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Probabilistic Mining of Socio-Geographic Routines From Mobile Phone Data

Katayoun Farrahi, Daniel Gatica-Perez
2010 IEEE Journal on Selected Topics in Signal Processing  
We use an unsupervised approach, based on probabilistic topic models, to discover latent human activities in terms of the joint interaction and location behaviors of 97 individuals over the course of approximately  ...  a 10-month period using data from MIT's Reality Mining project.  ...  Eagle (Santa Fe Institute) and A. (Sandy) Pentland (MIT) for sharing the data.  ... 
doi:10.1109/jstsp.2010.2049513 fatcat:4wiy5i2y55bjljr74h63emltgi

DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation [article]

Han Wang, Zhou Huang, Xiao Zhou, Ganmin Yin, Yi Bao
2022 arXiv   pre-print
We evaluate representations generated by our method and other baseline models on classification and clustering tasks.  ...  Driving trajectory representation learning is of great significance for various location-based services, such as driving pattern mining and route recommendation.  ...  This research was supported by grants from the National Key Research and Development Program of China (2017YFE0196100), and the National Natural Science Foundation of China (41771425, 41830645, 41625003  ... 
arXiv:2205.08356v1 fatcat:66qsragxmzbyzotchfrybtred4

Data Mining and Knowledge Discovery [chapter]

Chao Zhang, Jiawei Han
2021 The Urban Book Series  
In this chapter, we present recent developments in data-mining techniques for urban activity modeling, a fundamental task for extracting useful urban knowledge from social-sensing data.  ...  We first describe traditional approaches to urban activity modeling, including pattern discovery methods and statistical models.  ...  Data Mining for Urban Analysis Generally, data-mining techniques for urban analysis tasks can be categorized into four classes: (1) urban pattern discovery; (2) urban activity modeling; (3) urban mobility  ... 
doi:10.1007/978-981-15-8983-6_42 fatcat:gxnx3jgu4fcqvbieg3ltmdovk4

Trace analysis and mining for smart cities: issues, methods, and applications

Gang Pan, Guande Qi, Wangsheng Zhang, Shijian Li, Zhaohui Wu, Laurence Yang
2013 IEEE Communications Magazine  
In this article, we first give a brief introduction to trace data; then we present six research issues in trace analysis and mining, and survey the state-of-the-art methods; finally, five promising application  ...  Traces of moving objects in a city, which depict lots of semantics concerning human mobility and city dynamics, are becoming increasingly important.  ...  ACKNOWLEDGMENTS This work is partly supported by the National Key Basic Research Program of China (2013CB329504) and Qianjiang Talent Program of Zhejiang (2011R10078). Dr. Z.  ... 
doi:10.1109/mcom.2013.6525604 fatcat:57ge66oshbebdc5azxvbyu5hru

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Khatun E Zannat, Charisma F. Choudhury
2019 Journal of the Indian Institute of Science  
Chen et al. 17 reviewed the current methodologies of using mobile phone data for travel behavior analysis in three sub-areas: modeling travel behavior, behavioral factor, and human mobility pattern.  ...  using smart card data Smart card data and GPS trajectories The effects of demographic, land use and transportation factors on the ridership of PT can be explored using GWR analysis using smart card data  ... 
doi:10.1007/s41745-019-00125-9 fatcat:5qm66wzthzejdg67ed6uumbh2a

Friends Wall: A Semantic-based Friend Recommendation System for Social Networks

Mr. Raj Agarwal, Mr. Swaranjeet Singh, Ms. Samiksha Bhujbal, Ms. Pooja Jadhav, Prof. Wanaskar U.H.
2017 IJARCCE  
This project will build FriendsWall and evaluate its performance on both small-scale experiments and large-scale model.  ...  Inspired by text mining, in this project we design a users daily life as life documents, from which his/her life styles are taken out by using the Naive Byes algorithm.  ...  Wanaskar, for her instinct help and valuable guidance with a lot of encouragement throughout this paper work, right from selection of topic work up to its completion.  ... 
doi:10.17148/ijarcce.2017.6598 fatcat:l2p4wjif4naclcpufhmzyrmthu


S. Kamel Basmenj, S. Li
2022 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In response, this paper provides a review on urban human mobility, including the data, models, and applications used in the selected articles.  ...  There are several published review papers examing these studies on urban human mobility, with the focus on defining models and applications.  ...  used topic modeling-LDA to develop a new model. This model quantitatively extracted human activity patterns based on specific spatiotemporal and semantic characteristics.  ... 
doi:10.5194/isprs-archives-xliii-b4-2022-247-2022 fatcat:a2ajzdcxefhqxd5zqcahybzcym

A probabilistic approach to socio-geographic reality mining

Katayoun Farrahi
2011 ACM SIGMultimedia Records  
Big thank you to Iacopo and Corinne who helped us in many ways and showed us some special places in Switzerland.  ...  Special thanks to Kevin and Letizia for their friendship and for hours of board game fun, especially during 'on-call' days in Bern. Thanks to Daniel and Jennifer for their friendship.  ...  Mining patterns of human behavior from large-scale mobile phone data has deep potential impact on society.  ... 
doi:10.1145/2069203.2069206 fatcat:b4veqpnsajdrbhm4xvugd3qop4

Behavior Life Style Analysis for Mobile Sensory Data in Cloud Computing through MapReduce

Shujaat Hussain, Jae Bang, Manhyung Han, Muhammad Ahmed, Muhammad Amin, Sungyoung Lee, Chris Nugent, Sally McClean, Bryan Scotney, Gerard Parr
2014 Sensors  
In this paper a big data solution is proposed to analyze the sensory data and give insights into user behavior and lifestyle trends.  ...  Big data technology Hadoop MapReduce is employed to analyze the data and create user timeline of user's activities. These activities are visualized to find useful health analytics and trends.  ...  Shujaat Hussain and Sungyoung Lee designed and implemented the big data part as well as the energy moniroting part.  ... 
doi:10.3390/s141122001 pmid:25420151 pmcid:PMC4279574 fatcat:ql33cu43uraw5f5bkzsti32et4

CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network [article]

Fereshteh Asgari and Alexis Sultan and Haoyi Xiong and Vincent Gauthier and Mounim El-Yacoubi
2016 arXiv   pre-print
Mobile phone data have recently become an attractive source of information about mobility behavior.  ...  The HMM is unsupervised as the transition and emission probabilities are inferred using respectively the physical transportation properties and the information on the spatial coverage of antenna base stations  ...  We would also thanks Marco Fiore for his helpful discussion on this topic.  ... 
arXiv:1604.06577v1 fatcat:6epd2pihlzadpefo72kbbvimui

CT-Mapper: Mapping sparse multimodal cellular trajectories using a multilayer transportation network

Fereshteh Asgari, Alexis Sultan, Haoyi Xiong, Vincent Gauthier, Mounîm A. El-Yacoubi
2016 Computer Communications  
Mobile phone data have recently become an attractive source of information about mobility behavior.  ...  El-Yacoubi). ies used GPS to accurately sense spatial data with a localization error bound ≤50 m.  ...  We would also thanks Marco Fiore for his helpful discussion on this topic.  ... 
doi:10.1016/j.comcom.2016.04.014 fatcat:ow3dtq2ud5dovix4ntwpf37xy4

Learning and predicting multimodal daily life patterns from cell phones

Katayoun Farrahi, Daniel Gatica-Perez
2009 Proceedings of the 2009 international conference on Multimodal interfaces - ICMI-MLMI '09  
In this paper, we investigate the multimodal nature of cell phone data in terms of discovering recurrent and rich patterns in people's lives.  ...  We present a method that can discover routines from multiple modalities (location and proximity) jointly modeled, and that uses these informative routines to predict unlabeled or missing data.  ...  We thank Nathan Eagle (MIT) for sharing the data.  ... 
doi:10.1145/1647314.1647373 dblp:conf/icmi/FarrahiG09 fatcat:k4w3rtcezjhhxn3upie6mxti6u

Data-driven Computational Social Science: A Survey

Jun Zhang, Wei Wang, Feng Xia, Yu-Ru Lin, Hanghang Tong
2020 Big Data Research  
With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye.  ...  Social science concerns issues on individuals, relationships, and the whole society.  ...  Therefore, behavior analysis is another topic related to research on human beings.  ... 
doi:10.1016/j.bdr.2020.100145 fatcat:jazh5b3itfgmvh4pn37l4v5m7y

Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability [chapter]

Atousa Tajaddini, Geoffrey Rose, Kara M. Kockelman, Hai L. Vu
2020 Transportation Systems for Smart, Sustainable, Inclusive and Secure Cities [Working Title]  
The big data enables new ABM models to reflect mobility behavior on an unprecedented level of detail while collecting data over a longer period (e.g., more than one typical day) would improve the behavioral  ...  To this end, the first part of this paper will review the new real-time data resources revealing the pattern and traces of traveler's mobility at a large scale and over an extended period of time.  ...  Section 2 introduces new data sources such as mobile phone call data records, transit smart cards, and GPS data where the influence of new data sources on the planning of activities, formation, and analysis  ... 
doi:10.5772/intechopen.93827 fatcat:7a7ygjcwgbh5he53dyd7gtelfu

Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A Survey

Bin Guo, Yan Liu, Yi Ouyang, Vincent W. Zheng, Daqing Zhang, Zhiwen Yu
2019 IEEE Access  
Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product  ...  Crowdsourced business intelligence (CrowdBI), which leverages the crowdsourced usergenerated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment  ...  They use frequent pattern mining and group behavior analysis to detect group spamming. Wang et al.  ... 
doi:10.1109/access.2019.2901027 fatcat:a5vz6vl7urckpdsreplkvjalea
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