Providing routes that are passable allowing movement by transportation modes such as wheelchairs or strollers requires accessibility information including details about the type and location of barriers. Earlier research detected the types of barriers using sensor data from people in wheelchairs and able-bodied people, but the amount and range of travel of people in wheelchairs is limited. Also, small barriers are not noticed by able-bodied persons since they tend to be easily ignored during movement. In our research, the goal was to detect barrier details using sensor data from various transportation modes. However, there are issues with the selection of barrier detection models for each mode and with the cost of collecting data. To overcome this model-selection problem, we propose a model that detects transportation modes and barriers in two stages. We also propose a method for reducing the cost of collecting data, with which we prepare a course with a smooth surface, collect data, and simulate rough surfaces by adding noise. We conducted three experiments to verify the effectiveness of the proposed method. The proposed method achieved an accuracy of 91.5% in detecting six transportation modes, and showed that adding noise increased accuracy by 3.7 percentage points on rough surfaces. When detecting eight types of barriers, our method achieved an accuracy of 87.7% for walking with a stroller, and showed that adding noise increased accuracy by 6.8 percentage points on rough surfaces. Therefore, the proposed method is effective in detecting barrier details using multiple transportation modes.