Clusters of Driving Behavior from Observational Smartphone Data

TL;DR

This paper leverages smartphone sensor data to identify and cluster driving behaviors in San Francisco, providing insights into traffic patterns and driver norms without costly traditional studies.

Contribution

It introduces a novel approach using smartphone sensor data to cluster driver behaviors and detect deviations from norms, enhancing transportation safety analysis.

Findings

Identified distinct driver behavior clusters in San Francisco.

Mapped traffic speed and maneuvers across different city areas.

Flagged behaviors deviating from typical driver norms.

Abstract

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.