Although the number of traffic accidents occurring in Japan is decreasing, there still happen approximately 400,000 traffic accidents annually. Behind such accidents, there are frequent minor incidents (near-miss incidents) that may lead to such serious accidents. Analyzing such minor incidents is effective to reduce accidents, but the challenge is to design and deploy a method to collect and analyze such incident information. Drive recorders may be useful for such a purpose, but they cannot collect information from those vehicles without recorders. In this study, we propose the design and development of a platform that aggregates behavioral data from pedestrians and vehicle drivers using their smartphones, and automatically estimates risky traffic situations from the aggregated data. We present our preliminary result of detecting and classifying those events in a controlled environment and have achieved F-value 0.89 for four categories classification.