Household-level choice-set generation and parameter estimation in activity-based models

Abstract

Traditional Activity-based models (ABMs) treat individuals as isolated entities, limiting behavioural representation. Econometric ABMs assume agents schedule activities to maximise utility, explained through discrete choices. Using discrete choice models implies the need for calibration of maximum likelihood estimators of the parameters of utility functions. However, classical data sources like travel diaries only contain chosen alternatives, not full choice set, making parameter estimation challenging due to unobservable, and combinatorial activity spatio-temporal sequence. To address this, we propose a choice-set generation framework for household activity scheduling, to estimate significant and meaningful parameters. Our methodology adopts a Metropolis-Hastings sampling approach, and extends it to encompass parallel generation for all household agents, householdlevel choices, and time arrangements. Utilising this approach, we then estimate parameters of household-level scheduling model presented in (Rezvany et al., 2023). This approach aims to generate behaviourally sensible parameter estimates, estimated on ensemble of schedules with consistent alternatives for household members, enhancing model realism in capturing household dynamics.