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Learning daily activity patterns with probabilistic grammars

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  • Siyu Li

    (National University of Singapore)

  • Der-Horng Lee

    (National University of Singapore)

Abstract

Daily activity pattern is the reflection and abstraction of actual individual activity participation on daily basis. It carries information on activity type, frequency and sequence. Preference of daily activity patterns varies among population, and thus can be interpreted as personal life styles. This paper advances studies on human daily activity patterns by providing new perspective and methodology in the modeling and learning of daily activity patterns using probabilistic context-free grammars. In this paper, similarities between daily activity pattern—which is defined as activity sequence—and language are explored. We developed context-free grammars to parse and generate daily activity patterns. To replicate people’s heterogeneity in selecting daily activity patterns, we introduced probabilistic context-free grammars and proposed several formulations to estimate the probability of a context-free grammar with daily activity patterns observed in household travel survey. We conducted experiments on the proposed formulations, finding that under proper context-free grammar and problem formulation, the estimated probabilistic context-free grammar is able to reproduce the observed pattern distribution in household travel survey with satisfactory precision. Practically, the proposed methodology sheds light on the issue of generating stochastic and accessibility-dependent choice sets for daily activity pattern models in certain activity-based modeling frameworks.

Suggested Citation

  • Siyu Li & Der-Horng Lee, 2017. "Learning daily activity patterns with probabilistic grammars," Transportation, Springer, vol. 44(1), pages 49-68, January.
    • Handle: RePEc:kap:transp:v:44:y:2017:i:1:d:10.1007_s11116-015-9622-1
    DOI: 10.1007/s11116-015-9622-1
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    References listed on IDEAS

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    Cited by:

    1. Usman Ahmed & Ana Tsui Moreno & Rolf Moeckel, 2021. "Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes," Transportation, Springer, vol. 48(3), pages 1481-1502, June.
    2. 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.
    3. Usman Ahmed & Ana Tsui Moreno & Rolf Moeckel, 0. "Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes," Transportation, Springer, vol. 0, pages 1-22.
    4. Wim Ectors & Bruno Kochan & Davy Janssens & Tom Bellemans & Geert Wets, 2019. "Exploratory analysis of Zipf’s universal power law in activity schedules," Transportation, Springer, vol. 46(5), pages 1689-1712, October.

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