  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.
