Accommodating (non-)monotonically changing hazards in finite mixture models for the inter-episode duration of discretionary activities

Bibliographic Data

ID6419831
AuthorsPim Labee (Eindhoven University of Technology, corresponding author), Se‐heon Kim (0009-0003-0456-8867, Hanbat National University), Seheon Kim (0009-0001-1804-8702), Soora Rasouli (0000-0001-8044-8654, Eindhoven University of Technology)
Year2025
Volume168
Pages106480-106480
Publication date2025-09-22
Open AccessYes
TypeARTICLE
VenueCities (JOURNAL)
Journal identifiersISSN: 0264-2751 • E-ISSN: 1873-6084
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.cities.2025.106480
OpenAlexW4414398621
LanguageEN
References cited63

Understanding the dynamics behind inter-episode durations of discretionary activities can lay the groundwork for expanding activity-based models beyond single-day representation to encompass multi-day forecasting. Although the field has long recognized the variability in day-to-day activities, many advanced activity-based models still neglect the interdependencies among activities spanning multiple days. This paper examines the intervals between successive participations in grocery shopping, non-grocery shopping and leisure activities specifying different finite mixture models using three parameter Weibull and shifted-log-logistic baseline hazards. Besides accounting for unobserved heterogeneity in a non-parametric fashion, the models accommodate time-varying covariates, household-, individual-, and land use characteristics, and are estimated using over two years of GPS data collected in the Netherlands between 2019 and 2022. Restrictions during COVID-19 have been incorporated in the models as a time-varying covariate since the data collection period encompasses the pandemic period. The significant shape parameters for both the Weibull and log-logistic segments suggest increasing hazards with time, warranting the usage of more flexible models accommodating (non-)monotonically changing hazards. • Explores (non-)monotonicity in hazards for inter-episode durations for grocery shopping, non-grocery shopping and leisure activities • Includes non-parametric heterogeneity in the finite mixture model • Includes time-varying covariates and land use covariates in addition to personal and household characteristics

Baseline (sea) · Covariate · Data collection · Duration (music) · Interdependence · Mixture model · Neglect · Weibull distribution · Bayesian Methods and Mixture Models · Fuzzy Systems and Optimization · Statistical Distribution Estimation and Applications

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