Because of transportation planning, traffic management, and dispatch optimization importance, passenger origin-destination prediction has become one of the most important requirements for intelligent transportation systems management. In this paper, we propose a model to predict the next specified time window travels' origin and destination. To extract meaningful travel flows, we use K-means clustering in four-dimensional space with maximum cluster size limitation for origin and destination zones. Because of the large number of clusters, we use non-negative matrix factorization to decrease the number of travel clusters. Also, we use a stacked recurrent neural network model to predict travel count in each cluster. Comparing our results with other existing models shows that our proposed model has 5-7% lower mean absolute percentage error (MAPE) for 1-hour time windows, and 14% lower MAPE for 30-minute time windows.

翻译：由于运输规划、交通管理和调度的优化重要性,旅客来源目的地预测已成为智能运输系统管理的最重要要求之一。 在本文中,我们提出了一个预测下一个特定时间窗口旅行的起源和目的地的模型。为了吸引有意义的旅行流动,我们使用四维空间的K- means集群,对来源地和目的地区实行最大的集群规模限制。由于集群数量众多,我们使用非负矩阵化来减少旅行集群的数量。此外,我们使用堆叠的经常性神经网络模型来预测每个集群的旅行数量。比较我们与其他现有模型的结果显示,我们提议的模型1小时窗口的绝对百分比差5-7%(MAPE),30分钟窗口的MAPE低14%。