Explaining Taxi Demand Prediction Models Based on Feature Importance

Eric Loff, Sören Schleibaum, Jörg P. Müller, Benjamin Säfken

Abstract: The prediction of city-wide taxi demand is used to proactively relocate idle taxis. Often neural network-based models are applied to tackle this problem, which is difficult due to its multivariate input and output space. As these models are composed of multiple layers, their predictions become opaque. This opaqueness makes debugging, optimising, and using the models difficult. To address this, we propose the usage of eXplainable AI (XAI) – feature importance methods.