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 of lane changing in congested traffic using a next generation simulation dataset. A random parameters binary logit model with heterogeneity in means and variances was estimated to account for unobserved heterogeneity in lane-changing behavior across vehicles. Estimation results show that average headway, the original lane of the vehicle, driver acceleration/deceleration behavior, and vehicle size all significantly influence lane-changing probabilities. It was further found that the effect of vehicle size varied significantly across observations, that the mean of this variation decreased with increasing average headway, and the variance increased with increasing driver acceleration/deceleration. These empirical findings provide interesting new evidence on the determinants of lane changing, which can be used in traffic flow models to better replicate and predict traffic flow.

高速公路上的变道演习可能会导致通行能力下降，产生冲击波并可能增加碰撞风险。正确管理变道行为以减少这些不利影响需要了解其决定因素。本文使用下一代仿真数据集，研究了拥挤交通中车道变化的决定因素。估计均值和方差均具有异质性的随机参数二进制logit模型，以解决跨车辆变道行为中未观察到的异质性。估计结果表明，平均时距，车辆的原始车道，驾驶员的加减速行为和车辆尺寸均显着影响换道概率。进一步发现，车辆尺寸的影响在各个观察结果中差异很大，该变化的平均值随平均行驶距离的增加而减小，而方差随驾驶员加速/减速的增加而增大。这些经验发现为车道变化的决定因素提供了有趣的新证据，可将其用于交通流模型中，以更好地复制和预测交通流。