Rezvany, N., Hillel, T., and Bierlaire, M. (2024)

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

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 the utility functions. However, classical data sources like travel diaries only contain chosen alternatives, not the 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 algorithm for household activity scheduling, to estimate significant and meaningful parameters. Using a Metropolis-Hastings sampling approach, we sample an ensemble containing clusters of schedules for all agents in a household. Alternatives for all household agents are generated in parallel, encompassing household-level choices, and time arrangements. Utilising this approach, we then estimate the parameters of a household-level scheduling model presented in Rezvany et al. 2023. This approach aims to generate behaviourally sensible parameter estimates, enhancing the model realism in capturing household dynamics.