Assessment of Discretionary Lane-Changing Decisions using a Random Parameters Approach with Heterogeneity in Means and Variances

Lane-changing maneuvers on highways may cause capacity drops, create shock waves, and potentially increase collision risks. Properly managing lane-changing behavior to reduce these adverse impacts requires an understanding of their determinants. This paper investigates the determinants lane changing in congested traffic using a next generation simulation dataset. A random parameters binary logit model with heterogeneity means variances was estimated account for unobserved across vehicles. Estimation results show that average headway, original vehicle, driver acceleration/deceleration behavior, vehicle size all significantly influence probabilities. It further found effect varied observations, mean this variation decreased increasing variance increased acceleration/deceleration. These empirical findings provide interesting new evidence changing, which can be used flow models better replicate predict flow.