Assessing the Influence of Managed Lane Separation Type on Lane Deviation

Managed lanes (MLs) are increasingly implemented to enhance mobility and safety on congested freeways, but researchers have rarely explored how the type of separation between MLs and general-purpose lanes (GPLs) affects lane deviation, a key factor linked to crash risk, particularly sideswipe collisions. This study examines the relationship between ML separation type and lane deviation using naturalistic driving data from the Strategic Highway Research Program 2 in Washington State. The dataset includes 5,976 trips made by 310 drivers traveling along I-5 and I-90, focusing on three separation types: buffer, median, and concrete barrier. An Analysis of Variance was conducted to determine whether there were significant differences in mean lane deviation across separation types. A random forest model was then applied to assess the importance of explanatory variables, followed by a mixed-effects multinomial model to identify factors influencing lane deviation for drivers on ML facilities. Results show that, on average, drivers tend to steer slightly away from the separator across all designs. Median-separated facilities resulted in the highest mean lane deviation compared with concrete barriers and buffer-separated MLs. Separation type, driver age, driver gender, total miles driven, vision acuity, lane width, shoulder width, driver risk-taking behavior, and the number of MLs and GPLs were all significant factors influencing lane positioning behavior at the 95% confidence level. These findings provide valuable guidance for transportation agencies when selecting separation types for ML facilities, helping them improve safety, reduce crash risk, and improve the overall safety performance of MLs.