  Predicting risk map of traffic accidents is vital for accident prevention and
early planning of emergency response. Here, the challenge lies in the
multimodal nature of urban big data. We propose a compact neural ensemble model
to alleviate overfitting in fusing multimodal features and develop some new
features such as fractal measure of road complexity in satellite images, taxi
flows, POIs, and road width and connectivity in OpenStreetMap. The solution is
more promising in performance than the baseline methods and the single-modality
data based solutions. After visualization from a micro view, the visual
patterns of the scenes related to high and low risk are revealed, providing
lessons for future road design. From city point of view, the predicted risk map
is close to the ground truth, and can act as the base in optimizing spatial
configuration of resources for emergency response, and alarming signs. To the
best of our knowledge, it is the first work to fuse visual and spatio-temporal
features in traffic accident prediction while advances to bridge the gap
between data mining based urban computing and computer vision based urban
perception.
