Assessing a Connected Environment’s Safety Impact During Mandatory Lane-Changing: A Block Maxima Approach

Abstract

Previous studies have mostly hypothesised or provided preliminary evidence that crash risk during mandatory lane-changing manoeuvres in a connected environment would be reduced. However, the unavailability of crash data for a connected environment makes it challenging to confirm such a hypothesis. To this end, this study has adopted an Extreme Value Theory approach to estimate and compare crash risk using traffic conflicts during mandatory lane-changing manoeuvres in the traditional and connected environments. Using the CARRS-Q advanced driving simulator, seventy-eight participants performed mandatory lane-changing manoeuvres in three randomised driving conditions: baseline (without driving aids), connected environment with perfect communication (PC), and connected environment with communication delay (CD). Driving-related factors obtained from the driving simulator data such as speeds, spacings, lag gaps, and remaining distances were used as input to a block maximum (or generalised extreme value, GEV) model. The characteristics of the estimated GEV distributions and relative crash risk were employed as an indicator of safety. Results reveal that in the connected environment driving conditions, the mandatory lane-changing crash risk is significantly reduced compared to the baseline, with the highest reduction observed in the PC condition. The crash risk is found to be higher in the CD condition compared with the PC condition. Findings of this study confirm (i) that the connected environment has the potential to reduce the crash risk substantially by assisting drivers during mandatory lane-changing manoeuvres, and (ii) the efficacy of GEV models to quantify crash risk by using conflict data when crash records are unavailable.