another. Such movement may lead to degradation in performance for wireless networks as a result of packet losses. A promising technique proposed in this thesis is to apply multicasting techniques aided by mobility prediction in order to improve handoff performance. In this thesis, we present a method that uses a Grey model for mobility prediction and a fuzzy logic controller that has been fine-tuned using evolutionary algorithms in order to improve prediction accuracy. We also compare the self-tuning algorithm with two evolutionary algorithms in terms of accuracy and their convergence times. Our proposed method takes into account signal strengths from the base stations and predicts the signal strength of the next candidate base station in order to provide improved handover performance. The primary decision for mobility prediction is the accurate prediction of signal strengths obtained from the base stations and remove any unwanted errors in the prediction using suitable optimisation techniques. Furthermore, the model includes the procedures of fine-tuning the predicted data using fuzzy parameters. We also propose suitable multicasting algorithms to minimise the reservation of overall network resource requirements during handoff with the mobility prediction information. To be able to efficiently solve the problem, the situation is modelled using a multicast tree that is defined to maintain connectivity with the mobile node, whilst ensuring bandwidth guarantees and a minimum hop-count. In this approach, we have tried to solve the problem by balancing two objectives through putting a weight on each of two costs. We provide a detailed description of an algorithm to implement join and prune mechanisms, which will help to build an optimal multicast tree with QoS requirements during handoff as well as incorporating dynamic changes in the positions of mobile nodes. An analysis of how mobility prediction helps in the selection of potential Access Routers (AR) with QoS requirements - which affects the multicast group size and bandwidth cost of the multicast tree -- is presented. The proposed technique tries to minimise the number of multicast tree join and prune operations. Our results show that the expected size of the multicast group increases linearly with an increase in the number of selected destination AR's for multicast during handoff. We observe that the expected number of joins and prunes from the multicast tree increases with group size. A special simulation model was developed to demonstrate both homogeneous and heterogeneous handoff which is an emerging requirement for fourth generation mobile networks. The model incorporates our mobility prediction model for heterogeneous handoff between the Wireless LAN and a cellular network. The results presented in this thesis for mobility prediction, multicasting techniques and heterogeneous handoff include proposed algorithms and models which aid in the understanding, analysing and reducing of overheads during handoff.
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Baumann, Paul. "Human Mobility and Application Usage Prediction Algorithms for Mobile Devices." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-212427.
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Mobile devices such as smartphones and smart watches are ubiquitous companions of humans’ daily life. Since 2014, there are more mobile devices on Earth than humans. Mobile applications utilize sensors and actuators of these devices to support individuals in their daily life. In particular, 24% of the Android applications leverage users’ mobility data. For instance, this data allows applications to understand which places an individual typically visits. This allows providing her with transportation information, location-based advertisements, or to enable smart home heating systems. These and similar scenarios require the possibility to access the Internet from everywhere and at any time. To realize these scenarios 83% of the applications available in the Android Play Store require the Internet to operate properly and therefore access it from everywhere and at any time.
Mobile applications such as Google Now or Apple Siri utilize human mobility data to anticipate where a user will go next or which information she is likely to access en route to her destination. However, predicting human mobility is a challenging task. Existing mobility prediction solutions are typically optimized a priori for a particular application scenario and mobility prediction task. There is no approach that allows for automatically composing a mobility prediction solution depending on the underlying prediction task and other parameters. This approach is required to allow mobile devices to support a plethora of mobile applications running on them, while each of the applications support its users by leveraging mobility predictions in a distinct application scenario.
Mobile applications rely strongly on the availability of the Internet to work properly. However, mobile cellular network providers are struggling to provide necessary cellular resources. Mobile applications generate a monthly average mobile traffic volume that ranged between 1 GB in Asia and 3.7 GB in North America in 2015. The Ericsson Mobility Report Q1 2016 predicts that by the end of 2021 this mobile traffic volume will experience a 12-fold increase. The consequences are higher costs for both providers and consumers and a reduced quality of service due to congested mobile cellular networks. Several countermeasures can be applied to cope with these problems. For instance, mobile applications apply caching strategies to prefetch application content by predicting which applications will be used next. However, existing solutions suffer from two major shortcomings. They either (1) do not incorporate traffic volume information into their prefetching decisions and thus generate a substantial amount of cellular traffic or (2) require a modification of mobile application code.
In this thesis, we present novel human mobility and application usage prediction algorithms for mobile devices. These two major contributions address the aforementioned problems of (1) selecting a human mobility prediction model and (2) prefetching of mobile application content to reduce cellular traffic.
First, we address the selection of human mobility prediction models. We report on an extensive analysis of the influence of temporal, spatial, and phone context data on the performance of mobility prediction algorithms. Building upon our analysis results, we present (1) SELECTOR – a novel algorithm for selecting individual human mobility prediction models and (2) MAJOR – an ensemble learning approach for human mobility prediction. Furthermore, we introduce population mobility models and demonstrate their practical applicability. In particular, we analyze techniques that focus on detection of wrong human mobility predictions. Among these techniques, an ensemble learning algorithm, called LOTUS, is designed and evaluated.
Second, we present EBC – a novel algorithm for prefetching mobile application content. EBC’s goal is to reduce cellular traffic consumption to improve application content freshness. With respect to existing solutions, EBC presents novel techniques (1) to incorporate different strategies for prefetching mobile applications depending on the available network type and (2) to incorporate application traffic volume predictions into the prefetching decisions. EBC also achieves a reduction in application launch time to the cost of a negligible increase in energy consumption.
Developing human mobility and application usage prediction algorithms requires access to human mobility and application usage data. To this end, we leverage in this thesis three publicly available data set. Furthermore, we address the shortcomings of these data sets, namely, (1) the lack of ground-truth mobility data and (2) the lack of human mobility data at short-term events like conferences. We contribute with JK2013 and UbiComp Data Collection Campaign (UbiDCC) two human mobility data sets that address these shortcomings. We also develop and make publicly available a mobile application called LOCATOR, which was used to collect our data sets.
In summary, the contributions of this thesis provide a step further towards supporting mobile applications and their users. With SELECTOR, we contribute an algorithm that allows optimizing the quality of human mobility predictions by appropriately selecting parameters. To reduce the cellular traffic footprint of mobile applications, we contribute with EBC a novel approach for prefetching of mobile application content by leveraging application usage predictions. Furthermore, we provide insights about how and to what extent wrong and uncertain human mobility predictions can be detected. Lastly, with our mobile application LOCATOR and two human mobility data sets, we contribute practical tools for researchers in the human mobility prediction domain.
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Chen, Guangshuo. "Human Habits Investigation : from Mobility Reconstruction to Mobile Traffic Prediction." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLX026/document.
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La capacité à prévoir les activités humaines a des implications essentielles dans de nombreux aspects des réseaux cellulaires. En particulier, la haute disponibilité de la prédiction de la mobilité peut permettre différents scénarios d'application; une meilleure compréhension de la demande de trafic de données mobiles peut aider à améliorer la conception de solutions pour l'équilibrage de la charge du réseau. Bien que de nombreux chercheurs aient étudié le sujet de la prédiction de la mobilité humaine, il y a eu peu de discussions sur l'anticipation du trafic de données mobiles dans les réseaux cellulaires.Pour comprendre la mobilité humaine, les ensembles de données de téléphones mobiles, consistant en des enregistrements de données de taxation (CDR), constituent un choix pratique d'empreintes humaines. Comme le déploiement du réseau cellulaire est très irrégulier et que les fréquences d'interaction sont généralement faibles, les données CDR sont souvent caractérisées par une parcimonie spatio-temporelle qui, à son tour, peut biaiser les analyses de mobilité basées sur de telles données et provoquer la perte de trajectoires individuelles.Dans cette thèse, nous présentons de nouvelles solutions de reconstruction de trajectoires individuelles et de prédiction de trafic de données mobiles individuelles. Nos contributions abordent les problèmes de (1) surmonter l'incomplétude des informations de mobilité pour l'utilisation des ensembles de données de téléphonie mobile et (2) prédire la future demande de trafic de données mobiles pour le support des applications de gestion de réseau.Premièrement, nous nous concentrons sur la faille de l'information sur la mobilité dans les ensembles de données de téléphones mobiles. Nous rapportons une analyse en profondeur de son effet sur la mesure des caractéristiques de mobilité individuelles et l'exhaustivité des trajectoires individuelles. En particulier, (1) nous fournissons une confirmation des résultats antérieurs concernant les biais dans les mesures de mobilité causées par la rareté temporelle de la CDR; (2) nous évaluons le décalage géographique provoqué par la cartographie des emplacements des utilisateurs vers les tours cellulaires et révélons le biais causé par la rareté spatiale de la CDR; (3) nous fournissons une estimation empirique de l'exhaustivité des données des trajectoires CDR individuelles. (4) nous proposons de nouvelles solutions de complétion CDR pour reconstruire incomplète. Nos solutions tirent parti de la nature des modèles de mouvements humains répétitifs et des techniques d'inférence de données de pointe et surpassent les approches précédentes illustrées par des simulations axées sur les données.Deuxièmement, nous abordons la prédiction des demandes de trafic de données mobiles générées par les abonnés individuels du réseau mobile. Sur la base de trajectoires complétées par nos solutions développées et nos historiques de consommation de données extraites d'un ensemble de données de téléphonie mobile à grande échelle, (1) nous étudions les limites de prévisibilité en mesurant la prévisibilité maximale que tout algorithme peut atteindre. les approches de prédiction du trafic de données mobiles qui utilisent les résultats de l'analyse théorique de la prévisibilité. Notre analyse théorique montre qu'il est théoriquement possible d'anticiper la demande individuelle avec une précision typique de 75% malgré l'hétérogénéité des utilisateurs et avec une précision améliorée de 80% en utilisant la prédiction conjointe avec des informations de mobilité. Notre pratique basée sur des techniques d'apprentissage automatique peut atteindre une précision typique de 65% et avoir un degré d'amélioration de 1% à 5% en considérant les déplacements individuels.En résumé, les contributions mentionnées ci-dessus vont dans le sens de l'utilisation des ensembles de données de téléphonie mobile et de la gestion des opérateurs de réseau et de leurs abonnésThe understanding of human behaviors is a central question in multi-disciplinary research and has contributed to a wide range of applications. The ability to foresee human activities has essential implications in many aspects of cellular networks. In particular, the high availability of mobility prediction can enable various application scenarios such as location-based recommendation, home automation, and location-related data dissemination; the better understanding of mobile data traffic demand can help to improve the design of solutions for network load balancing, aiming at improving the quality of Internet-based mobile services. Although a large and growing body of literature has investigated the topic of predicting human mobility, there has been little discussion in anticipating mobile data traffic in cellular networks, especially in spatiotemporal view of individuals.For understanding human mobility, mobile phone datasets, consisting of Charging Data Records (CDRs), are a practical choice of human footprints because of the large-scale user populations and the vast diversity of individual movement patterns. The accuracy of mobility information granted by CDR depends on the network infrastructure and the frequency of user communication events. As cellular network deployment is highly irregular and interaction frequencies are typically low, CDR data is often characterized by spatial and temporal sparsity, which, in turn, can bias mobility analyses based on such data and cause the loss of whereabouts in individual trajectories.In this thesis, we present novel solutions of the reconstruction of individual trajectories and the prediction of individual mobile data traffic. Our contributions address the problems of (1) overcoming the incompleteness of mobility information for the use of mobile phone datasets and (2) predicting future mobile data traffic demand for the support of network management applications.First, we focus on the flaw of mobility information in mobile phone datasets. We report on an in-depth analysis of its effect on the measurement of individual mobility features and the completeness of individual trajectories. In particular, (1) we provide a confirmation of previous findings regarding the biases in mobility measurements caused by the temporal sparsity of CDR; (2) we evaluate the geographical shift caused by the mapping of user locations to cell towers and reveal the bias caused by the spatial sparsity of CDR; (3) we provide an empirical estimation of the data completeness of individual CDR-based trajectories. (4) we propose novel solutions of CDR completion to reconstruct incomplete. Our solutions leverage the nature of repetitive human movement patterns and the state-of-the-art data inference techniques and outperform previous approaches shown by data-driven simulations.Second, we address the prediction of mobile data traffic demands generated by individual mobile network subscribers. Building on trajectories completed by our developed solutions and data consumption histories extracted from a large-scale mobile phone dataset, (1) we investigate the limits of predictability by measuring the maximum predictability that any algorithm has potential to achieve and (2) we propose practical mobile data traffic prediction approaches that utilize the findings of the theoretical predictability analysis. Our theoretical analysis shows that it is theoretically possible to anticipate the individual demand with a typical accuracy of 75% despite the heterogeneity of users and with an improved accuracy of 80% using joint prediction with mobility information. Our practical based on machine learning techniques can achieve a typical accuracy of 65% and have a 1%~5% degree of improvement by considering individual whereabouts.In summary, the contributions mentioned above provide a step further towards supporting the use of mobile phone datasets and the management of network operators and their subscribers
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Aljeri, Noura. "Efficient AI and Prediction Techniques for Smart 5G-enabled Vehicular Networks." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41497.
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With the recent growth and wide availability of heterogeneous wireless access technologies, inter-vehicle communication systems are expected to culminate in integrating various wireless standards for the next generation of connected and autonomous vehicles. The role of 5G-enabled vehicular networks has become increasingly important, as current Internet clients and providers have urged robustness and effectiveness in digital services over wireless networks to cope with the latest advances in wireless mobile communication. However, to enable 5G wireless technologies' dense diversity, seamless and reliable wireless communication protocols need to be thoroughly investigated in vehicular networks. 5G-enabled vehicular networks applications and services such as routing, mobility management, and service discovery protocols can integrate mobility-based prediction techniques to elevate those applications' performance with various vehicles, applications, and network measurements.
In this thesis, we propose a novel suite of 5G-enabled smart mobility prediction and management schemes and design a roadmap guide to mobility-based predictions for intelligent vehicular network applications and protocols. We present a thorough review and classification of vehicular network architectures and components, in addition to mobility management schemes, benchmarks advantages, and drawbacks. Moreover, multiple mobility-based schemes are proposed, in which vehicles' mobility is managed through the utilization of machine learning prediction and probability analysis techniques. We propose a novel predictive mobility management protocol that incorporates a new networks' infrastructure discovery and selection scheme. Next, we design an efficient handover trigger scheme based on time-series prediction and a novel online neural network-based next roadside unit prediction protocol for smart vehicular networks. Then, we propose an original adaptive predictive location management technique that utilizes vehicle movement projections to estimate the link lifetime between vehicles and infrastructure units, followed by an efficient movement-based collision detection scheme and infrastructure units localization strategy.
Last but not least, the proposed techniques have been extensively evaluated and compared to several benchmark schemes with various networks' parameters and environments. Results showed the high potentials of empowering vehicular networks' mobility-based protocols with the vehicles' future projections and the prediction of the network's status.
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Pamuluri, Harihara Reddy. "Predicting User Mobility using Deep Learning Methods." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-19340.
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Context: The context of this thesis to predict user mobility using deep learning algorithms which can increase the quality of service for the users and reduce the cost of paging for telecom carriers. Objectives: This study first investigates to find the suitable deep learning algorithms that can be used to predict user mobility and then an experiment is performed with the chosen algorithms as a global model and individual model then evaluate the performance of algorithms. Methods: Firstly, a Literature review is used to find suitable deep learning algorithms and then based on finding an experiment is performed to evaluate the chosen deep learning algorithms. Results: Results from the literature review show that the RNN, LSTM, and variants of the LSTM are the suitable deep learning algorithms. The models are evaluated with metrics accuracy. The results from the experiment showed that the individual model gives better performance in predicting user mobility when compared to the global model. Conclusions: From the results obtained from the experiment, it can be concluded that the individual model is the technique of choice in predicting user mobility.
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Baumann, Paul [Verfasser]. "Human Mobility and Application Usage Prediction Algorithms for Mobile Devices / Paul Baumann." München : Verlag Dr. Hut, 2016. http://d-nb.info/1120763134/34.
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Senatore, Carmine. "Prediction of mobility, handling, and tractive efficiency of wheeled off-road vehicles." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/37781.
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Our society is heavily and intrinsically dependent on energy transformation and usage.
In a world scenario where resources are being depleted while their demand is increasing, it
is crucial to optimize every process. During the last decade the concept of energy efficiency
has become a leitmotif in several fields and has directly influenced our everyday life: from
light bulbs to airplane turbines, there has been a general shift from pure performance to
better efficiency.
In this vein, we focus on the mobility and tractive efficiency of off-road vehicles. These
vehicles are adopted in military, agriculture, construction, exploration, recreation, and mining
applications and are intended to operate on soft, deformable terrain.
The performance of off-road vehicles is deeply influenced by the tire-soil interaction
mechanism. Soft soil can drastically reduce the traction performance of tires up to the
point of making motion impossible. In this study, a tire model able to predict the performance
of rigid wheels and flexible tires is developed. The model follows a semi-empirical
approach for steady-state conditions and predicts basic features, such as the drawbar pull,
the driving torque and the lateral force, as well as complex behaviors, such as the slip-sinkage
phenomenon and the multi-pass effect. The tractive efficiency of different tire-soil configurations is simulated and discussed. To investigate the handling and the traction efficiency,
the tire model is implemented into a four-wheel vehicle model. Several tire geometries, vehicle
configurations (FWD, RWD, AWD), soil types, and terrain profiles are considered to
evaluate the performance under different simulation scenarios. The simulation environment
represents an effective tool to realistically analyze the impact of tire parameters (size, inflation
pressure) and torque distribution on the energy efficiency. It is verified that larger
tires and decreased inflation pressure generally provide better traction and energy efficiency
(under steady-state working conditions). The torque distribution strategy between the axles
deeply affects the traction and the efficiency: the two variables canâ t clearly be maximized
at the same time and a trade-off has to be found.Ph. D.
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Lui, Sin Ting Angela. "Enhancing stochastic mobility prediction models for robust planetary navigation on unstructured terrain." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12904.
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Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is challenging on unstructured terrain, and especially on deformable terrain, due to the complex interaction between the platform and its environment. In this thesis, we propose to enhance stochastic transition models for planning, where the outcomes of the control actions are learnt from experience and represented statistically using probability density functions. These transition models that capture control uncertainty are known as Stochastic Mobility Prediction Models (SMPM). Rovers may traverse a mixture of rigid and deformable terrain. However, current SMPMs are only capable of estimating one dimension of control uncertainty in rigid terrain. We propose to enhance the SMPM by Learning from Exteroception, a training method that relies on sample executions of motion primitives on representative terrain and the corresponding platform configurations collected along the executed path. This method enables the estimation of the outcome of future control actions on deformable terrain. The SMPM is further enhanced by using multi-output Gaussian process regression by simultaneously considering the correlation between multiple dimensions of uncertainty. The enhanced SMPM is integrated into a Markov decision process framework and dynamic programming is used to construct a control policy for navigation to a goal region in a terrain map. We consider both rigid and deformable terrain, consisting of uneven ground, small rocks, and non-traversable rocks. Over 300 experimental trials are reported using a planetary rover platform in a Mars-analogue environment. Our results demonstrate increased path safety and reliability by the improved traversal cost and actions executed; due to the SMPM improvement in predicting control action outcomes.
Books on the topic "Mobility prediction"
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Davis, Andy. Predicting arsenic mobility as part of the Anaconda Sewage Treatment Lagoon Waterfowl Project. Place of publication not identified]: Camp Dresser & McKee, 1986.
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Zhang, Haoran. Handbook of Mobility Data Mining, Volume 2: Mobility Analytics and Prediction. Elsevier, 2023.
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Zhang, Haoran. Handbook of Mobility Data Mining, Volume 2: Mobility Analytics and Prediction. Elsevier, 2023.
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Morita, Hodaka. US–Japanese Differences in Employment Practices. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198812555.003.0009.
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This chapter shows that a model that captures the interconnections between firm dynamics, labour mobility, and specific human capital provides new explanations for and predictions on the US–Japanese differences in labour mobility, wage structure, and firm-sponsored training, based on cross-country differences in the importance of managerial capability. My argument is based on the idea that managerial capability increases its importance as an economy or an industry approaches the technological frontier. It also provides complementary explanations and predictions based on governmental interventions in firm dynamics, given that a guiding principle of Japanese industrial policy has been the regulation of so-called ‘excessive competition’.
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SIMVEC – Simulation und Erprobung in der Fahrzeugentwicklung. VDI Verlag, 2018. http://dx.doi.org/10.51202/9783181023334.
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Inhalt Zukünftiges Mobilitätsverhalten Mobilität 2050 – Selfdriving-eCo-Hyperflyyer, Drahtesel, oder was? . . . . . . . . . . . . . . . . . . .1 K. C. Keller, Aveniture GmbH, Freinsheim Ökobilanzierung Einfluss von Zellbauform und Zellchemie auf die Ökobilanz von batterieelektrischen Fahrzeugen . . . . . . .5 T. Semper, M. Clauß, IAV GmbH, Stollberg; A. Forell, IAV GmbH, Bad Cannstatt Anwendungsfallabhängige CO2 -Bilanzen elektrifizierter Fahrzeugantriebe – Use case driven CO2 footprint of electrified powertrains . . . . . . . . . . . . . . . . . . . . . . . . . . 17 O. Ludwig, J. Muth, M. Gernuks, H. Schröder, T. Löscheter Horst, Volkswagen AG, Wolfsburg Prädiktion der Lebensdauer von Traktionsbatteriesystemen für reale Nutzungsszenarien . . . .33 M. Ufert, Professur für Fahrzeugmechatronik, Technische Universität Dresden; A. Batzdorf, L. Morawietz, IAM GmbH, Dresden Predictive Energy Management Strategies for Hybrid Electric Vehicles: eHorizon for Battery Manage...
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Innovative Antriebe 2018. VDI Verlag, 2018. http://dx.doi.org/10.51202/9783181023341.
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Zukünftiges Mobilitätsverhalten Mobilität 2050 – Selfdriving-eCo-Hyperflyyer, Drahtesel, oder was? . . . . . . . . . . . . . . . . . . .1 K. C. Keller, Aveniture GmbH, Freinsheim Ökobilanzierung Einfluss von Zellbauform und Zellchemie auf die Ökobilanz von batterieelektrischen Fahrzeugen . . . . . . . . . .5 T. Semper, M. Clauß, IAV GmbH, Stollberg; A. Forell, IAV GmbH, Bad Cannstatt Anwendungsfallabhängige CO2 -Bilanzen elektrifizierter Fahrzeugantriebe –Use case driven CO2 footprint of electrified powertrains . . . . . . . . . . . . . . .17 O. Ludwig, J. Muth, M. Gernuks, H. Schröder, T. Löscheter Horst, Volkswagen AG, Wolfsburg Prädiktion der Lebensdauer von Traktionsbatteriesystemen für reale Nutzungsszenarien . . . .33 M. Ufert, Professur für Fahrzeugmechatronik, Technische Universität Dresden; A. Batzdorf, L. Morawietz, IAM GmbH, Dresden Predictive Energy Management Strategies for Hybrid Electric Vehicles: eHorizon for Battery Management System. . . . . 49 M. ...
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Nielsen, François. Genes and Status Achievement. Edited by Rosemary L. Hopcroft. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190299323.013.22.
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A number of human traits that are predictive of socioeconomic success (e.g., intelligence, certain personality traits, and educational attainment) or reflective of success (e.g., occupational prestige and earnings) have been found to be substantially affected by individual genetic endowments; some outcomes, such as educational attainment, are also affected by the family environment, although usually to a lesser extent. The associations among status-related traits are themselves largely due to genetic causes. By reshuffling the genes of parents at each generation, sexual reproduction produces a regression of status-relevant traits of offspring toward the population mean—downward for high-status parents, upward for low-status parents—generating social mobility in an achievement-oriented society. Incorporating the quantitative genetic decomposition of trait variance into genetic, shared environmental, and nonshared environmental sources into the classic sociological model of status achievement allows for a better understanding and measurement of central social stratification concepts, such as opportunity and ascription.
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Trussell, Jessica W., and M. Christina Rivera. Word Identification and Adolescent Deaf and Hard-of-Hearing Readers. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190880545.003.0011.
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Many deaf and hard-of-hearing (DHH) high school students graduate with reading abilities that leave them poorly prepared for postsecondary settings. In college, reading ability is an important predictor of graduation rates and level of degree attained, and the postsecondary degree a DHH student completes will affect his or her future earnings, upward mobility, and job satisfaction. Considering how important reading is to a DHH student’s future, this chapter will review the evidence base surrounding the foundational building block of reading, decoding. Researchers suggest that decoding instruction for adolescents should occur not only during language arts classes but also in the content areas (i.e., math, science, and social studies). This chapter reviews successful decoding strategies and suggests decoding strategies that teachers can use to support adolescents in various content-area disciplines. The authors discuss how teachers and parents can make strategic decisions when implementing decoding interventions that have no available evidence base.
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Chhibber, Pradeep K., and Rahul Verma. The Myth of Vote Buying in India. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190623876.003.0006.
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A common view is that in Indian elections parties, politicians, and voters are engaged in a quid-pro-quo in which citizens vote for a politician who offers them individual benefits. We find no evidence that voters exchange votes for benefits. In fact, ideology is a better predictor of the vote than the receipt of private or club goods. The use of cash is indeed widespread in India during election time but money is needed to build the campaign, to mobilize votes and for candidates, and to establish candidates’ credibility as leaders of import. We show this using the survey data from national election studies, a case study, and the results of a small experiment in Tamil Nadu.
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Aslanidis, Paris. Populism and Social Movements. Edited by Cristóbal Rovira Kaltwasser, Paul Taggart, Paulina Ochoa Espejo, and Pierre Ostiguy. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198803560.013.23.
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Populism is usually treated as an exclusively top-down affair where political party leaders mobilize diverse constituencies to reap electoral benefits. This perspective discounts a rich universe of bottom-up populist mobilization that remains exogenous to strict electoral contestation, thus unreasonably constraining the empirical study of the phenomenon. This chapter draws from social movement studies and social psychology to examine populist social movements under a comprehensive theoretical framework, aiming to bring together theorists of populism with scholars of social mobilization and encourage their mutually beneficial interaction. It argues that populism—as a compelling political dialect—has traditionally informed and continues to inform significant waves of grassroots contention around the world, triggering seemingly extraordinary developments at the party system level while also potentially determining processes of democratization. The chapter concludes by predicting an increasing relevance for grassroots populism, urging scholars to widen their scope of study by embracing it alongside its top-down variant.
Book chapters on the topic "Mobility prediction"
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Kim, Hyong S., and Wee-Seng Soh. "Mobility Prediction for QoS Provisioning." In Mobile and Wireless Internet, 77–108. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0225-8_4.
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Berradi, Zahra, Mohamed Lazaar, Oussama Mahboub, Hicham Omara, and Halim Berradi. "Stock Market Prediction Based on Advanced LSTM Models." In Smart Mobility and Industrial Technologies, 163–70. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-46849-0_18.
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Georgiou, Harris, Petros Petrou, Panagiotis Tampakis, Stylianos Sideridis, Eva Chondrodima, Nikos Pelekis, and Yannis Theodoridis. "Future Location and Trajectory Prediction." In Big Data Analytics for Time-Critical Mobility Forecasting, 215–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45164-6_8.
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Naretto, Francesca, Roberto Pellungrini, Salvatore Rinzivillo, and Daniele Fadda. "EXPHLOT: EXplainable Privacy Assessment for Human LOcation Trajectories." In Discovery Science, 325–40. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_22.
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AbstractHuman mobility data play a crucial role in understanding mobility patterns and developing analytical services across various domains such as urban planning, transportation, and public health. However, due to the sensitive nature of this data, accurately identifying privacy risks is essential before deciding to release it to the public. Recent work has proposed the use of machine learning models for predicting privacy risk on raw mobility trajectories and the use of shap for risk explanation. However, applying shap to mobility data results in explanations that are of limited use both for privacy experts and end-users. In this work, we present a novel version of the Expert privacy risk prediction and explanation framework specifically tailored for human mobility data. We leverage state-of-the-art algorithms in time series classification, as Rocket and InceptionTime, to improve risk prediction while reducing computation time. Additionally, we address two key issues with shap explanation on mobility data: first, we devise an entropy-based mask to efficiently compute shap values for privacy risk in mobility data; second, we develop a module for interactive analysis and visualization of shap values over a map, empowering users with an intuitive understanding of shap values and privacy risk.
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Samaan, Nancy, and Ahmed Karmouch. "An Evidence-Based Mobility Prediction Agent Architecture." In Lecture Notes in Computer Science, 230–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39646-8_22.
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Chen, Haoyuan, Yali Fan, Jing Jiang, and Xiang Chen. "Mobility Prediction Based on POI-Clustered Data." In Machine Learning and Intelligent Communications, 60–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00557-3_7.
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Persi, Stefano, Burcu Kolbay, Emilio Flores, and Irene Chausse. "Prediction, One of the Key Points in the Development of Electric Vehicles." In Lecture Notes in Mobility, 223–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65871-7_17.
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Heckmann, Kevin, Lena Elisa Schneegans, and Robert Hoyer. "Stage Prediction of Traffic Lights Using Machine Learning." In Towards the New Normal in Mobility, 635–53. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-39438-7_36.
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Jung, Jae-il, Jaeyeol Kim, and Younggap You. "Mobility Prediction Handover Using User Mobility Pattern and Guard Channel Assignment Scheme." In Universal Multiservice Networks, 155–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30197-4_16.
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Barhdadi, Mohamed, Badreddine Benyacoub, Abdelilah Sabri, and Mohamed Ouzineb. "Churn Prediction in Telecom Using VNS Algorithm with Bootstrap Resampling Technique." In Smart Mobility and Industrial Technologies, 65–71. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-46849-0_7.
Full textConference papers on the topic "Mobility prediction"
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Liu, Yuan, Xiaonan Chen, Zhou Lin, Yi-shou Wang, Qifeng Zhou, and Xinlin Qing. "Aeroengine Gas Path Parameter Trend Prediction Based on LSTM." In SAE 2023 Intelligent Urban Air Mobility Symposium. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-7087.
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<div class="section abstract"><div class="htmlview paragraph">Accurately predicting the trend of aero-engine gas path parameters is crucial for ensuring safe flight and enabling condition-based maintenance. However, the demanding and uncertain service environment introduces challenges in dealing with the noisy and non-stationary data collected by engine gas path sensors. Traditional time series models struggle to accurately predicts parameter trends, resulting in insufficient fitting and prediction accuracy. In this paper, we address these challenges by leveraging the characteristics of engine post-flight data and introducing Long Short-Term Memory (LSTM), a type of artificial neural network in deep learning. We construct both single-feature input and multi-feature input LSTM prediction models for six key indicators of engine gas path performance. We analyze the models' capabilities for single-step and multistep predictions. To evaluate the effectiveness of our approach, we compare the LSTM model with the traditional Autoregressive Moving Average (ARMA) model and support vector regression (SVR) method. The results demonstrate that the LSTM model outperforms the traditional ARMA and SVR models in terms of prediction accuracy and stability. This indicates that utilizing LSTM is an effective approach for improving the accuracy of engine gas path parameter prediction. By accurately predicting these parameters, we can enhance flight safety and enable more efficient condition based maintenance.</div></div>
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Suzuki, Masahiro, Shomu Furuta, and Yusuke Fukazawa. "Personalized human mobility prediction for HuMob challenge." In HuMob-Challenge '23: 1st International Workshop on the Human Mobility Prediction Challenge. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3615894.3628501.
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Wu, Xigang, Duanfeng Chu, Zejian Deng, Guipeng Xin, Hongxiang Liu, and Liping Lu. "Vehicle Trajectory Prediction in Highway Merging Area Using Interactive Graph Attention Mechanism." In SAE 2023 Intelligent Urban Air Mobility Symposium. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-7110.
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<div class="section abstract"><div class="htmlview paragraph">Accurately predicting the future trajectories of surrounding traffic agents is important for ensuring the safety of autonomous vehicles. To address the scenario of frequent interactions among traffic agents in the highway merging area, this paper proposes a trajectory prediction method based on interactive graph attention mechanism. Our approach integrates an interactive graph model to capture the complex interactions among traffic agents as well as the interactions between these agents and the contextual map of the highway merging area. By leveraging this interactive graph model, we establish an agent-agent interactive graph and an agent-map interactive graph. Moreover, we employ Graph Attention Network (GAT) to extract spatial interactions among trajectories, enhancing our predictions. To capture temporal dependencies within trajectories, we employ a Transformer-based multi-head self-attention mechanism. Additionally, GAT are utilized to model the interactions between traffic agents and the map. The method we propose comprehensively incorporates the influences of time, space, and the map on trajectories. The interactive graph models can serve as effective prior knowledge for learning-based approaches, thereby enhancing the acquisition of interaction patterns among traffic scenarios and facilitating the interpretability of the method. We evaluate the performances of our method using real-world trajectory datasets from the highway merging area, i.e., the Exits and Entries Drone Dataset (<i>exiD</i>). Comparative analysis against classical algorithms demonstrates a reduced trajectory prediction error for prediction horizons of both 3s and 4s.</div></div>
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Fan, Zhaoya, Jichao Chen, Tao Zhang, Ning Shi, and Wei Zhang. "Machine Learning for Formation Tightness Prediction and Mobility Prediction." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206208-ms.
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Abstract From the perspective of wireline formation test (WFT), formation tightness reflects the "speed" of pressure buildup while the pressure test is being conducted. We usually define a pressure test point that has a very low pressure-buildup speed as a tight point. The mobility derived from this kind of pressure point is usually less than 0.01md/cP; otherwise, the pressure points will be defined as valid points with valid formation pressure and mobility. Formation tightness reflects the formation permeability information and can be an indicator to estimate the difficulty of the WFT pumping and sampling operation. Mobility, as compared to permeability, reflects the dynamic supply capacity of the formation. A rapid and good mobility prediction based on petrophysical logging can not only directly provide valid formation productivity but can also evaluate the feasibility of the WFT and doing optimization work in advance. Compared to a time-consuming and costly drillstem test (DST) operation, the WFT is the most efficient and cost-saving method to confirm hydrocarbon presence. However, the success rate of WFT sampling operations in the deep Kuqa formation is less than 50% overall, mostly due to the formation tightness exceeding the capability of the tools. Therefore, a rapid mobility evaluation is necessary to meet WFT feasibility analysis. As companion work to a previous WFT optimization study(SPE-195932-MS), we further studied and discuss the machine learning for mobility prediction. In the previous study, we formed a mobility prediction workflow by doing a statistical analysis of more than 1000 pressure test points with several statistical mathematic methods, such as univariate linear regression (ULR), multivariate linear regression (MLR), neural network regression analysis (NNA), and decision tree classification analysis (DTA) methods. In this paper, the methods and principles of machine learning are expounded. A series of machine learning methods were tested. The algorithms that are appropriate for these specific data set were selected. Includes DTA, discriminant analysis (DA), logistic regression, support vector machine (SVM), K-nearest neighbor (KNN) for formation tightness prediction and linear regression, DTA, SVM, Gaussian process regression SVM, random tree, neural network analysis for mobility prediction. Contrastive analysis reveals that: The SVM classifier has the best result over other methods for formation tightness probability prediction. Based on R squared and RMSE analysis, linear regression, GPR, and NNA delivered relatively good results compared with other mobility prediction methods. An optimized data processing workflow was proposed, and it delivered a better result than the workflow proposed in SPE-195932-MS under the same training and testing dataset condition. The comparison between measured mobility and predicted mobility results reveals that, in most situations, the predicted mobility and measured mobility matched very well with each other. WFT were conducted in newly drilled wells. Sampling success rate also achieved 100% in these wells by optimizing the WFT tool string and sampling stations selection in advance, and NPT is significantly reduced.
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Khan, Saeed, Ash Rahimi, and Neil Bergmann. "Urban Mobility Prediction Using Twitter." In 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). IEEE, 2020. http://dx.doi.org/10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00082.
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Sun, Michael H., and Douglas M. Blough. "Mobility prediction using future knowledge." In the 10th ACM Symposium. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1298126.1298167.
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Liu, Lu, Junyao Guo, Sihai Zhang, and Jinkang Zhu. "Similar User Assisted Mobility Prediction." In 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2019. http://dx.doi.org/10.1109/wcsp.2019.8928002.
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Dang, Minling, Zhiwen Yu, Liming Chen, Zhu Wang, Bin Guo, and Chris Nugent. "Human Mobility: Prediction and Predictability." In 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2024. http://dx.doi.org/10.1109/percomworkshops59983.2024.10502436.
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Terashima, Haru, Naoki Tamura, Kazuyuki Shoji, Shin Katayama, Kenta Urano, Takuro Yonezawa, and Nobuo Kawaguchi. "Human Mobility Prediction Challenge: Next Location Prediction using Spatiotemporal BERT." In HuMob-Challenge '23: 1st International Workshop on the Human Mobility Prediction Challenge. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3615894.3628498.
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Amirrudin, Nurul Ain, Sharifah H. S. Ariffin, N. N. N. Abd Malik, and N. Effiyana Ghazali. "User's mobility history-based mobility prediction in LTE femtocells network." In 2013 IEEE International RF and Microwave Conference (RFM). IEEE, 2013. http://dx.doi.org/10.1109/rfm.2013.6757228.
Full textReports on the topic "Mobility prediction"
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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, March 2023. http://dx.doi.org/10.31979/mti.2023.2211.
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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration.
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Bradley, Thomas. Research Performance Final Report: Mobility and Energy Improvements Realized through Prediction-based Vehicle Powertrain Control and Traffic Management. Office of Scientific and Technical Information (OSTI), May 2022. http://dx.doi.org/10.2172/1868335.
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Yang, Yu, and Hen-Geul Yeh. Electrical Vehicle Charging Infrastructure Design and Operations. Mineta Transportation Institute, July 2023. http://dx.doi.org/10.31979/mti.2023.2240.
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California aims to achieve five million zero-emission vehicles (ZEVs) on the road by 2030 and 250,000 electrical vehicle (EV) charging stations by 2025. To reduce barriers in this process, the research team developed a simulation-based system for EV charging infrastructure design and operations. The increasing power demand due to the growing EV market requires advanced charging infrastructures and operating strategies. This study will deliver two modules in charging station design and operations, including a vehicle charging schedule and an infrastructure planning module for the solar-powered charging station. The objectives are to increase customers’ satisfaction, reduce the power grid burden, and maximize the profitability of charging stations using state-of-the-art global optimization techniques, machine-learning-based solar power prediction, and model predictive control (MPC). The proposed research has broad societal impacts and significant intellectual merits. First, it meets the demand for green transportation by increasing the number of EV users and reducing the transportation sector’s impacts on climate change. Second, an optimal scheduling tool enables fast charging of EVs and thus improves the mobility of passengers. Third, the designed planning tools enable an optimal design of charging stations equipped with a solar panel and battery energy storage system (BESS) to benefit nationwide transportation system development.
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Audoly, Richard, Rory McGee, Sergio Ocampo, and Gonzalo Paz-Pardo. The Life-Cycle Dynamics of Wealth Mobility. Federal Reserve Bank of New York, April 2024. http://dx.doi.org/10.59576/sr.1097.
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We use twenty-five years of tax records for the Norwegian population to study the mobility of wealth over people’s lifetimes. We find considerable wealth mobility over the life cycle. To understand the underlying mobility patterns, we group individuals with similar wealth rank histories using agglomerative hierarchical clustering, a tool from statistical learning. The mobility patterns we elicit provide evidence of segmented mobility. Over 60 percent of the population remains at the top or bottom of the wealth distribution throughout their lives. Mobility is driven by the remaining 40 percent, who move only within the middle of the distribution. Movements are tied to differential income trajectories and business activities across groups. We show parental wealth is the key predictor of who is persistently rich or poor, while human capital is the main predictor of those who rise and fall through the middle of the distribution.
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Eylander, John, Michael Lewis, Maria Stevens, John Green, and Joshua Fairley. An investigation of the feasibility of assimilating COSMOS soil moisture into GeoWATCH. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41966.
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This project objective evaluated the potential of improving linked weather-and-mobility model predictions by blending soil moisture observations from a Cosmic-ray Soil Moisture Observing System (COSMOS) sensor with weather-informed predictions of soil moisture and soil strength from the Geospatial Weather-Affected Terrain Conditions and Hazards (GeoWATCH). Assimilating vehicle-borne COSMOS observations that measure local effects model predictions of soil moisture offered potential to produce more accurate soil strength and vehicle mobility forecast was the hypothesis. This project compared soil moisture observations from a COSMOS mobile sensor driven around an area near Iowa Falls, IA, with both GeoWATCH soil moisture predictions and in situ probe observations. The evaluation of the COSMOS rover data finds that the soil moisture measurements contain a low measurement bias while the GeoWATCH estimates more closely matched the in situ data. The COSMOS rover captured a larger dynamic range of soil moisture conditions as compared to GeoWATCH, capturing both very wet and very dry soil conditions, which may better flag areas of high risk for mobility considerations. Overall, more study of the COSMOS rover is needed to better understand sensor performance in a variety of soil conditions to determine the feasibility of assimilating the COSMOS rover estimates into GeoWATCH.
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Policy Support Activity, Myanmar Agriculture. Is poverty in Myanmar on the rise? Poverty predictions from Google mobility data. Washington, DC: International Food Policy Research Institute, 2021. http://dx.doi.org/10.2499/p15738coll2.134385.
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Albrecht, Jochen, Andreas Petutschnig, Laxmi Ramasubramanian, Bernd Resch, and Aleisha Wright. Comparing Twitter and LODES Data for Detecting Commuter Mobility Patterns. Mineta Transportation Institute, May 2021. http://dx.doi.org/10.31979/mti.2021.2037.
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Local and regional planners struggle to keep up with rapid changes in mobility patterns. This exploratory research is framed with the overarching goal of asking if and how geo-social network data (GSND), in this case, Twitter data, can be used to understand and explain commuting and non-commuting travel patterns. The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips. It turns out that the reverse is true and the common practice of employing LODES data to extrapolate to overall traffic demand is indeed justified. This means that expensive and rarely comprehensive surveys are now only needed to capture trip purposes. Regardless of trip purpose (e.g., shopping, regular recreational activities, dropping kids at school), the LODES data is an excellent predictor of overall road segment loads.
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Tarko, Andrew P., Raul Pineda-Mendez, and Qiming Guo. Predicting the Impact of Changing Speed Limits on Traffic Safety and Mobility on Indiana Freeways. Purdue University, December 2019. http://dx.doi.org/10.5703/1288284316922.
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Wong, J. Y., C. Senatore, P. Jayakumar, and K. Iagnemma. Predicting Mobility Performance of a Small, Lightweight Track System Using the Computer-Aided Method NTVPM. Fort Belvoir, VA: Defense Technical Information Center, April 2015. http://dx.doi.org/10.21236/ada615244.
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Benekohal, Rahim, and Hongjae Jeon. Results of Work Zone Queue Analysis Training Classes. Illinois Center for Transportation, November 2023. http://dx.doi.org/10.36501/0197-9191/23-024.
Full textAbstract:
This white paper summarizes the results from three training classes on queue analysis in work zones. Accurately predicting the mobility impacts of work zones will enable designers to identify effective countermeasures to improve mobility and safety in work zones. The course provides hands-on training on basic analytical methods (queue analysis methods) to compute work zone performance measures such as capacity, speed, queue length, delay, and users’ costs. The capabilities and limitations of WorkZoneQ-Pro and the Highway Capacity Manual procedure for work zones are discussed, and basic guidance on how to use them is presented. The first and second courses were in-person and lasted 1.5 days. The third course was virtual and lasted only one day with reduced content. The evaluation results indicate that the participants very much liked the trainings and learned a lot. The in-person classes had slightly higher scores than the virtual class. The average scores were 4.4–4.9 (out of 5) for the in-person classes and 4.2–4.9 (out of 5) for the online class.