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Predicting risk map of traffic accidents is vital for accident prevention andearly planning of emergency response. Here, the challenge lies in themultimodal nature of urban big data. We propose a compact neural ensemble modelto alleviate overfitting in fusing multimodal features and develop some newfeatures such as fractal measure of road complexity in satellite images, taxiflows, POIs, and road width and connectivity in OpenStreetMap. The solution ismore promising in performance than the baseline methods and the single-modalitydata based solutions. After visualization from a micro view, the visualpatterns of the scenes related to high and low risk are revealed, providinglessons for future road design. From city point of view, the predicted risk mapis close to the ground truth, and can act as the base in optimizing spatialconfiguration of resources for emergency response, and alarming signs. To thebest of our knowledge, it is the first work to fuse visual and spatio-temporalfeatures in traffic accident prediction while advances to bridge the gapbetween data mining based urban computing and computer vision based urbanperception.

Risk Prediction on Traffic Accidents using a Compact Neural Model for Multimodal Information Fusion over Urban Big Data