Clusters of Driving Behavior from Observational Smartphone Data
This work addresses the need for cost-effective data collection in transportation safety and mobility, though it is incremental by complementing traditional methods with smartphone data.
The researchers tackled the problem of understanding driving behaviors by using smartphone sensor data from 500 drivers in San Francisco to cluster drivers and identify deviations from norms, providing insights into traffic speed and maneuvers across the city.
Understanding driving behaviors is essential for improving safety and mobility of our transportation systems. Data is usually collected via simulator-based studies or naturalistic driving studies. Those techniques allow for understanding relations between demographics, road conditions and safety. On the other hand, they are very costly and time consuming. Thanks to the ubiquity of smartphones, we have an opportunity to substantially complement more traditional data collection techniques with data extracted from phone sensors, such as GPS, accelerometer gyroscope and camera. We developed statistical models that provided insight into driver behavior in the San Francisco metro area based on tens of thousands of driver logs. We used novel data sources to support our work. We used cell phone sensor data drawn from five hundred drivers in San Francisco to understand the speed of traffic across the city as well as the maneuvers of drivers in different areas. Specifically, we clustered drivers based on their driving behavior. We looked at driver norms by street and flagged driving behaviors that deviated from the norm.