Crash Risk Estimation Due to Lane Changing: A Data-Driven Approach Using Naturalistic Data

Abstract

Road crashes are one of the critical issues in the transportation sector. Crash studies aim to establish the relationship of crash occurrences with driver, environment and traffic factors. Lack of a large disaggregate driving or accident data constrains the study of driver factors and driving maneuvers. Moreover, the usual outcome of these studies is the crash likelihood without accounting for the impact of a potential crash, thus leading to an incomplete interpretation of crash risk. New and innovative ways of data collection, such as drone videography and floating car data, are a promising candidate for a disaggregated analysis. Our study proposes a method for estimation of rear-end crash risk during a specific traffic state, using separate formulations of likelihood and severity. We quantify rear-end crash risk by weighing the likelihood by the potential severity of a collision, wherein we introduce a severity indicator for the rear-end crash. The methodology is applied to the highD dataset, a large naturalistic traffic dataset collected on German freeways. The proposed methodology allows for the analysis of risk with traffic state and lane-changing maneuver. The findings show that speed-drop is associated with increased crash risk. Also, lane changing is associated with higher rear-end crash risk, as compared to lane-keeping, during the free-flow as well as congestion traffic. The understanding of the evolution of the crash risk, due to the driving maneuvers under different traffic conditions, can be useful for real-time crash prediction and for devising traffic management strategies.