Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis

TL;DR

This paper discusses how causal inference can enhance the interpretability and robustness of machine learning models in mobility analysis, aiming to improve AI applications in transportation systems.

Contribution

It summarizes recent advances in causal inference for mobility data and highlights research opportunities for developing causally-enabled models tailored to transportation analysis.

Findings

Causal inference improves interpretability of mobility models.

Causal methods enhance robustness against data variability.

Opportunities exist for tailored causally-enabled transportation models.

Abstract

Artificial intelligence (AI) is revolutionizing many areas of our lives, leading a new era of technological advancement. Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems. Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. However, such deep neural networks are difficult to interpret and lack robustness, which slows the deployment of these AI-powered algorithms in practice. To improve their usability, increasing research efforts have been devoted to developing interpretable and robust machine learning methods, among which the causal inference approach recently gained traction as it provides interpretable and actionable information. Moreover, most of these methods are developed for image or sequential data which do not satisfy specific requirements of mobility data analysis. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness, summarizes recent developments in using causal inference for improving the interpretability and robustness of machine learning methods, and highlights opportunities in developing causally-enabled machine learning models tailored for mobility analysis. This research direction will make AI in the transportation sector more interpretable and reliable, thus contributing to safer, more efficient, and more sustainable future transportation systems.