This paper is in the following e-collection/theme issue:

Public Health Data Visualization and Pattern Recognition (67)

Published on in Vol 12, No 1 (2020):

Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations

Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations

Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations

Authors of this article:

Dr. Albert M. Lund ;   Dr. Ramkiran Gouripeddi ;   Dr. Julio C. Facelli1
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Journals

  1. Vosoughkhosravi S, Jafari A, Zhu Y. Application of American time use survey (ATUS) in modelling energy-related occupant-building interactions: A comprehensive review. Energy and Buildings 2023;294:113245 View
  2. Hubal R, Cohen Hubal E. Simulating patterns of life: More representative time-activity patterns that account for context. Environment International 2023;172:107753 View
  3. Du H, Yuan Z, Wu Y, Yu K, Sun X. An LBS and agent-based simulator for Covid-19 research. Scientific Reports 2022;12(1) View
  4. Lund A, Gouripeddi R, Facelli J. STHAM: an agent based model for simulating human exposure across high resolution spatiotemporal domains. Journal of Exposure Science & Environmental Epidemiology 2020;30(3):459 View
  5. Ahmed M, Fatmi M. Exploring the complexity of daily activity schedules using spatial statistics and machine learning methods. Transportation Letters 2025;17(9):1549 View
  6. Vosoughkhosravi S, Jafari A. Advanced modeling of American household occupancy profiles through data-driven approaches. Journal of Building Engineering 2025;116:114642 View

Books/Policy Documents

  1. Thakur D, Biswas S, Pal A. Internet of Things Based Smart Healthcare. View

Conference Proceedings

  1. Vosoughkhosravi S, Jafari A. Computing in Civil Engineering 2023. American Time Use Survey in Modeling Occupant Behavior: A Systematic Review View
  2. Vosoughkhosravi S, Jafari A. Computing in Civil Engineering 2024. Advancing Occupancy Prediction Using Machine Learning Techniques: Insights from the American Time Use Survey View