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.Comment: 25 pages, 20 figure