In this work, a learnable data-driven motion model namely Multi-Feature Fusion Recursive Neural Network (MFF-RNN) is proposed. The model yields pedestrians’ velocities by learning from the designed motion states consisting of the relative distances and velocities with neighbors, as well as individuals’ previous velocity sequences. A novel Radar-Nearest-Neighbor (Radar-NN) method is developed to determine the nearest neighbors of a pedestrian by treating him/her as a radar and detecting the surrounding environment within a limited circular receptive field. Bidirectional flow scenarios are adopted to evaluate the performance of the proposed model and the lane formation phenomenon can be successfully reproduced. The simulation results coincide with that of experiments and are superior to the work of Ma et al. in pedestrian trajectories, distributions, as well as fundamental diagrams. By calculating five evaluation metrics, it shows that the errors of our model are reduced by 34.1–79.0% compared with their work.

在这项工作中，提出了一种可学习的数据驱动运动模型，即多特征融合递归神经网络（MFF-RNN）。该模型通过从设计的运动状态（包括与邻居的相对距离和速度以及个人先前的速度序列）中学习来产生行人的速度。开发了一种新颖的雷达最近邻 (Radar-NN) 方法，通过将行人视为雷达并在有限的圆形感受野内检测周围环境来确定行人的最近邻居。采用双向流动场景来评估所提出模型的性能，并且可以成功再现车道形成现象。模拟结果与实验一致，优于 Ma 等人的工作。在行人轨迹、分布、以及基本图。通过计算五个评估指标，它表明我们模型的错误与其工作相比减少了 34.1-79.0%。