Clusters of Driving Behavior From Observational Smartphone Data

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.

Lightweight Driver Behavior Identification Model with Sparse Learning on In-Vehicle CAN-BUS Sensor Data

“…The in-vehicle sensor data included parameters related to the engine (e.g., engine coolant temperature and friction torque), fuel (e.g., long-term fuel trim bank and fuel consumption), and transmission (e.g., wheel velocity and transmission oil temperature). Similarly, in other studies [ 11 , 16 , 17 ], the authors used smartphone sensor data for driver-behavior profiling and many other applications [ 9 ]. Smartphones are equipped with sensors including GPS sensors, accelerometers, magnetometers, and gyroscopes, all of which can provide information regarding speed, acceleration, rotational speed, and several other combinations of parameters used for driver profiling.…”

Toward Flexible Data Collection of Driving Behaviour

“…Several researchers are currently using mobile phones to collect and gather driving related data. As reported by (Warren et al, 2019) data collection using un-obtrusive technology such as smartphone provides a valuable alternative to study-based data collection. The percentage of 52% (11 out of 21 studies) related to studies that used the mobile phone to acquire data is based only on the use of a cell phone.…”

Driving Behaviour Style Study with a Hybrid Deep Learning Framework Based on GPS Data

“…Previous research has extensively studied the classification process, the input data analysis and the algorithms used to predict and label drivers into specific driving styles [40]. Therefore, it is hypothesized that actions of a driver in a specific category of driving can be represented and predicted due to the definitive and measurable nature of driving styles [11], such as aggressive drivers, conservative or slow drivers, inattentive drivers, drunk drivers. However, to detect unsafe drivers, for most learning and classification techniques, a pre-set 'normal' driving profile has to be defined as reference, typically, using a discrete scale with several levels [41].…”

“…These contribute to the understanding of correlations between individual demographics, road and traffic conditions, as well as safety. However, these experiments are costly and time consuming [11]. Innovative technologies and traffic data sources provide great potential to extend advanced strategies and methods in travel behaviour research.…”