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Learning daily activity patterns with probabilistic grammars

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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.

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Acknowledgments

This research was conducted under Future (Urban) Mobility Interdisciplinary Research Group (FM IRG) of Singapore-MIT Alliance for Research and Technology (SMART), which is funded by the National Research Foundation of Singapore (NRF). The authors would like to thank the Land Transport Authority of Singapore (LTA) for providing the household travel survey data used in this study.

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  1. Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, E1 08-20, Singapore, 117576, Singapore

    Siyu Li

  2. Civil and Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, E1A 07-16, Singapore, 117576, Singapore

    Der-Horng Lee

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  1. Siyu Li
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Correspondence to Der-Horng Lee.

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Li, S., Lee, DH. Learning daily activity patterns with probabilistic grammars. Transportation 44, 49–68 (2017). https://doi.org/10.1007/s11116-015-9622-1

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