A Safe Route Recommendation Method Based on Driver Characteristics from Telematics Data

Route recommendation services have been widely used due to the spread of mobile devices such as smartphones. However, conventional route recommendation services often rec-ommend difficult and unsafe routes which require skilled driving techniques because conventional services aim to recommend the shortest route considering the travel distance and time. Therefore, in this paper, we propose a safe route recommendation method. The proposed method estimates the accident rate for each road section based on driver characteristics from telematics data and recommends routes that minimize the estimated accident rate. A binary classification model is generated by machine learning from the acquired data, and the objective variable is the presence or absence of accidents. The features of the driver are input to the generated model and the accident rate is estimated for the case where that driver travels that road section. Simulation evaluation confirmed that the proposed method can recommend routes with lower accident rates than the distance-minimizing route recommendation method.