Bus driver deceleration behavior modeling at intersections using multi-source on-board sensor data

Understanding the impact of various factors on bus deceleration behavior at intersections has important implications for bus operations control, management, and safety. This paper develops a multiple linear regression model to analyze the factors influencing bus driver deceleration (a proxy of safe driving state) at intersections using data from multiple sources, including the on-board closed-circuit television (CCTV), the advanced driver assistance system (ADAS), the bus controller area network (CAN), the bus operation, and the driver profile data. We develop a comprehensive model data extraction framework and corresponding methods to effectively estimate/calculate the bus deceleration rate (dependent variable) and its influencing factors (independent variables). We explored the factors impact on bus deceleration behavior at intersections using data from a typical bus route in China. The results highlight significant factors, including driver characteristics (age), en-route and intersection approaching driving states (trip delay, turnaround time, driving direction, and approaching speed), intersection characteristics (types, the number of lanes, zebra crossing, divider, bus lanes, right turn lanes, the stop location) and traffic conditions (surrounding vehicles). Generally, drivers with younger ages (having short reaction times) and driving with psychological anticipation of complex situations (from surrounding vehicles and pedestrians or unsignalized intersections) tend to decelerate more smoothly. The agencies may enhance safe bus driving behavior by allowing enough turnaround time in timetabling, recommending intersection approaching speed, and providing tailored ADAS system alarms (rather than flooding all alerts). Also, the planning of bus infrastructures (e.g., dedicated lanes and stop locations) should be properly evaluated considering their soft contribution to safe driving behaviors at intersections. • Develop a bus driving behavioral model dataset from multi-source on-board bus sensors. • Model the bus deceleration behavior at road intersections. • Drivers with young ages and psychological anticipation of complex situations drive smoothly. • The planning of bus service infrastructures influences bus driver behaviors. • Provide tailored ADAS alarms could facilitate effective driver behavior adaptions.