  Mobile digital billboards are an effective way to augment brand-awareness.
Among various such mobile billboards, taxicab rooftop devices are emerging in
the market as a brand new media. Motov is a leading company in South Korea in
the taxicab rooftop advertising market. In this work, we present a lightweight
yet accurate deep learning-based method to predict taxicabs' next locations to
better prepare for targeted advertising based on demographic information of
locations. Considering the fact that next POI recommendation datasets are
frequently sparse, we design our presented model based on neural ordinary
differential equations (NODEs), which are known to be robust to
sparse/incorrect input, with several enhancements. Our model, which we call
LightMove, has a larger prediction accuracy, a smaller number of parameters,
and/or a smaller training/inference time, when evaluating with various
datasets, in comparison with state-of-the-art models.
