Clustering daily patterns of human activities in the city

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

Book Review: Pattern Recognition and Machine Learning

Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066

Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392

Pattern Classification

Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482

Motivation and Personality

(1997) · Naming the Mind: How Psychology Found its Language · doi:10.4135/9781446221815.n7 · Cited by 15,148

Principal Component Analysis

Jolliffe, I. T. (2005) · Wiley eBooks · doi:10.1002/0470013192.bsa501 · Cited by 14,522

Eigenfaces for Recognition

Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733

A Treatise on the Family

Becker, G. S. (1991) · doi:10.4159/9780674020665 · Cited by 10,565

A Theory of the Allocation of Time

Becker, G. S. (1965) · The Economic Journal · doi:10.2307/2228949 · Cited by 10,354

A collective human mobility analysis method based on data usage detail records

Jiang, H., Li, Q., Zhou, X., Chen, Y., Yi, S., Wang, H., & Lu, Z. (2017) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2017.1370715 · Cited by 17

Human Mobility and Predictability enriched by Social Phenomena InformationPreprint

Ponieman, N. B., Salles, A., & Sarraute, C. (2016) · Cited by 17

A novel dynamic quantification model for diurnal urban land use intensity

Zhou, H., Yuan, J., Yi, D., Jin, S., Zhao, Y., Zhang, Z., Zhao, Z., & Zhang, J. (2024) · Cities · doi:10.1016/j.cities.2024.104861 · Cited by 16

Mining multiple spatial–temporal paths from social media data

Yao, H. L., Xiong, M., Zeng, D., & Gong, J. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.08.003 · Cited by 16

Analyzing Charging Behavior of Electric City Buses in Typical Chinese Cities

Wei, W., Zhang, Z., Liu, P., Wang, Z., & Xue, L. (2019) · IEEE Access · doi:10.1109/access.2019.2963258 · Cited by 15

A critical review of activity participation decision: a key component of activity-based travel demand models

Nayak, S., & Pandit, D. (2022) · International Journal of Urban Sciences · doi:10.1080/12265934.2022.2154249 · Cited by 15

Automatic Extraction of Behavioral Patterns for Elderly Mobility and Daily Routine Analysis

Li, C., Cheung, W. K., Liu, J., & Ng, J. K. (2018) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3178116 · Cited by 15

Types and Characteristics Analysis of Human Dynamics in Seoul Using Location- Based Big Data

Jung, J., & Nam, J. (2019) · 국토계획 · doi:10.17208/jkpa.2019.06.54.3.75 · Cited by 15

Jobs-housing balance and travel patterns among different occupations as revealed by Hidden Markov mixture models: the case of Hong Kong

Zhang, F., Loo, B. P. Y., Lan, H., Chan, A. B., & Hsiao, J. H. (2023) · Transportation · doi:10.1007/s11116-023-10390-4 · Cited by 14

Understanding factors associated with individuals’ non-mandatory activities using machine learning and SHAP interpretation: A case study of Guangzhou, China

Zou, D., Li, Q., Zhou, Y. M., Shen, L., & Zhou, S. (2024) · Travel Behaviour and Society · doi:10.1016/j.tbs.2024.100894 · Cited by 14

Exploring the Relationship Between Visual Perception of the Urban Riverfront Core Landscape Area and the Vitality of Riverfront Road: A Case Study of Guangzhou

Zhang, S., Lu, J., Guo, R., & Yang, Y. (2024) · Land · doi:10.3390/land13122142 · Cited by 14

MobiDict

Kulkarni, V., Moro, A., & Garbinato, B. (2016) · International Workshop on GeoStreaming · doi:10.1145/3003421.3003424 · Cited by 14

Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data

Li, C., Hu, J., Dai, Z., Fan, Z., & Wu, Z. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9110666 · Cited by 14

Community time-activity trajectory modeling based on Markov chain simulation and Dirichlet regression

Xia, C., Hu, Y., & Chen, J. (2022) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2022.101933 · Cited by 14

Ensemble anomaly detection from multi-resolution trajectory features

Ando, S., Thanomphongphan, T., Seki, Y., & Suzuki, E. (2013) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-013-0334-x · Cited by 14

Space-adjusting technologies and the social ecologies of place: review and research agenda

Janelle, D. G. (2012) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2012.713958 · Cited by 14

Applying the Hidden Markov Model to Analyze Urban Mobility Patterns: An Interdisciplinary Approach

Loo, B. P. Y., Zhang, F., Hsiao, J. H., Chan, A. B., & Lan, H. (2021) · Chinese Geographical Science · doi:10.1007/s11769-021-1173-0 · Cited by 14

Measuring acceptance of tradable credit scheme and its effect on behavioral intention through theory of planned behavior

Cui, S., Tian, L., Xu, Y., & Wang, Y. (2024) · Transport Policy · doi:10.1016/j.tranpol.2024.03.009 · Cited by 13

Exploring Individual Activity-Travel Patterns Based on Geolocation Data from Mobile Phones

Yin, B., & Leurent, F. M. (2021) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981211031234 · Cited by 13

Using mobile phone data to explore spatial-temporal evolution of home-based daily mobility patterns in Shanghai

Liu, Z., Yu, J., Xiong, W., Lu, J. J., Yang, J., & Wang, Q. (2016) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc.2016.7804481 · Cited by 13

Truck industry classification from anonymous mobile sensor data using machine learning

Akter, T., & Hernandez, S. V. (2021) · International Journal of Transportation Science and Technology · doi:10.1016/j.ijtst.2021.07.001 · Cited by 12

Reconsidering Seasonality, Weather, and Road Safety in Non-temperate Areas: the Case of Kaohsiung, Taiwan

Hsu, C. S. (2023) · Travel Behaviour and Society · doi:10.1016/j.tbs.2023.100710 · Cited by 12

Towards Privacy-Preserving Semantic Mobility Analysis

Andrienko, N. V., Andrienko, G., & Fuchs, G. (2013) · City Research Online (City University London) · doi:10.2312/pe.eurovast.eurova13.019-023 · Cited by 12

Statistical patterns of human mobility in emerging Bicycle Sharing Systems

Chang, X., Shen, J., Lu, X., & Huang, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193795 · Cited by 12

A joint and simultaneous prediction framework of weekday and weekend daily-activity travel pattern using conditional dependency networks

Nayak, S., & Pandit, D. (2023) · Travel Behaviour and Society · doi:10.1016/j.tbs.2023.100595 · Cited by 12

Denoising autoencoders for fast real-time traffic estimation on urban road networks

Ghosh, S., Asif, M. T., & Wynter, L. (2017) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2017.8264610 · Cited by 12

Unveiling mobility complexity through complex network analysis

Guidotti, R., Monreale, A., Rinzivillo, S., Pedreschi, D., & Giannotti, F. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0369-2 · Cited by 12

Decoding Urban Mobility: Application of Natural Language Processing and Machine Learning to Activity Pattern Recognition, Prediction, and Temporal Transferability Examination

Chen, M., Yuan, Q., Yang, C., & Zhang, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3339772 · Cited by 11

Spatiotemporal analysis of the impact of electric vehicle integration on Reykjavik's electrical system at the city and distribution system level

Dillman, K. J., Fazeli, R., Shafiei, E., Jónsson, J. Ö. G., Haraldsson, H. V., & Davíðsdóttir, B. (2020) · Utilities Policy · doi:10.1016/j.jup.2020.101145 · Cited by 11

Supervised Land Use Inference from Mobility Patterns

Cáceres, N., & Benítez, F. G. (2018) · Journal of Advanced Transportation · doi:10.1155/2018/8710402 · Cited by 11

Modeling human activity dynamics: an object-class oriented space–time composite model based on social media and urban infrastructure data

Zhang, Z., Yin, D., Virrantaus, K., Ye, X., & Wang, S. (2021) · Computational Urban Science · doi:10.1007/s43762-021-00006-x · Cited by 11

Weighted Complex Network Analysis of the Different Patterns of Metro Traffic Flows on Weekday and Weekend

Feng, J., Li, X., Mao, B., Xu, Q., & Bai, Y. (2016) · Discrete Dynamics in Nature and Society · doi:10.1155/2016/9865230 · Cited by 11

Impacts of distinct travel behaviors on potential air pollution exposure measurement error

Lu, Y., & Habre, R. (2023) · Atmospheric Environment · doi:10.1016/j.atmosenv.2023.119820 · Cited by 10

How do children move and behave on streets? Vision-based movement behavior analysis using children's trajectories in urban surveillance systems

No, W., Choi, J., & Kim, Y. (2023) · Applied Geography · doi:10.1016/j.apgeog.2023.103170 · Cited by 11

GeoAvatar: A big mobile phone positioning data-driven method for individualized pseudo personal mobility data generation

Li, P., Zhang, H., Li, W., Huang, D., Guo, Z., Chen, J., Zhang, J., Song, X., Zhao, P., Yan, J., et al. (2025) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2025.102252 · Cited by 10

What are the multimodal patterns of individual mobility at the day level in the Paris region? A two-stage data-driven approach based on the 2018 Household Travel Survey

Yin, B., & Leurent, F. M. (2022) · Transportation · doi:10.1007/s11116-022-10285-w · Cited by 10

Sensing the disturbed rhythm of city mobility with chaotic measures: anomaly awareness from traffic flows

Gao, J., Zheng, D., & Yang, S. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01338-7 · Cited by 10

Measuring the Gap Between the Maximum Predictability and Prediction Accuracy of Human Mobility

Guo, J., Zhang, S., Zhu, J., & Ni, R. (2020) · IEEE Access · doi:10.1109/access.2020.3009868 · Cited by 10

Ups and Downs in Buzzes: Life Cycle Modeling for Temporal Pattern Discovery

Chang, Y., Yamada, M., Ortega, A., & Liu, Y. (2014) · Proceedings · doi:10.1109/icdm.2014.28 · Cited by 10

A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments

Santa, F., Henriques, R., Torres-Sospedra, J., & Pebesma, E. J. (2019) · Sustainability · doi:10.3390/su11030595 · Cited by 11

Where Chicagoans tweet the most

Soliman, A., Yin, J., Soltani, K., Padmanabhan, A., & Wang, S. (2015) · ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics (UrbanGIS) · doi:10.1145/2835022.2835032 · Cited by 10

New perspectives on spatial and temporal aspects of aircraft noise: Dynamic noise maps for Heathrow airport

Ganić, E., Rajé, F., & van Oosten, N. (2023) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2022.103527 · Cited by 9

Activity behavior of residents of Paraisópolis slum: Analysis of multiday activity patterns using data collected with smartphones

Pizzol, B., Strambi, O., Giannotti, M., Arbex, R., & Alves, B. B. (2021) · Journal of Choice Modelling · doi:10.1016/j.jocm.2021.100287 · Cited by 9

Mobility and sociocultural events in mobile phone data records

Ponieman, N. B., Sarraute, C., Minnoni, M., Travizano, M., Zivic, P. H. R., & Salles, A. (2015) · AI Communications · doi:10.3233/aic-150687 · Cited by 9

Lifecycle Modeling for Buzz Temporal Pattern Discovery

Chang, Y., Yamada, M., Ortega, A., & Liu, Y. (2016) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2994605 · Cited by 9

Impacts of negative congestion experiences on acceptance of tradable credits schemes: Integration of NAM and TPB

Cui, S., Tian, L., Bao, Y., & Zhang, Z. (2024) · Transport Policy · doi:10.1016/j.tranpol.2024.11.010 · Cited by 8

Spatiotemporal impacts of purpose-specific human mobility on air pollution: Evidence from taxi trajectories and interpretable machine learning

Wenrui, X. U., & Xinyue, G. U. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106411 · Cited by 8

Characterisation of Breathing and Physical Activity Patterns in the General Population Using the Wearable Respeck Monitor

Arvind, D. K., Fischer, D., Bates, C. A., & Kinra, S. (2019) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-34833-5_6 · Cited by 8

What Drives the Spatial Heterogeneity of Urban Leisure Activity Participation? A Multisource Big Data-Based Metrics in Nanjing, China

Liu, S., Chen, X., Zhang, F., Liu, Y., & Ge, J. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi12120499 · Cited by 8

Recovering Human Mobility Flow Models and Daily Routine Patterns in a Smart Environment

Chen, L., & Cheung, W. K. (2014) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2014.155 · Cited by 8

Characterizing Urban Mobility Patterns: A Case Study of Mexico City

Melikov, P., Kho, J. A., Fighiera, V., Alhasoun, F., Audiffred, J., Mateos, J. L., & González, M. C. (2021) · The urban book series · doi:10.1007/978-981-15-8983-6_11 · Cited by 8

Body Area Networks: Smart IoT and Big Data for Intelligent Health Management

Arvind, D. K., Fischer, D., Bates, A., & Kinra, S. (2019) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-34833-5 · Cited by 8

The geography of connectivity: a review of mobile positioning data for economic geography

Erlström, A., Grillitsch, M., & Hall, O. (2022) · Journal of Geographical Systems · doi:10.1007/s10109-022-00388-4 · Cited by 10

Human mobility and predictability enriched by social phenomena information

Ponieman, N. B., Salles, A., & Sarraute, C. (2013) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2492517.2500236 · Cited by 8

A Rule-based Method for Discovering Trajectory Profiles

de Alencar, L. A., Álvares, L. O., Renso, C., Raffaetà, A., & Bogorny, V. (2015) · Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering · doi:10.18293/seke2015-143 · Cited by 8

A Deep Learning Approach for Intelligent Cockpits: Learning Drivers Routines

Fernandes, C., Ferreira, F., Erlhagen, W., Monteiro, S., & Bicho, E. G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62365-4_17 · Cited by 8

Empirical Characterization of Mobility of Multi-Device Internet UsersPreprint

Trivedi, A., Gummeson, J., & Shenoy, P. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08512 · Cited by 8

Adaptive weighted multi-view subspace clustering method for recognizing urban functions from multi-source social sensing data

Liu, Q., Lu, Z., Huan, W., & Fan, C. (2024) · Geo-spatial Information Science · doi:10.1080/10095020.2024.2356243 · Cited by 9

Inferring Location Types With Geo-Social-Temporal Pattern Mining

Anwar, T., Liao, K., Goyal, A., Sellis, T., Kayes, A. S. M., & Shen, H. (2020) · IEEE Access · doi:10.1109/access.2020.3018997 · Cited by 7

Analysis of Machine Learning Approaches’ Performance in Prediction Problems with Human Activity Patterns

Torres-López, R., Casillas-Pérez, D., Pérez‐Aracil, J., Cornejo-Bueno, L. M., Alexandre, E., & Salcedo‐Sanz, S. (2022) · Mathematics · doi:10.3390/math10132187 · Cited by 7

Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys

Adenaw, L., & Bachmeier, Q. (2022) · Applied Sciences · doi:10.3390/app12178456 · Cited by 7

A Framework for Modeling Occupancy Schedules and Local Trips Based on Activity Based Surveys

Rakha, T., Rose, C., & Reinhart, C. F. (2014) · ASHRAE/IBPSA-USA Building Simulation Conference · doi:10.26868/25746308.2014.55 · Cited by 7

Learning functional compositions of urban spaces with crowd-augmented travel survey data

Zhu, Z. Z., Yang, J., Zhong, C., Seiter, J., & Tröster, G. (2015) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2820783.2820832 · Cited by 7

Dimensionality Reduction to Reveal Urban Truck Driver Activity Patterns

Lu, F., Zhao, F., & Cheah, L. W. (2018) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198118787105 · Cited by 7

Activity recognition for a smartphone and web based travel surveyPreprint

Kim, Y., Pereira, F. C., Zhao, F., Ghorpade, A., Zegras, P. C., & Ben-Akiva, M. E. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.03634 · Cited by 7

A severe local flood and social events show a similar impact on human mobility

Loreti, S., Keiler, M., & Zischg, A. P. (2025) · npj Complexity · doi:10.1038/s44260-025-00030-6 · Cited by 6

Electric vehicle trips detection and synthesis using Sequential Generative Adversarial Networks

Chen, X., Hamim, O. F., & Ukkusuri, S. V. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127410 · Cited by 6

Mobility Genome™- A Framework for Mobility Intelligence from Large-Scale Spatio-Temporal Data

Dang, T. A., Deepak, J., Wang, J., Luo, S., Jin, Y., Ng, Y., Lim, A., & Li, Y. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.20 · Cited by 6

A tale of three cities: uncovering human-urban interactions with geographic-context aware social media data

Yin, J., & Chi, G. (2022) · Urban Informatics · doi:10.1007/s44212-022-00020-2 · Cited by 6

A clustering-based approach to quantifying socio-demographic impacts on urban mobility patterns

Yang, Y., Samaranayake, S., & Dogan, T. (2023) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083231159909 · Cited by 6

Investigating semantic regularity of human mobility lifestyle

de Lira, V. M., Rinzivillo, S., Renso, C., Times, V. C., & Tedesco, P. C. D. A. R. (2014) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1145/2628194.2628226 · Cited by 6

User-Based Context Modeling for Music Recommender Systems

Sassi, I. B., Yahia, S. B., & Mellouli, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-60438-1_16 · Cited by 6

Adaptive mobile behavior change intervention using reinforcement learning

Cai, L., Wu, C., Meimandi, K. J., & Gerber, M. S. (2017) · doi:10.1109/companion.2017.8287078 · Cited by 6

Mining SQL workloads for learning analysis behavior

Moreau, C., Legroux, C., Peralta, V., & Hamrouni, M. A. (2022) · Information Systems · doi:10.1016/j.is.2022.102004 · Cited by 6

Spatiotemporal Identification of Potential Tsunami Vertical Evacuation Sites: A Case Study of Shizuoka City, Japan

Voulgaris, G., & Aleksejeva, J. (2017) · PLoS Currents · doi:10.1371/currents.dis.e66442ce2b19de55532457d967d9645d · Cited by 6

Daily activity-travel pattern identification using natural language processing and semantic matching

Nayak, S., & Pandit, D. (2024) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2024.104057 · Cited by 5

Elderly Mobility and Daily Routine Analysis Based on Behavior-Aware Flow Graph Modeling

Li, C., Cheung, W. K., & Liu, J. (2015) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2015.60 · Cited by 5

Towards an Affective Semantic Trajectory Generator (ASTG)

Karatzoglou, A., Szarvas, M., & Beigl, M. (2018) · IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) · doi:10.1109/wimob.2018.8589185 · Cited by 5

Exploring lifestyle patterns from GPS trajectory data: embedding spatio-temporal context information via geohash and POI

Lee, H., Kang, Y., Noh, S. M., Kim, J., & Lee, J. (2024) · Spatial Information Research · doi:10.1007/s41324-024-00597-7 · Cited by 5

Quantifying the vibrancy of streets: Large-scale pedestrian density estimation with dashcam data

Oda, T., & Yoshimura, Y. (2024) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2024.104840 · Cited by 5

GeoSmart cities: Event-driven geoprocessing as enabler of smart cities

Morales, J., & García, M. G. R. (2015) · IEEE International Smart Cities Conference (ISC2) · doi:10.1109/isc2.2015.7366207 · Cited by 5

A Comparative Study of Urban Mobility Patterns Using Large-Scale Spatio-Temporal Data

Dang, T. A., Chiam, J., & Li, Y. (2018) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2018.00089 · Cited by 5

Towards Endowing Intelligent Cars with the Ability to Learn the Routines of Multiple Drivers: A Dynamic Neural Field Model

Wojtak, W., Ferreira, F., Guimarães, P., Barbosa, P., Monteiro, S., Erlhagen, W., & Bicho, E. G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86973-1_24 · Cited by 5

How The Urban Microclimate And Outdoor Thermal Comfort Can Affect Intra-City Mobility Patterns: Evidence From New York City

Yang, Y., Wang, D., & Dogan, T. (2022) · Annual Modeling and Simulation Conference (ANNSIM) · doi:10.23919/annsim55834.2022.9859328 · Cited by 5

The Deep Structure of Lives

Kubovy, M. (2015) · Philosophia Scientae · doi:10.4000/philosophiascientiae.1139 · Cited by 5

Modeling and elucidation of housing price

Tan, F., Cheng, C., & Wei, Z. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-018-00612-0 · Cited by 5

Urban Functional Area Division Based on Cell Tower Classification

Wu, Y., Qiao, Y., & Yang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2955755 · Cited by 5

Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities

Qarout, Y., Raykov, Y. P., & Little, M. A. (2020) · Sensors · doi:10.3390/s20030784 · Cited by 5

Exploring regularity and structure in travel behavior using Smart Card data

Goulet-Langlois, G. (2015) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 5

HoWDe: A validated algorithm for Home and Work location Detection

de Sojo, S., Lucchini, L., Langle-Chimal, O., Fraiberger, S. P., & Alessandretti, L. (2026) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2025.102397 · Cited by 4

Identification of daily environmental noise patterns in two different urban sites in Santiago, Chile

Alburquenque, M. F., Ríos, A. M., Lambarri, M. C., Arellano, R. T., Cáceres, D., & Orellana, S. A. (2020) · Revista médica de Chile · doi:10.4067/s0034-98872020000500582 · Cited by 4

Social demographics imputation based on similarity in multi-dimensional activity-travel pattern: A two-step approach

Zhang, B., Rasouli, S., & Feng, T. (2024) · Travel Behaviour and Society · doi:10.1016/j.tbs.2024.100843 · Cited by 4

Discovering dynamic patterns of urban space via semi-nonnegative matrix factorization

Liu, Z., Jun, C., Yang, J., & Wang, Q. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258332 · Cited by 4

Analysis of the spatial structure of Beijing from the point view of Weibo Data

Yang, L., & Duarte, C. R. M. (2020) · ACE Arquitectura Ciudad y Entorno · doi:10.5821/ace.15.43.9302 · Cited by 4

Clustering sequences of multi-dimensional sets of semantic elements

Moreau, C., Chanson, A., Peralta, V., Devogèle, T., & de Runz, C. (2021) · ACM Symposium on Applied Computing · doi:10.1145/3412841.3441920 · Cited by 4

The digital society as a determining factor in mobility, urban dynamics and current cities structure

Luna, C., & Victor, M. L. (2021) · Transportation research procedia · doi:10.1016/j.trpro.2021.11.057 · Cited by 4

Discovering Latent Psychological Structures from Self-Report Assessments of Hospital Workers

Kao, H.-T., Hosseinmardi, H., Yan, S., Hasan, M., Narayanan, S. S., Lerman, K., & Ferrara, E. (2018) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc.2018.8697325 · Cited by 4

Mobility pattern recognition method: Segmentation and geographic projection

Allahviranloo, M., & Priol, E. L. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569283 · Cited by 4

Modelling individual routines and spatio-temporal trajectories in human mobility.Preprint

Pappalardo, L., & Simini, F. (2016) · arXiv (Cornell University) · Cited by 4

Where Chicagoans tweet the most: Semantic analysis of preferential return locations of Twitter usersPreprint

Soliman, A., Yin, J., Soltani, K., Padmanabhan, A., & Wang, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.06880 · Cited by 4

Discovering urban spatial-temporal structure from human activity patterns

Jiang, S., Ferreira, J., & González, M. C. (2012) · ACM SIGKDD International Workshop on Urban Computing (UrbComp) · doi:10.1145/2346496.2346512 · Cited by 99

Workers, adventurers, explorers: uncovering activity patterns in Melbourne

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