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Relevant bibliographies by topics / Mobility prediction

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Academic literature on the topic 'Mobility prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 (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‐predi

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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 (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

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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 num

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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 (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 re

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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 (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 l

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

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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 incorporati

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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 (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 predictio

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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 mobi

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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 (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

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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.<br>Includes bibliographical references (leaves 94-98).<br>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 pra

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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

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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 s

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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 réseau

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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 an

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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 experi

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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,

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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 ar

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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. 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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Nyangaresi, Vincent. Statistical Analysis of the GSM (Global System for Mobile Communications) Mobility Prediction Models. GRIN Verlag GmbH, 2017.

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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 interventi

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Burton, Anthony Kim. Patterns of lumbar sagittal mobility and their predictive value in the natural history of back and sciativ pain. 1987.

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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. Ger

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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. Lös

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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

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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 instruct

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Book chapters on the topic "Mobility prediction"

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Tympakianaki, Athina, Mohammadmahdi Rahimiasl, Charis Chalkiadakis, et al. "Predicting Demand and Supply in a Real-Time Traffic Management Framework." In Lecture Notes in Mobility. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85578-8_45.

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Abstract Predicting the future supply and demand of a transport network are challenging and important problems in real-time traffic management systems that are essential to enhance the decision-making process for deploying adequate traffic strategies under different conditions (e.g., road works, accidents). In the context of the TANGENT H2020 project, simulation-based and data-driven methodologies are developed focusing on the real-time demand and supply prediction problems. This paper focuses on the development and integration of the demand and supply models as well as incident detection meth

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Kim, Hyong S., and Wee-Seng Soh. "Mobility Prediction for QoS Provisioning." In Mobile and Wireless Internet. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0225-8_4.

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Zhang, Haijun, and Ning Yang. "Learning Traffic and Mobility Prediction." In Deep Learning in Wireless Communications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6314-6_5.

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Oikonomou, Maria G., Apostolos Ziakopoulos, Shanna Lucchesi, Monica Olyslagers, and George Yannis. "Traffic Simulation and Safety Assessment Requirements for Enhancing Road Safety Prediction Tools." In Lecture Notes in Mobility. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85578-8_109.

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Abstract Improving road safety prediction tools requires assessing established traffic simulation tools and safety assessment methods. Enhancing these tools with innovative data sources and methods can significantly reduce urban crashes and their impact. To achieve this, it is imperative to identify the requirements and gaps of relevant stakeholders in terms of professional road safety analysis tools. The present study aims to utilize association rule mining to determine underlying profiles of local stakeholders who are identified as hands-on practitioners. To accomplish this objective, a dedi

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Silveira-Santos, Tulio, Anestis Papanikolaou, Thais Rangel, and Jose Manuel Vassallo. "Exploring the Dynamics of Ride-Hailing Fares in Madrid: A Machine Learning Approach." In Lecture Notes in Mobility. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85578-8_73.

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Abstract Ride-hailing apps are getting increasingly common in cities all around the world. However, the major factors that determine how supply and demand interact to determine the ultimate prices are still mostly understood. By using statistical and supervised machine learning techniques (Linear Regression, Decision Tree, and Random Forest), this study aims to comprehend and forecast the behavior of ride-hailing fares. Ten months’ worth of data were taken from the Uber Application Programming Interface for the city of Madrid and used to calibrate the model. The results show that the Random Fo

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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. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-46849-0_18.

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Yao, Yao, and Zijin Guo. "Prediction and Analysis of Urban Mobility Based on Attention Mechanism and Geographic Information Embedding." In Urban Human Mobility. CRC Press, 2025. https://doi.org/10.1201/9781003503262-12.

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Naretto, Francesca, Roberto Pellungrini, Salvatore Rinzivillo, and Daniele Fadda. "EXPHLOT: EXplainable Privacy Assessment for Human LOcation Trajectories." In Discovery Science. 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 fo

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Georgiou, Harris, Petros Petrou, Panagiotis Tampakis, et al. "Future Location and Trajectory Prediction." In Big Data Analytics for Time-Critical Mobility Forecasting. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45164-6_8.

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Samaan, Nancy, and Ahmed Karmouch. "An Evidence-Based Mobility Prediction Agent Architecture." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39646-8_22.

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Conference papers on the topic "Mobility prediction"

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Saputra, Ragil, Suprapto, and Agus Sihabuddin. "Mobility Prediction Using Markov Models: A Survey." In 2024 7th International Conference on Informatics and Computational Sciences (ICICoS). IEEE, 2024. http://dx.doi.org/10.1109/icicos62600.2024.10636860.

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Han, Zengyi, Xuefu Dong, Liqiang Xu, et al. "RideGuard: Micro-Mobility Steering Maneuver Prediction with Smartphones." In 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2024. http://dx.doi.org/10.1109/icdcs60910.2024.00100.

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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. 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 characteristi

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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. 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. 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 high

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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

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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. 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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Reports 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, 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 successfu

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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), 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, 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

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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.), 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 soi

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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, 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,

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Policy Support Activity, Myanmar Agriculture. Is poverty in Myanmar on the rise? Poverty predictions from Google mobility data. 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, 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 e

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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, 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. Defense Technical Information Center, 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, 2023. http://dx.doi.org/10.36501/0197-9191/23-024.

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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 guidanc

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