An Extreme Value Theory approach to estimate crash risk during mandatory lane-changing in a connected environment

Examining crash risk in the highly anticipated connected environment is hindered by its novelty and the consequent scarcity of relevant data. This study proposes an Extreme Value Theory approach to examine and quantify mandatory lane-changing crash risk in the traditional and connected environments using traffic conflict techniques. The CARRS-Q advanced driving simulator was utilised to collect trajectory data of 78 participants performing mandatory lane-changing manoeuvres in three randomised driving conditions: baseline (without driving aids), connected environment with perfect communication, and connected environment with communication delay. Using the exceedance statistics theory (also known as a Peak Over Threshold approach corresponding to Generalised Pareto distribution), three separate models corresponding to each driving condition were developed. Driving-related factors obtained from the driving simulator data, such as speeds, spacings, lag gaps, and remaining distances, as well as driver demographics, were used as input variables to these models. Relative crash risk analysis and characteristics of the fitted Generalised Pareto distributions were employed as indicators of safety. The findings suggest that the connected environment significantly reduces mandatory lane-changing crash risk compared with the baseline condition, with the highest risk reduction observed in the perfect communication condition. While the crash risk of the communication delay condition is higher than that of the perfect communication condition, it is lower than the baseline condition. Furthermore, a comparison of the developed model to its counterpart (i.e., Block Maxima approach) showed the better performance of the adopted approach. The findings of this study provide insights into the positive impact of the connected environment on the safety of mandatory lane-changing manoeuvres as well as confirm the veracity of Peak Over Threshold models in estimating crash risk using traffic conflict data.