  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.
