This paper investigates the use of smartphones in vehicle fleets for identifying high-risk locations in a road network, before a crash may have happened. A novel method is proposed on how to use smartphone GPS and motion sensor data to automatically recognize critical car driving situations and near-misses such as emergency braking, evasion manoeuvres or sudden driving speed changes. In the area of Vienna, Austria, approximately 200 hours of driving data were collected with a dedicated smartphone app, from about 100 drivers covering more than 8,000 km. Additionally, various near-miss manoeuvres were measured on a closed test track under controlled conditions. In post-processing, this data was analysed in terms of driver-specific thresholds for critical driving situations. Results show that by using this modelling approach, critical situations can be accurately identified and geographically located with smartphones. An interface to traffic management would allow near-miss information to be used along accident data in the improvement of safety and efficiency of a traffic system. A combination of the proposed method with digital maps enables future applications for traffic and fleet managers, such as a "road safety hazard map". (C) 2016 The Authors. Published by Elsevier B.V.
