为提高传统车辆在无信号斑马线路段对行人过街模式的预测准确度，提出一种融合极端梯度提升树（extreme gradient boosting, XGBoost）与多层感知机（multilayer perceptron, MLP）算法的行人过街模式预测模型。首先，基于安装于路侧的摄像机和激光雷达采集无信号斑马线路段行人-车辆交互数据，对行人和车辆行为特性进行分析，进而筛选出影响行人过街模式的因素；其次，探究不同组合作为模型输入时的预测效果；最终，将车辆速度、车辆到斑马线距离、碰撞时间（time to collision, TTC）和行人步速作为模型输入，将行人过街模式分为直接过街和等待过街，并作为模型输出，建立用于行人过街模式预测的XGBoost-MLP模型。该模型对行人过街模式的预测准确率达88.65%，相比单一XGBoost模型和MLP模型，其准确率分别提高了3.85%和2.61%。

In order to improve the prediction accuracy of pedestrian crossing patterns by conventional vehicles in unsignalized crosswalk road sections, a pedestrian crossing pattern prediction model integrating extreme gradient boosting (XGBoost) and multilayer perceptron (MLP) algorithms was proposed. First, the pedestrian-vehicle interaction data in the unsignalized crosswalk section were collected based on the cameras and LiDAR installed on the roadside, and the behavioral characteristics of pedestrians and vehicles were analyzed, and then the factors affecting the pedestrian crossing patterns were screened. Next, the predictive effects of different combinations when used as model inputs were explored. Finally, vehicle speed, vehicle-to-zebra crossing distance, time to collision(TTC) and pedestrian step speed were used as model inputs, and pedestrian crossing patterns were categorized into direct crossing and waiting crossing and used as model outputs, and the XGBoost-MLP model for pedestrian crossing pattern prediction was established. The prediction accuracy of this model for pedestrian crossing patterns reaches 88.65%, which compares with the single XGBoost model and the MLP model, and its accuracy is improved by 3.85% and 2.61% compared to the single XGBoost model and MLP model, respectively.