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Journal articles on the topic 'Mobility patterns'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

du Mouza, Cédric, and Philippe Rigaux. "Mobility Patterns." GeoInformatica 9, no. 4 (2005): 297–319. http://dx.doi.org/10.1007/s10707-005-4574-9.

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2

Potter, David C. "IAS Mobility Patterns." Indian Journal of Public Administration 33, no. 4 (1987): 845–56. http://dx.doi.org/10.1177/0019556119870401.

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3

Zonoozi, M. M., and P. Dassanayake. "User mobility modeling and characterization of mobility patterns." IEEE Journal on Selected Areas in Communications 15, no. 7 (1997): 1239–52. http://dx.doi.org/10.1109/49.622908.

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4

Perez, K. "Towards new patterns of mobility." Journal of Epidemiology & Community Health 64, no. 01 (2009): 8–9. http://dx.doi.org/10.1136/jech.2009.090936.

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5

González, Marta C., César A. Hidalgo, and Albert-László Barabási. "Understanding individual human mobility patterns." Nature 453, no. 7196 (2008): 779–82. http://dx.doi.org/10.1038/nature06958.

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6

González, Marta C., César A. Hidalgo, and Albert-László Barabási. "Understanding individual human mobility patterns." Nature 458, no. 7235 (2009): 238. http://dx.doi.org/10.1038/nature07850.

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7

Burkhauser, Richard V., Barbara A. Butrica, and Michael J. Wasylenko. "Mobility Patterns of Older Homeowners." Research on Aging 17, no. 4 (1995): 363–84. http://dx.doi.org/10.1177/0164027595174001.

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8

Leyendecker, Katja. "Mobility patterns and urban structure." URBAN DESIGN International 21, no. 3 (2016): 276–77. http://dx.doi.org/10.1057/udi.2016.4.

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9

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

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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

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10

Chen, Yanyan, Zheng Zhang, and Tianwen Liang. "Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns." Sustainability 11, no. 19 (2019): 5452. http://dx.doi.org/10.3390/su11195452.

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Information and communication technology development has yielded large-scale spatiotemporal datasets, such as mobile phone, automatic collection system, and car-hailing data, which have resulted in new opportunities to investigate urban transportation systems. However, few studies have focused on regional mobility patterns. This study presents a multistep method for exploring traffic analysis zone (TAZ)-based mobility patterns and the corresponding relations with local land use characteristics. Based on a large-scale mobile phone dataset from a major mobile phone operator in Beijing, we applie

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11

Valentini, Enzo. "Patterns of Intergenerational Educational (Im)Mobility." Economies 12, no. 6 (2024): 126. http://dx.doi.org/10.3390/economies12060126.

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Intergenerational education mobility is a key dimension of social mobility and explores the extent to which educational attainment is transmitted across generations within a society. The implications of low education mobility concern both equity (everyone should have the same opportunities) and efficiency (it would be good for the economy and society if the most gifted and deserving young people were to study and not the children of the already educated). The literature identifies several drivers that can influence the level of social mobility in general and education mobility specifically, in

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12

Suhad, Faisal Behadili, Bertelle Cyrille, and E. George Loay. "Human Mobility Patterns Modelling using CDRs." International Journal of UbiComp (IJU) 7, no. 1 (2022): 7. https://doi.org/10.5281/zenodo.6782820.

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The research objectives are exploring characteristics of human mobility patterns, subsequently modelling them mathematically depending on inter-event time ∆t and traveled distances ∆rparameters using CDRs (Call Detailed Records). The observations are obtained from Armada festival in France. Understanding, modelling and simulating human mobility among urban regions is excitement approach, due to itsimportance in rescue situations for various events either indoor events like evacuation of buildings or outdoor ones like public assemblies,community evacuation in casesemerged during emergency situa

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13

Behadili, Suhad Faisal, Cyrille Bertelle, and Loay E. George. "Human Mobility Patterns Modelling using CDRs." International Journal of UbiComp 7, no. 1 (2016): 13–19. http://dx.doi.org/10.5121/iju.2016.7102.

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14

Grasias, S. John. "Mobile Computing Aggregated Human Mobility Patterns." International Journal of Computer Sciences and Engineering 6, no. 7 (2018): 1271–73. http://dx.doi.org/10.26438/ijcse/v6i7.12711273.

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15

Forbes, J. B. "EARLY INTRAORGANIZATIONAL MOBILITY: PATTERNS AND INFLUENCES." Academy of Management Journal 30, no. 1 (1987): 110–25. http://dx.doi.org/10.2307/255898.

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16

Forbes, J. Benjamin. "Early Intraorganizational Mobility: Patterns and Influences." Academy of Management Journal 30, no. 1 (1987): 110–25. http://dx.doi.org/10.5465/255898.

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17

Gittleman, Maury, and Mary Joyce. "Have Family Income Mobility Patterns Changed?" Demography 36, no. 3 (1999): 299. http://dx.doi.org/10.2307/2648054.

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18

SMIDT, MARC DE. "LABOUR MARKET SEGMENTATION AND MOBILITY PATTERNS." Tijdschrift voor Economische en Sociale Geografie 77, no. 5 (1986): 399–407. http://dx.doi.org/10.1111/j.1467-9663.1986.tb01724.x.

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19

Zhou, Xuan, Zhifeng Zhao, Rongpeng Li, Yifan Zhou, Jacques Palicot, and Honggang Zhang. "Human Mobility Patterns in Cellular Networks." IEEE Communications Letters 17, no. 10 (2013): 1877–80. http://dx.doi.org/10.1109/lcomm.2013.090213.130924.

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20

Gallotti, Riccardo, Armando Bazzani, Mirko Degli Esposti, and Sandro Rambaldi. "Entropic measures of individual mobility patterns." Journal of Statistical Mechanics: Theory and Experiment 2013, no. 10 (2013): P10022. http://dx.doi.org/10.1088/1742-5468/2013/10/p10022.

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21

Djurfeldt, Göran, and Cecilia Waldenström. "Mobility Patterns of Swedish Farming Households." Journal of Rural Studies 15, no. 3 (1999): 331–44. http://dx.doi.org/10.1016/s0743-0167(98)00070-9.

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22

Le Moigne, J. J., and A. M. Waxman. "Structured light patterns for robot mobility." IEEE Journal on Robotics and Automation 4, no. 5 (1988): 541–48. http://dx.doi.org/10.1109/56.20439.

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23

Hasan, Samiul, Christian M. Schneider, Satish V. Ukkusuri, and Marta C. González. "Spatiotemporal Patterns of Urban Human Mobility." Journal of Statistical Physics 151, no. 1-2 (2012): 304–18. http://dx.doi.org/10.1007/s10955-012-0645-0.

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24

Thajchayapong, S., and J. M. Peha. "Mobility patterns in microcellular wireless networks." IEEE Transactions on Mobile Computing 5, no. 1 (2006): 52–63. http://dx.doi.org/10.1109/tmc.2006.13.

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25

Hope, Joan. "Clearinghouse Report reveals student mobility patterns." Recruiting & Retaining Adult Learners 21, no. 1 (2018): 9. http://dx.doi.org/10.1002/nsr.30398.

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26

Hope, Joan. "Clearinghouse Report reveals student mobility patterns." Successful Registrar 18, no. 8 (2018): 9. http://dx.doi.org/10.1002/tsr.30518.

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27

Hope, Joan. "Clearinghouse Report reveals student mobility patterns." Enrollment Management Report 22, no. 7 (2018): 9. http://dx.doi.org/10.1002/emt.30466.

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28

Suhad, Faisal Behadili, Bertelle Cyrille, and E. George Loay. "Human Mobility Patterns Modelling using CDRs." International Journal of Ubiquitous Computing (IJU) 7, no. 1 (2023): 7. https://doi.org/10.5281/zenodo.7970641.

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Recent advances in computing have caused cyber security to become an increasingly critical issue that affects our everyday life. Both young and old in society are exposed to benefits and dangers that accompany technological advance. Cyber security education is a vital part of reducing the risks associated with cyber-threats. This is particularly important for current and future youth, who are the most technology-literate generations. Many research studies and competitions have been undertaken around the world to emphasize and identify the significance of cyber security as a relevant and pressi

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29

Wu, Laiyun, Samiul Hasan, Younshik Chung, and Jee Eun Kang. "Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences." Sustainability 13, no. 24 (2021): 13921. http://dx.doi.org/10.3390/su132413921.

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Characterizing individual mobility is critical to understand urban dynamics and to develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns. However, due to the limitations of the underlying datasets, these studies could not investigate how mobility patterns differ over user characteristics among demographic groups. In this study, we analyzed a large-scale Automatic Fare Collection (AFC) dataset of the transit system of Seoul, South Korea and investigated how mobility patterns vary over user characteristics

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30

DU BOIS, ANDRÉ RAUBER, PHIL TRINDER, and HANS-WOLFGANG LOIDL. "TOWARDS MOBILITY SKELETONS." Parallel Processing Letters 15, no. 03 (2005): 273–88. http://dx.doi.org/10.1142/s0129626405002210.

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In mobile languages programmers control the placement of code or computations in open networks, e.g., a program can migrate between locations. Mobile computations are typically stateful and interact with the state at each location in the network. We propose mobility skeletons, a library of parameterisable functions that encapsulate common patterns of mobile computation. Mobility skeletons are analogous to, but very different from, algorithmic skeletons — parameterisable functions encapsulating common patterns of parallel computation. We have identified three common patterns of mobile computati

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31

Su, Rongxiang, Jingyi Xiao, Elizabeth C. McBride, and Konstadinos G. Goulias. "Understanding senior's daily mobility patterns in California using human mobility motifs." Journal of Transport Geography 94 (June 2021): 103117. http://dx.doi.org/10.1016/j.jtrangeo.2021.103117.

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32

Ma, Wenchao, Yuguang Fang, and Phone Lin. "Mobility Management Strategy Based on User Mobility Patterns in Wireless Networks." IEEE Transactions on Vehicular Technology 56, no. 1 (2007): 322–30. http://dx.doi.org/10.1109/tvt.2006.883743.

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33

Lind, Pedro G., and Adriano Moreira. "Human Mobility Patterns at the Smallest Scales." Communications in Computational Physics 18, no. 2 (2015): 417–28. http://dx.doi.org/10.4208/cicp.120614.190115a.

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AbstractWe present a study on human mobility at small spatial scales. Differently from large scale mobility, recently studied through dollar-bill tracking and mobile phone data sets within one big country or continent, we report Brownian features of human mobility at smaller scales. In particular, the scaling exponents found at the smallest scales is typically close to one-half, differently from the larger values for the exponent characterizing mobility at larger scales. We carefully analyze 12-month data of the Eduroam database within the Portuguese university of Minho. A full procedure is in

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34

Oh, Seokjoon, Seungyoung Joo, Soohwan Kim, and Minkyoung Kim. "Decoding Urban Dynamics: Contextual Insights from Human Meta-Mobility Patterns." Systems 12, no. 8 (2024): 313. http://dx.doi.org/10.3390/systems12080313.

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Research on capturing human mobility patterns for efficient and sustainable urban planning has been widely conducted. However, studies that unveil spatial context beyond macro-level mobility patterns are relatively scarce. This study aims to analyze the spatiotemporal human meta-mobility patterns with rich context using POI data in Seoul from comprehensive perspectives. As a result, the floating population of Seoul exhibits regular and irregular cyclical mobility patterns on weekdays and weekends, respectively, stemming from the periodicity of the dominant POIs. Additionally, graph constructio

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35

Dalziel, Benjamin D., Babak Pourbohloul, and Stephen P. Ellner. "Human mobility patterns predict divergent epidemic dynamics among cities." Proceedings of the Royal Society B: Biological Sciences 280, no. 1766 (2013): 20130763. http://dx.doi.org/10.1098/rspb.2013.0763.

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The epidemic dynamics of infectious diseases vary among cities, but it is unclear how this is caused by patterns of infectious contact among individuals. Here, we ask whether systematic differences in human mobility patterns are sufficient to cause inter-city variation in epidemic dynamics for infectious diseases spread by casual contact between hosts. We analyse census data on the mobility patterns of every full-time worker in 48 Canadian cities, finding a power-law relationship between population size and the level of organization in mobility patterns, where in larger cities, a greater fract

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36

Xu, Zhenzhou, Ge Cui, Ming Zhong, and Xin Wang. "Anomalous Urban Mobility Pattern Detection Based on GPS Trajectories and POI Data." ISPRS International Journal of Geo-Information 8, no. 7 (2019): 308. http://dx.doi.org/10.3390/ijgi8070308.

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Anomalous urban mobility pattern refers to abnormal human mobility flow in a city. Anomalous urban mobility pattern detection is important in the study of urban mobility. In this paper, a framework is proposed to identify anomalous urban mobility patterns based on taxi GPS trajectories and Point of Interest (POI) data. In the framework, functional regions are first generated based on the distribution of POIs by the DBSCAN clustering algorithm. A Weighted Term Frequency-Inverse Document Frequency (WTF-IDF) method is proposed to identify function values in each region. Then, the Origin-Destinati

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37

Cho, Sung-Bae, and Jin-Young Kim. "Clustered embedding using deep learning to analyze urban mobility based on complex transportation data." PLOS ONE 16, no. 4 (2021): e0249318. http://dx.doi.org/10.1371/journal.pone.0249318.

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Urban mobility is a vital aspect of any city and often influences its physical shape as well as its level of economic and social development. A thorough analysis of mobility patterns in urban areas can provide various benefits, such as the prediction of traffic flow and public transportation usage. In particular, based on its exceptional ability to extract patterns from complex large-scale data, embedding based on deep learning is a promising method for analyzing the mobility patterns of urban residents. However, as urban mobility becomes increasingly complex, it becomes difficult to embed pat

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38

Lyons, Sean T., Linda Schweitzer, and Eddy S. W. Ng. "How have careers changed? An investigation of changing career patterns across four generations." Journal of Managerial Psychology 30, no. 1 (2015): 8–21. http://dx.doi.org/10.1108/jmp-07-2014-0210.

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Purpose – Popular literature argues that successive generations are experiencing more job changes and changes of employer. The “new careers” literature also proposes that career mobility patterns are becoming more diverse as people engage in more downward and lateral job changes and changes of occupation. The purpose of this paper is to test these assertions by comparing the career mobility patterns across four generations of workers. Design/methodology/approach – The authors analyzed the career mobility patterns of four generations of Canadian professionals (n=2,555): Matures (born prior to 1

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39

Orellana, D., C. Hermida, and P. Osorio. "A MULTIDISCIPLINARY ANALYTICAL FRAMEWORK FOR STUDYING ACTIVE MOBILITY PATTERNS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 527–34. http://dx.doi.org/10.5194/isprs-archives-xli-b2-527-2016.

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Intermediate cities are urged to change and adapt their mobility systems from a high energy-demanding motorized model to a sustainable low-motorized model. In order to accomplish such a model, city administrations need to better understand active mobility patterns and their links to socio-demographic and cultural aspects of the population. During the last decade, researchers have demonstrated the potential of geo-location technologies and mobile devices to gather massive amounts of data for mobility studies. However, the analysis and interpretation of this data has been carried out by speciali

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40

Orellana, D., C. Hermida, and P. Osorio. "A MULTIDISCIPLINARY ANALYTICAL FRAMEWORK FOR STUDYING ACTIVE MOBILITY PATTERNS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 527–34. http://dx.doi.org/10.5194/isprsarchives-xli-b2-527-2016.

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Intermediate cities are urged to change and adapt their mobility systems from a high energy-demanding motorized model to a sustainable low-motorized model. In order to accomplish such a model, city administrations need to better understand active mobility patterns and their links to socio-demographic and cultural aspects of the population. During the last decade, researchers have demonstrated the potential of geo-location technologies and mobile devices to gather massive amounts of data for mobility studies. However, the analysis and interpretation of this data has been carried out by speciali

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41

Yin, Yanzhong, Qunyong Wu, and Mengmeng Li. "Characterizing Intercity Mobility Patterns for the Greater Bay Area in China." ISPRS International Journal of Geo-Information 12, no. 1 (2022): 5. http://dx.doi.org/10.3390/ijgi12010005.

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Understanding intercity mobility patterns is important for future urban planning, in which the intensity of intercity mobility indicates the degree of urban integration development. This study investigates the intercity mobility patterns of the Greater Bay Area (GBA) in China. The proposed workflow starts by analyzing intercity mobility characteristics, proceeds to model the spatial-temporal heterogeneity of intercity mobility structures, and then identifies the intercity mobility patterns. We first conduct a complex network analysis, based on weighted degrees and the PageRank algorithm, to me

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42

Reiter, Samantha S., and Karin M. Frei. "Interpreting Past Human Mobility Patterns: A Model." European Journal of Archaeology 22, no. 4 (2019): 454–69. http://dx.doi.org/10.1017/eaa.2019.35.

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In the last decade, the exponential increase in migration studies focusing on the mobility of groups and single individuals—mostly based on aDNA and strontium isotope analyses—has provided an important extra layer of information regarding past social dynamics. The current relatively large quantity of data and their constant increase provide an opportunity to examine human mobility in unprecedented detail. In short, the course of academic dialogue is changing from producing evidence for movement to examining differences or similarities in human mobilities across temporal and geographical barrie

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43

Chopin, Julien, Stefano Caneppele, and Eric Beauregard. "An Analysis of Mobility Patterns in Sexual Homicide." Homicide Studies 24, no. 2 (2019): 178–202. http://dx.doi.org/10.1177/1088767919884601.

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This article—based on a national data set ( N = 173)—focuses on extrafamilial sexual homicides and their spatial mobility. The study combines the location of the crime scene and the offenders and victims’ residences in mobility crime triangles. The findings reveal that most of the homicides fall within the categories of offender mobility and total mobility. Our results show the validity of the distance decay function, with over 70% of homicides occurring within 10 km of the offender’s residence. It appears that under certain circumstances, sexual murderers perceive their surroundings as a safe

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44

Todoric, Jovana, Anatoliy Yamashkin, and Zlata Vuksanovic-Macura. "Spatial patterns of entertainment mobility in cities." Journal of the Geographical Institute Jovan Cvijic, SASA 72, no. 2 (2022): 207–20. http://dx.doi.org/10.2298/ijgi2202207t.

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Cultural and entertainment activities have become an important part of the life of the city's population. Spaces of entertainment are being developed in almost all parts of the city-in the inner city, in larger neighborhoods both in the city center and on the outskirts, near the centers of labor, shopping areas (e.g., attractive streets or shopping malls), or recreation areas. Hence the need to research the spatial patterns of entertainment mobility in the city. Studies to date have found that people spatially adjust their consumer activities (including entertainment) to their primary function

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45

Joseph, Joshua, Finale Doshi-Velez, and Nicholas Roy. "A Bayesian Nonparametric Approach to Modeling Mobility Patterns." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1587–93. http://dx.doi.org/10.1609/aaai.v24i1.7721.

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Constructing models of mobile agents can be difficult without domain-specific knowledge. Parametric models flexible enough to capture all mobility patterns that an expert believes are possible are often large, requiring a great deal of training data. In contrast, nonparametric models are extremely flexible and can generalize well with relatively little training data. We propose modeling the mobility patterns of moving agents as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides a flexible representation for each individual mobility pa

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46

Arambepola, Rohan, Kathryn L. Schaber, Catherine Schluth, et al. "Fine scale human mobility changes within 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income." PLOS Global Public Health 3, no. 7 (2023): e0002151. http://dx.doi.org/10.1371/journal.pgph.0002151.

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Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level within-city mobility data from 26 US cities between February 2 –August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June—August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude

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47

Barros, Matheus F. C., Carlos H. G. Ferreira, Lourenco A. P. Junior, Marco Mellia, Jussara M. Almeida, and Bruno Pereira dos Santos. "Understanding mobility in networks." ACM SIGMETRICS Performance Evaluation Review 49, no. 4 (2022): 124–30. http://dx.doi.org/10.1145/3543146.3543173.

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Motivated by the growing number of mobile devices capable of connecting and exchanging messages, we propose a methodology aiming to model and analyze node mobility in networks. We note that many existing solutions in the literature rely on topological measurements calculated directly on the graph of node contacts, aiming to capture the notion of the node's importance in terms of connectivity and mobility patterns beneficial for prototyping, design, and deployment of mobile networks. However, each measure has its specificity and fails to generalize the node importance notions that ultimately ch

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48

Wu, Hao, Xuehua Tang, Zhongyuan Wang, and Nanxi Wang. "Uncovering Abnormal Behavior Patterns from Mobility Trajectories." Sensors 21, no. 10 (2021): 3520. http://dx.doi.org/10.3390/s21103520.

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Using personal trajectory information to grasp the spatiotemporal laws of dangerous activities to curb the occurrence of criminal acts is a new opportunity and method for security prevention and control. This paper proposes a novel method to discover abnormal behaviors and judge abnormal behavior patterns using mobility trajectory data. Abnormal behavior trajectory refers to the behavior trajectory whose temporal and spatial characteristics are different from normal behavior, and it is an important clue to discover dangerous behavior. Abnormal patterns are the behavior patterns summarized base

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49

Stevens, Gillian. "Sex-Differentiated Patterns of Intergenerational Occupational Mobility." Journal of Marriage and the Family 48, no. 1 (1986): 153. http://dx.doi.org/10.2307/352239.

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50

Mertens, Karl. "Patterns of Evenki Mobility in Eastern Siberia." Sibirica 15, no. 1 (2016): 1–40. http://dx.doi.org/10.3167/sib.2016.150101.

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