Vision paper

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. CCS CONCEPTS• Computing methodologies → Neural networks; Causal reasoning and diagnostics; • Information systems → Spatialtemporal systems.

Determining Causality in Travel Mode Choice

“…Almost all these techniques are based on correlations. Introducing the concept of causality in transportation is a step forward (Xin et al, 2022) and to do so, this study introduces a novel methodology for causal modeling of travel modes. Several noteworthy observations were made during this analysis which highlights the challenges and opportunities in the application of causality in transportation modeling.…”

Causal Inference Applied to Explaining the Appearance of Shadow Phenomena in an Image

“…Then, to obtain the explanation we are looking for about an event captured in an image, we would have to consider causal relationships that can either be inferred through expert knowledge (Martin, 2018) or intervene such data sets through experimentation, as indicated in He and Geng (2008), taking into account that, in probabilistic language, not having a way to distinguish between giving value to a variable and observing it, prevents modelling cause and effect relationships (Perry, 2003). Thus, taking modelling as an essential step to achieve causal inference, Xin et al (2022) discusses the role of causal inference to improve the interpretability and robustness of machine learning methods, and highlights opportunities in the development of machine learning models with causal capacity adapted for the analysis of mobility considering images or sequential data. In the punctual case on causal inference applied to images, Lopez-Paz et al (2017) propose to use neural causality coefficients (NCCs) that are calculated by applying convolutional neural networks (CNNs) to the pixels of an image, so that the appearance of causality between variables suggests that there is a causal link between the real-world entities themselves, Lebeda et al (2015) have proposed a statistical approach -transfer entropy -to discover and quantify the relationship between camera motion and the motion of a tracked object to predict the location of the tracked object, Fire and Zhu (2013) presented a Bayesian grammar (C-AOG) model for human-perceived causal relationships that can be learned from a video, and Pickup et al (2014) use the causality method, supplemented with computer vision and machine learning techniques, to determine whether a video is playing forward or backward by observing the "arrow of time" in a temporal sequence.…”