IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v43y2016i5p920-940.html
   My bibliography  Save this article

Inferring individual daily activities from mobile phone traces: A Boston example

Author

Listed:
  • Mi Diao
  • Yi Zhu
  • Joseph Ferreira Jr
  • Carlo Ratti

Abstract

Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer individuals’ activities from their mobile phone traces. Using Metro Boston as an example, we develop an activity detection model with travel diary surveys to reveal the common laws governing individuals’ activity participation, and apply the modeling results to mobile phone traces to extract the embedded activity information. The proposed approach enables us to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new “big data†such as mobile phone traces and traditional travel surveys to improve transportation planning and urban planning and management.

Suggested Citation

  • Mi Diao & Yi Zhu & Joseph Ferreira Jr & Carlo Ratti, 2016. "Inferring individual daily activities from mobile phone traces: A Boston example," Environment and Planning B, , vol. 43(5), pages 920-940, September.
    • Handle: RePEc:sae:envirb:v:43:y:2016:i:5:p:920-940
    DOI: 10.1177/0265813515600896
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0265813515600896
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0265813515600896?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Bowman, J. L. & Ben-Akiva, M. E., 2001. "Activity-based disaggregate travel demand model system with activity schedules," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 1-28, January.
    2. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xin Li & Bingruo Duan, 2018. "Organizational microblogging for event marketing: a new approach to creative placemaking," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 22(1), pages 59-79, January.
    2. Griffin, Greg Phillip & Mulhall, Megan & Simek, Chris & Riggs, William W., 2020. "Mitigating Bias in Big Data for Transportation," SocArXiv trbv9, Center for Open Science.
    3. Yaxi Gong & Xiang Ji & Yuan Zhang & Shanshan Cheng, 2023. "Spatial Vitality Evaluation and Coupling Regulation Mechanism of a Complex Ecosystem in Lixiahe Plain Based on Multi-Source Data," Sustainability, MDPI, vol. 15(3), pages 1-32, January.
    4. Liu, Lun & Gao, Xuesong & Zhuang, Jiexin & Wu, Wen & Yang, Bo & Cheng, Wei & Xiao, Pengfei & Yao, Xingzhu & Deng, Ouping, 2020. "Evaluating the lifestyle impact of China’s rural housing land consolidation with locational big data: A study of Chengdu," Land Use Policy, Elsevier, vol. 96(C).
    5. Yi Zhu, 2020. "Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore," Transportation, Springer, vol. 47(6), pages 2703-2730, December.
    6. Zhang, Shanqi & Yang, Yu & Zhen, Feng & Lobsang, Tashi & Li, Zhixuan, 2021. "Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach," Journal of Transport Geography, Elsevier, vol. 90(C).
    7. Tamás Kovalcsik & Ábel Elekes & Lajos Boros & László Könnyid & Zoltán Kovács, 2022. "Capturing Unobserved Tourists: Challenges and Opportunities of Processing Mobile Positioning Data in Tourism Research," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    8. Yadi Zhu & Feng Chen & Ming Li & Zijia Wang, 2018. "Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
    9. Andersson, Angelica & Engelson, Leonid & Börjesson, Maria & Daly, Andrew & Kristoffersson, Ida, 2022. "Long-distance mode choice model estimation using mobile phone network data," Journal of choice modelling, Elsevier, vol. 42(C).
    10. Deng, Yiling & Zhao, Pengjun, 2022. "The impact of new metro on travel behavior: Panel analysis using mobile phone data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 46-57.
    11. Yating Fan & Da Kuang & Wei Tu & Yu Ye, 2023. "Which Spatial Elements Influence Waterfront Space Vitality the Most?—A Comparative Tracking Study of the Maozhou River Renewal Project in Shenzhen, China," Land, MDPI, vol. 12(6), pages 1-18, June.
    12. Yuting Chen & Bingyao Jia & Jing Wu & Xuejun Liu & Tianyue Luo, 2022. "Temporal and Spatial Attractiveness Characteristics of Wuhan Urban Riverside from the Perspective of Traveling," Land, MDPI, vol. 11(9), pages 1-21, August.
    13. Richard Harris & David O’Sullivan & Mark Gahegan & Martin Charlton & Lex Comber & Paul Longley & Chris Brunsdon & Nick Malleson & Alison Heppenstall & Alex Singleton & Daniel Arribas-Bel & Andy Evan, 2017. "More bark than bytes? Reflections on 21+ years of geocomputation," Environment and Planning B, , vol. 44(4), pages 598-617, July.
    14. Yi Zhu, 2022. "Inference of activity patterns from urban sensing data using conditional random fields," Environment and Planning B, , vol. 49(2), pages 549-565, February.
    15. Fangye Du & Jiaoe Wang & Liang Mao & Jian Kang, 2024. "Daily rhythm of urban space usage: insights from the nexus of urban functions and human mobility," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    16. Li, Shaoying & Zhuang, Caigang & Tan, Zhangzhi & Gao, Feng & Lai, Zhipeng & Wu, Zhifeng, 2021. "Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China," Journal of Transport Geography, Elsevier, vol. 91(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lijun Sun & Xinyu Chen & Zhaocheng He & Luis F. Miranda-Moreno, 2023. "Routine Pattern Discovery and Anomaly Detection in Individual Travel Behavior," Networks and Spatial Economics, Springer, vol. 23(2), pages 407-428, June.
    2. Jeong-Hui Park & Eunhye Yoo & Youngdeok Kim & Jung-Min Lee, 2021. "What Happened Pre- and during COVID-19 in South Korea? Comparing Physical Activity, Sleep Time, and Body Weight Status," IJERPH, MDPI, vol. 18(11), pages 1-13, May.
    3. Matteo Böhm & Mirco Nanni & Luca Pappalardo, 2022. "Gross polluters and vehicle emissions reduction," Nature Sustainability, Nature, vol. 5(8), pages 699-707, August.
    4. Su, Rongxiang & Xiao, Jingyi & McBride, Elizabeth C. & Goulias, Konstadinos G., 2021. "Understanding senior's daily mobility patterns in California using human mobility motifs," Journal of Transport Geography, Elsevier, vol. 94(C).
    5. Robert Stewart & Marie Urban & Samantha Duchscherer & Jason Kaufman & April Morton & Gautam Thakur & Jesse Piburn & Jessica Moehl, 2016. "A Bayesian machine learning model for estimating building occupancy from open source data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1929-1956, April.
    6. Arroyo Arroyo,Fatima & Fernandez Gonzalez,Marta & Matekenya,Dunstan & Espinet Alegre,Xavier, 2021. "Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone," Policy Research Working Paper Series 9519, The World Bank.
    7. Naqavi, Fatemeh & Sundberg, Marcus & Västberg, Oskar Blom & Karlström, Anders & Hugosson, Muriel Beser, 2023. "Mobility constraints and accessibility to work: Application to Stockholm," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    8. David Kofoed Wind & Piotr Sapiezynski & Magdalena Anna Furman & Sune Lehmann, 2016. "Inferring Stop-Locations from WiFi," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-15, February.
    9. Zhou, Xingang & Yeh, Anthony G.O. & Yue, Yang, 2018. "Spatial variation of self-containment and jobs-housing balance in Shenzhen using cellphone big data," Journal of Transport Geography, Elsevier, vol. 68(C), pages 102-108.
    10. Maxime Lenormand & Miguel Picornell & Oliva G Cantú-Ros & Antònia Tugores & Thomas Louail & Ricardo Herranz & Marc Barthelemy & Enrique Frías-Martínez & José J Ramasco, 2014. "Cross-Checking Different Sources of Mobility Information," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
    11. Miotti, Marco & Needell, Zachary A. & Jain, Rishee K., 2023. "The impact of urban form on daily mobility demand and energy use: Evidence from the United States," Applied Energy, Elsevier, vol. 339(C).
    12. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    13. Huang, Feihu & Qiao, Shaojie & Peng, Jian & Guo, Bing & Xiong, Xi & Han, Nan, 2019. "A movement model for air passengers based on trip purpose," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 798-808.
    14. Duan, Zhengyu & Zhao, Haoran & Li, Zhenming, 2023. "Non-linear effects of built environment and socio-demographics on activity space," Journal of Transport Geography, Elsevier, vol. 111(C).
    15. Vanky, Anthony & Courtney, Theodore & Verma, Santosh & Ratti, Carlo, 2016. "One to Many: Opportunities to Understanding Collective Behaviors in Urban Environments Through Individual's Passively-Collected Locative Data," SocArXiv f7mpd, Center for Open Science.
    16. Shanshan Wan & Zhuo Chen & Cheng Lyu & Ruofan Li & Yuntao Yue & Ying Liu, 2022. "Research on disaster information dissemination based on social sensor networks," International Journal of Distributed Sensor Networks, , vol. 18(3), pages 15501329221, March.
    17. Elisa Frutos-Bernal & Ángel Martín del Rey & Irene Mariñas-Collado & María Teresa Santos-Martín, 2022. "An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    18. Zhai, Wei & Bai, Xueyin & Peng, Zhong-ren & Gu, Chaolin, 2019. "From edit distance to augmented space-time-weighted edit distance: Detecting and clustering patterns of human activities in Puget Sound region," Journal of Transport Geography, Elsevier, vol. 78(C), pages 41-55.
    19. Johannes Stübinger & Lucas Schneider, 2020. "Understanding Smart City—A Data-Driven Literature Review," Sustainability, MDPI, vol. 12(20), pages 1-23, October.
    20. Mohammad Hesam Hafezi & Lei Liu & Hugh Millward, 2019. "A time-use activity-pattern recognition model for activity-based travel demand modeling," Transportation, Springer, vol. 46(4), pages 1369-1394, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envirb:v:43:y:2016:i:5:p:920-940. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.