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

• URL: http://arxiv.org/abs/2405.01708v1
• Date: Thu, 2 May 2024 20:06:06 GMT
• Title: A deep causal inference model for fully-interpretable travel behaviour analysis
• Authors: Kimia Kamal, Bilal Farooq,
• Abstract summary: We present the deep CAusal infeRence mOdel for traveL behavIour aNAlysis (CAROLINA), a framework that explicitly models causality in travel behaviour. Within this framework, we introduce a Generative Counterfactual model for forecasting human behaviour. We demonstrate the effectiveness of our proposed models in uncovering causal relationships, prediction accuracy, and assessing policy interventions.
• Score: 4.378407481656902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Normalizing Flow method. Through the case studies of virtual reality-based pedestrian crossing behaviour, revealed preference travel behaviour from London, and synthetic data, we demonstrate the effectiveness of our proposed models in uncovering causal relationships, prediction accuracy, and assessing policy interventions. Our results show that intervention mechanisms that can reduce pedestrian stress levels lead to a 38.5% increase in individuals experiencing shorter waiting times. Reducing the travel distances in London results in a 47% increase in sustainable travel modes.

Related papers

• Counterfactual Generative Modeling with Variational Causal Inference [1.9287470458589586]
  We present a novel variational Bayesian causal inference framework to handle counterfactual generative modeling tasks. In experiments, we demonstrate the advantage of our framework compared to state-of-the-art models in counterfactual generative modeling.
  arXiv  Detail & Related papers  (2024-10-16T16:44:12Z)
• MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction [24.3907895281179]
  This paper presents a novel deep learning model designed for counterfactual post-crash traffic prediction. The proposed model is treatment-aware, with a specific focus on comprehending and predicting traffic speed under hypothetical crash intervention strategies. The model is validated using both synthetic and real-world data, demonstrating that MSCT outperforms state-of-the-art models in multi-step-ahead prediction performance.
  arXiv  Detail & Related papers  (2024-07-19T06:42:41Z)
• Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach [54.429396802848224]
  This paper proposes an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases. For interpretability, the model achieves the target-driven motion prediction by estimating the spatial distribution of long-term destinations. Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable.
  arXiv  Detail & Related papers  (2024-03-10T04:16:04Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Interpretable Imitation Learning with Dynamic Causal Relations [65.18456572421702]
  We propose to expose captured knowledge in the form of a directed acyclic causal graph. We also design this causal discovery process to be state-dependent, enabling it to model the dynamics in latent causal graphs. The proposed framework is composed of three parts: a dynamic causal discovery module, a causality encoding module, and a prediction module, and is trained in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-09-30T20:59:42Z)
• Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study [4.705182901389292]
  We investigate the usefulness of a novel cognitively plausible model for predicting human behavior in gap acceptance scenarios. We show that this model can compete with or even outperform well-established data-driven prediction models.
  arXiv  Detail & Related papers  (2023-05-24T14:27:00Z)
• Spatio-temporal neural structural causal models for bike flow prediction [2.991894112851257]
  The fundamental issue of managing bike-sharing systems is bike flow prediction. Recent methods over-emphasize the contextual conditions on the transportation system. We propose a Spatiotemporal-temporal Structure Causal Model.
  arXiv  Detail & Related papers  (2023-01-19T01:39:21Z)
• Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities [80.37857025201036]
  Key challenge for robotic systems is to figure out the behavior of another agent. Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally. We propose equipping robots with the necessary tools to conduct observational studies on people.
  arXiv  Detail & Related papers  (2022-01-27T22:15:56Z)
• Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models [3.4806267677524896]
  This study introduces an important missing dimension - computational fairness - to travel behavior analysis. We first operationalize computational fairness by equality of opportunity, then differentiate between the bias inherent in data and the bias introduced by modeling.
  arXiv  Detail & Related papers  (2021-09-25T19:02:23Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
  We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
  arXiv  Detail & Related papers  (2020-06-08T19:24:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.