Relationships between Crash Involvement and Temporal-Spatial Driving Behavior Activity Patterns

Abstract

Knowledge about characteristics of crash-involved drivers and their driving performance can enhance countermeasure programs designed to improve safety on roadways. However, few studies have been able to analyze detailed exposure data of individual drivers (travel by time of day and by roadway type) alongside driving performance data (speed, throttle, braking, and acceleration). Thus, researchers have been forced to base safety analyses on aggregate crash experience rather than on more frequent individual driving activities and driving performance parameters. This research evaluated driving exposure and performance differences between drivers who were involved in crashes versus drivers who were not involved in crashes during a 14-month study period, on the basis of data for vehicles with Global Positioning Systems. The objective was to verify whether the exposure of crash-involved drivers and driving performance were significantly different on the basis of disaggregated analyses by facility types and trip start times. Researchers found that the mileage exposure, speed, and acceleration patterns of drivers who were involved in crashes during the study period were significantly different from those of drivers who were not involved in crashes. Further, crash-involved drivers accumulated more mileage, consistently traveled at higher speeds, and engaged more frequently in hard deceleration events than their no-crash counterparts did. Although this study does not definitively correlate driver exposure and performance measures with crash causation, it does suggest that there is real potential to identify at-risk drivers on the basis of data obtained from vehicle-monitoring technologies. The identification and immediate correction of at-risk behaviors could obviate crash experience. If this research is successful, the surrogate measures also would reduce safety analysts' dependency on crash data.

Exposure as Duration and Distance in Telematics Motor Insurance Using Generalized Additive Models

“…These models are in some way subjective or based on generally accepted ideas. Although Jun et al (2007) were able to show the potential of GPS-based data and Ayuso et al (2017) review some ratemaking possibilities using real data, not many complete and advanced statistical studies on the topic are available, except for Verbelen et al (2017) and Henckaerts et al (2017).…”

Telematics and Gender Discrimination: Some Usage-Based Evidence on Whether Men’s Risk of Accidents Differs from Women’s

“…Speed is normally considered in terms of violation of the limits; time is normally established in terms of day/nighttime driving (with nights being more expensive); and location distinguishes between urban and non-urban driving (with the former being more expensive). All these factors have been shown to account for the risk of accidents [5][6][7][8][9][10][11].…”

Driver behaviour profiles for road safety analysis

“…The weights for acceleration (w acc c ) and braking (w brake c ) magnitudes were derived from thresholds identified in the literature (Bagdadi and Várhelyi, 2011;including Dingus et al, 2006;Jun et al, 2007) to be associated with crashes or near crashes. Specifically, drivers with higher frequencies of acceleration and braking events greater than approximately 3 m/s 2 are involved in statistically significantly higher rates of crash involvement (Jun et al, 2007).…”