Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data

Computers, Environment and Urban Systems - Tập 77 - Trang 101346 - 2019
Mingxiao Li1,2,3, Song Gao1, Feng Lu4,5,2, Hengcai Zhang2
1Geospatial Data Science Lab, Department of Geography, University of Wisconsin, Madison, WI 53706, USA
2State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3University of the Chinese Academy of Sciences, Beijing 100049, China
4Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China
5Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

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Tài liệu tham khảo

Ahas, 2010, Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area: Case study with mobile positioning data, Transportation Research Part C: Emerging Technologies, 18, 45, 10.1016/j.trc.2009.04.011

Algizawy, 2017, Real-time large-scale map matching using mobile phone data, ACM Transactions on Knowledge Discovery from Data, 11, 52, 10.1145/3046945

Breiman, 2001, Random forests, 45, 5

Calabrese, 2013, Understanding individual mobility patterns from urban sensing data: A mobile phone trace example, Transportation research part C: emerging technologies, 26, 301, 10.1016/j.trc.2012.09.009

Cao, 2015, A scalable framework for spatiotemporal analysis of location-based social media data, Computers, Environment and Urban Systems, 51, 70, 10.1016/j.compenvurbsys.2015.01.002

Capela, 2016, Evaluation of a smartphone human activity recognition application with able-bodied and stroke participants, Journal of Neuroengineering and Rehabilitation, 13, 5, 10.1186/s12984-016-0114-0

Cascetta, 1984, Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator, Transportation Research Part B: Methodological, 18, 289, 10.1016/0191-2615(84)90012-2

Chen, 2017, Towards an adaptive completion of sparse call detail records for mobility analysis, 302

Chen, 2015, Probabilistic multimodal map matching with rich smartphone data, Journal of Intelligent Transportation Systems, 19, 134, 10.1080/15472450.2013.764796

Chen, 2005, Robust and fast similarity search for moving object trajectories, 491

Chen, 2010, Searching trajectories by locations: An efficiency study, 255

Cheng, 2017, A two-step method for missing spatio-temporal data reconstruction, ISPRS International Journal of Geo-Information, 6, 187, 10.3390/ijgi6070187

Cortes, 1995, Support-vector networks, Machine Learning, 20, 273, 10.1007/BF00994018

Deng, 2016, A hybrid method for interpolating missing data in heterogeneous spatio-temporal datasets, ISPRS International Journal of Geo-Information, 5, 13, 10.3390/ijgi5020013

Deville, 2014, Dynamic population mapping using mobile phone data, Proceedings of the National Academy of Sciences, 111, 15888, 10.1073/pnas.1408439111

Fan, 2016, A collaborative filtering approach to citywide human mobility completion from sparse call records, 2500

Fiadino, 2012, Steps towards the extraction of vehicular mobility patterns from 3G signaling data, 66

Ficek, 2012, Inter-Call Mobility model: A spatio-temporal refinement of call data records using a Gaussian mixture model, 469

Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, 55, 119, 10.1006/jcss.1997.1504

Friedman, 2001, Greedy function approximation: A gradient boosting machine, Annals of Statistics, 29, 1189, 10.1214/aos/1013203451

Gao, 2015, Spatio-temporal analytics for exploring human mobility patterns and urban dynamics in the mobile age, Spatial Cognition and Computation, 15, 86, 10.1080/13875868.2014.984300

Gao, 2013, Discovering spatial interaction communities from mobile phone data, Transactions in GIS, 17, 463, 10.1111/tgis.12042

Gao, 2017, Uncovering the digital divide and the physical divide in Senegal using mobile phone data, 143

Giannotti, 2011, Unveiling the complexity of human mobility by querying and mining massive trajectory data, The VLDB Journal—The International Journal on Very Large Data Bases, 20, 695, 10.1007/s00778-011-0244-8

Gil-Garcia, 2006, A general framework for agglomerative hierarchical clustering algorithms, Vol. 2, 569

González, 2008, Understanding individual human mobility patterns, Nature, 453, 779, 10.1038/nature06958

Gurumurthy, 2018, Analyzing the dynamic ride-sharing potential for shared autonomous vehicle fleets using cellphone data from Orlando, Florida, Computers, Environment and Urban Systems, 71, 177, 10.1016/j.compenvurbsys.2018.05.008

Hägerstraand, 1970, What about people in regional science?, Papers in Regional Science, 24, 7, 10.1111/j.1435-5597.1970.tb01464.x

Han, 2011, Origin of the scaling law in human mobility: Hierarchy of traffic systems, Physical Review E, 83, 10.1103/PhysRevE.83.036117

Hoteit, 2013, Estimating real human trajectories through mobile phone data, 2, 148

Hoteit, 2014, Estimating human trajectories and hotspots through mobile phone data, Computer Networks, 64, 296, 10.1016/j.comnet.2014.02.011

Huang, 2014, From where do tweets originate? A GIS approach for user location inference, 1

Huang, 2010, Anchor points seeking of large urban crowd based on the mobile billing data, 346

Jaccard, 1912, The distribution of the flora in the alpine zone, New Phytologist, 11, 37, 10.1111/j.1469-8137.1912.tb05611.x

Jagadeesh, 2015, Probabilistic map matching of sparse and noisy smartphone location data, 812

Jagadeesh, 2017, Online map-matching of noisy and sparse location data with hidden Markov and route choice models, IEEE Transactions on Intelligent Transportation Systems, 18, 2423, 10.1109/TITS.2017.2647967

Kang, 2010, Analyzing and geo-visualizing individual human mobility patterns using mobile call records, 1

Kang, 2012, Intra-urban human mobility patterns: An urban morphology perspective, Physica A: Statistical Mechanics and its Applications, 391, 1702, 10.1016/j.physa.2011.11.005

Kung, 2014, Exploring universal patterns in human home-work commuting from mobile phone data, PLoS One, 9, 10.1371/journal.pone.0096180

Lee, 2007, Trajectory clustering: A partition-and-group framework, 593

Li, 2018, Predicting future locations of moving objects with deep fuzzy-LSTM networks, Transportmetrica A: Transport Science, 10.1080/23249935.2018.1552334

Lindsey, 2014, Pre-trip information and route-choice decisions with stochastic travel conditions: Theory, Transportation Research Part B: Methodological, 67, 187, 10.1016/j.trb.2014.05.006

Liu, 2019, Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling, Computers, Environment and Urban Systems, 74, 50, 10.1016/j.compenvurbsys.2018.12.001

Liu, 2015, Revealing travel patterns and city structure with taxi trip data, Journal of Transport Geography, 43, 78, 10.1016/j.jtrangeo.2015.01.016

Liu, 2018, Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records, Transactions in GIS, 22, 494, 10.1111/tgis.12323

Long, 2013, A review of quantitative methods for movement data, International Journal of Geographical Information Science, 27, 292, 10.1080/13658816.2012.682578

Long, 2015, Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing, Computers, Environment and Urban Systems, 53, 19, 10.1016/j.compenvurbsys.2015.02.005

Manley, 2015, A heuristic model of bounded route choice in urban areas, Transportation Research Part C: Emerging Technologies, 56, 195, 10.1016/j.trc.2015.03.020

Mao, 2017, An adaptive trajectory clustering method based on grid and density in mobile pattern analysis, Sensors, 17, 2013, 10.3390/s17092013

Martínez, 2009, A traffic analysis zone definition: A new methodology and algorithm, Transportation, 36, 581, 10.1007/s11116-009-9214-z

Miller, 1991, Modelling accessibility using space-time prism concepts within geographical information systems, International Journal of Geographical Information Systems, 5, 287, 10.1080/02693799108927856

Miller, 2005, A measurement theory for time geography, Geographical Analysis, 37, 17, 10.1111/j.1538-4632.2005.00575.x

Moreno, 2014, SMOT+: Extending the SMOT algorithm for discovering stops in nested sites, Computing and Informatics, 33, 327

Palma, 2008, A clustering-based approach for discovering interesting places in trajectories, 863

Pei, 2014, A new insight into land use classification based on aggregated mobile phone data, International Journal of Geographical Information Science, 28, 1988, 10.1080/13658816.2014.913794

Ranjan, 2012, Are call detail records biased for sampling human mobility?, ACM SIGMOBILE Mobile Computing and Communications Review, 16, 33, 10.1145/2412096.2412101

Shaw, 2008, A space-time GIS approach to exploring large individual-based spatiotemporal datasets, Transactions in GIS, 12, 425, 10.1111/j.1467-9671.2008.01114.x

Shin, 2015, Urban sensing: Using smartphones for transportation mode classification, Computers, Environment and Urban Systems, 53, 76, 10.1016/j.compenvurbsys.2014.07.011

Song, 2010, Limits of predictability in human mobility, Science, 327, 1018, 10.1126/science.1177170

Sui, 2018, Human dynamics in smart and connected communities, Computers, Environment and Urban Systems, 72, 1, 10.1016/j.compenvurbsys.2018.08.003

Toohey, 2015, Trajectory similarity measures, Sigspatial Special, 7, 43, 10.1145/2782759.2782767

Wan, 2018, Big data and urban system model - substitutes or complements? A case study of modelling commuting patterns in Beijing, Computers, Environment and Urban Systems, 68, 64, 10.1016/j.compenvurbsys.2017.10.004

Xiao, 2014, Lightweight map matching for indoor localisation using conditional random fields, 131

Xu, 2018, Human mobility and socioeconomic status: Analysis of Singapore and Boston, Computers, Environment and Urban Systems, 72, 51, 10.1016/j.compenvurbsys.2018.04.001

Xu, 2016, Estimating potential demand of bicycle trips from mobile phone data—An anchor-point based approach, ISPRS International Journal of Geo-Information, 5, 131, 10.3390/ijgi5080131

Xu, 2015, Understanding aggregate human mobility patterns using passive mobile phone location data: A home-based approach, Transportation, 42, 625, 10.1007/s11116-015-9597-y

Yan, 2011, Exact solution of the gyration radius of an individual's trajectory for a simplified human regular mobility model, Chinese Physics Letters, 28, 120506, 10.1088/0256-307X/28/12/120506

Yao, 2018, A stepwise spatio-temporal flow clustering method for discovering mobility trends, IEEE Access, 6, 44666, 10.1109/ACCESS.2018.2864662

Yu, 2018, Using cell phone location to assess misclassification errors in air pollution exposure estimation, Environmental Pollution, 233, 261, 10.1016/j.envpol.2017.10.077

Yuan, 2018, Toward space-time buffering for spatiotemporal proximity analysis of movement data, International Journal of Geographical Information Science, 32, 1211, 10.1080/13658816.2018.1432862

Yuan, 2015, Discovering urban functional zones using latent activity trajectories, IEEE Transactions on Knowledge and Data Engineering, 27, 712, 10.1109/TKDE.2014.2345405

Yuan, 2012, Correlating mobile phone usage and travel behavior – A case study of Harbin, China, Computers, Environment and Urban Systems, 36, 118, 10.1016/j.compenvurbsys.2011.07.003

Yue, 2014, Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies, Travel Behaviour and Society, 1, 69, 10.1016/j.tbs.2013.12.002

Zhao, 2016, Understanding the bias of call detail records in human mobility research, International Journal of Geographical Information Science, 30, 1738, 10.1080/13658816.2015.1137298

Zheng, 2015, Trajectory data mining: An overview, 6, 29