Due to the significance of transportation planning, traffic management, and dispatch optimization, predicting passenger origin-destination has emerged as a crucial requirement for intelligent transportation systems management. In this study, we present a model designed to forecast the origin and destination of travels within a specified time window. To derive meaningful travel flows, we employ K-means clustering in a four-dimensional space with a maximum cluster size constraint for origin and destination zones. Given the large number of clusters, we utilize non-negative matrix factorization to reduce the number of travel clusters. Furthermore, we implement a stacked recurrent neural network model to predict the travel count in each cluster. A comparison of our results with existing models reveals that our proposed model achieves a 5-7\% lower mean absolute percentage error (MAPE) for 1-hour time windows and a 14\% lower MAPE for 30-minute time windows.

翻译：由于交通规划、交通管理和调度优化的重要性，预测乘客起讫点已成为智能交通系统管理的关键需求。本研究提出了一种模型，用于预测指定时间窗口内出行的起点和终点。为提取有意义的出行流，我们在四维空间中采用带有最大聚类规模约束的K-means聚类方法，对起讫点区域进行划分。针对聚类数量过多的问题，我们利用非负矩阵分解减少出行聚类的数量。此外，我们实现了一种堆叠递归神经网络模型，用于预测每个聚类中的出行数量。将我们的结果与现有模型进行比较表明，所提模型在1小时时间窗口下平均绝对百分比误差（MAPE）降低了5-7%，在30分钟时间窗口下降低了14%。