Choice Set Generation in Work Destination Choice Modeling with Variational Autoencoders

Accurate destination choice modeling is vital for transportation and urban planning, influencing demand prediction, infrastructure development, and policy assessment. Conventional methods for choice set generation—both deterministic and stochastic—often face limitations such as failing to capture individual variability, requiring extensive data, and inadequately representing decision-making processes, thus compromising reliability. This study introduces a novel application of variational autoencoders (VAEs) for generating choice sets in destination choice modeling, focusing on home-based work trips in Montreal, Canada. Leveraging the deep generative capabilities of VAEs enhances the realism and diversity of choice sets, addressing implicit perception and availability of alternatives. Our methodology integrates VAE-generated choice sets with a multinomial logit model to predict destination choices. The results show significant improvements in model fit and predictive accuracy. The VAE-based model achieved a ρ 2 value of 0.5733 and an accuracy of 49.61%, outperforming other models. The importance-sampling-based model followed, with a ρ 2 of 0.3123 and an accuracy of 41.19%, while the simple random-sampling-based model achieved a ρ 2 of 0.2528 and an accuracy of 30.10%. The model considering all alternatives available performed worst, with a ρ 2 of 0.1532 and an accuracy of 10.82%. These results highlight the effectiveness of VAEs for choice set generation, leading to more reliable destination choice models. All models were evaluated using the same number of alternatives and observations, ensuring a fair comparison and demonstrating the superior performance of the VAE-based approach.