Activity-based travel demand generation using Bayesian networks

Abstract

While activity-based travel demand generation has improved over the last few decades, the behavioural richness and intuitive interpretation remain challenging. This paper argues that it is essential to understand why people travel the way they do and not only be able to predict the overall activity patterns accurately. If one cannot understand the “why?” then a model’s ability to evaluate the impact of future interventions is severely diminished. Bayesian networks (BNs) provide the ability to investigate causality and is showing value in recent literature to generate synthetic populations. This paper is novel in extending the application of BNs to daily activity tours. Results show that BNs can synthesise both activity and trip chain structures accurately. It outperforms a frequentist approach and can cater for infrequently observed activity patterns, and patterns unobserved in small sample data. It can also account for temporal variables like activity duration.