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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
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    References listed on IDEAS

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    1. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    2. 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.
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    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. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    12. 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).
    13. 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.
    14. 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.
    15. 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).
    16. 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.

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