为描述交织区突发瓶颈段车辆换道决策机理，提供一种突发事件环境可采用的换道决策模型，本文基于车辆微观轨迹信息与社会力行人交通流模型，构建面向瓶颈路段的车辆协同换道决策模型，可为智能网联突发瓶颈环境提供一种换道决策方法。首先，基于突发瓶颈段车辆换道决策特征，考虑车辆类型和驾驶员类型，构建车辆等效质量模型以改进社会力模型，在此基础上，将驱使车辆换道的因素描述为自驱动力、车辆间排斥力和障碍物排斥力，构建协同换道决策模型；然后，筛选了832个有效的换道决策微观轨迹数据，并分为标定集和验证集，以加速度为指标，曼哈顿距离为目标函数，利用遗传算法对模型进行标定，基于模拟数据和实测数据验证了标定方法的有效性；最后，与主动换道决策模型在换道方向识别、换道意愿强度、模型预测误差方面进行对比验证。结果表明，本文模型换道方向识别成功率达92.6%，输出的换道意愿强度与实测数据基本吻合，预测结果的均方根百分比偏差（RMSPE）值平均降低0.825，相对误差（RE）值平均降低1.379，显著优于主动换道决策模型。研究成果可为智能网联环境瓶颈段车辆换道意图识别、突发事件下的交通管理和控制提供理论依据。

To describe the lane-changing decision mechanism of vehicles in the bottleneck section of weaving area and provide a lane-changing decision model in the emergency environment, this paper constructed a collaborative lane-changing decision model of vehicles facing bottleneck section based on the micro-trajectory information of vehicles and the human traffic flow model of social force. This model can provide a lane-changing decision method for the sudden bottleneck environment of intelligent network connection. Firstly, based on the characteristics of lane-changing decision of vehicles in the sudden bottleneck section, the vehicle equivalent mass model was constructed to improve the social force model by considering the types of vehicles and drivers. On this basis, the factors driving vehicle lane-changing were described as automatic driving force, repulsive force among vehicles and repulsive force of obstacles, and the collaborative lane-changing decision model was constructed. Then, 832 microscopic trajectory data of lane change decisions were selected and divided into calibration set and verification set. The model was calibrated using genetic algorithm with acceleration as index and Manhattan distance as objective function. The validity of the calibration method was verified based on simulated data and measured data. Finally, this model was compared with the active lane changing decision model in lane changing direction identification, lane changing intention intensity and model prediction error. The results show that the success rate of lane change direction recognition of the proposed model is 92.6%, the output lane change intention intensity is basically consistent with the measured data, and the predicted RMSPE value decreases by 0.825 on average and RE value decreases by 1.379 on average, which are significantly better than those of the active lane change decision model. The research results can provide a theoretical basis for the identification of vehicle lane change intention in the bottleneck section of intelligent network environment and traffic management and control under emergencies.