Summary of A Deep Causal Inference Model For Fully-interpretable Travel Behaviour Analysis, by Kimia Kamal and Bilal Farooq
A deep causal inference model for fully-interpretable travel behaviour analysis
by Kimia Kamal, Bilal Farooq
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents CAROLINA, a framework that models causality in travel behaviour, enhancing predictive accuracy and interpretability by combining causal inference, deep learning, and traditional discrete choice modelling. The authors introduce a Generative Counterfactual model using the Normalizing Flow method for forecasting human behaviour. Case studies demonstrate the effectiveness of the proposed models in uncovering causal relationships, predicting outcomes, and assessing policy interventions. For example, reducing pedestrian stress levels leads to a 38.5% increase in individuals experiencing shorter waiting times, while reducing travel distances in London results in a 47% increase in sustainable travel modes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to understand how people make choices about travel. They use deep learning and other techniques to build a model that can predict what people will do in different situations. The authors tested their model with real data from London and found that it worked well. They also used the model to show that certain policies, like reducing stress at pedestrian crossings or decreasing travel distances, could lead to positive outcomes, such as shorter waiting times or more sustainable travel modes. |
Keywords
» Artificial intelligence » Deep learning » Inference
