A deep causal inference model for fully-interpretable travel behaviour analysis

arXiv:2405.01708v1 Announce Type: new
Abstract: Transport policy assessment often involves causal questions, yet the causal inference capabilities of traditional travel behavioural models are at best limited. We present the deep CAusal infeRence mOdel for traveL behavIour aNAlysis (CAROLINA), a framework that explicitly models causality in travel behaviour, enhances predictive accuracy, and maintains interpretability by leveraging causal inference, deep learning, and traditional discrete choice modelling. Within this framework, we introduce a Generative Counterfactual model for forecasting human behaviour by adapting the …

abstract accuracy analysis arxiv assessment capabilities causal causal inference causality cs.lg framework inference policy predictive questions transport travel type