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Cycling, as a green transportation mode, provides an environmentally friendly transportation choice for short-distance traveling. However, cyclists are also getting involved in fatal accidents more frequently in recent years. Thus, understanding and modeling their road behaviors is crucial in helping improving road safety laws and infrastructures. Traditionally, people understand road user behavior using either purely spatial trajectory data, or videos from fixed surveillance camera through tracking or predicting their paths. However, these data only cover limited areas and do not provide information from the cyclist's field of view. In this paper, we take advantage of geo-referenced egocentric video data collected from the handlebar cameras of cyclists to learn how to predict their behaviors. This approach is technically more challenging, because both the observer and objects in the scene might be moving, and there are strong temporal dependencies in both the behaviors of cyclists and the video scenes. We propose Cycling-Net, a novel deep learning model that tracks different types of objects in consecutive scenes and learns the relationship between the movement of these objects and the behavior of the cyclist. Experiment results on a naturalistic trip dataset show the Cycling-Net is effective in behavior prediction and outperforms a baseline model.