Predicting passenger origin-destination in online taxi-hailing systems
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Because of transportation planning, traffic management and dispatch optimization importance, the 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 origin and destination of travels which will occur in the next specified time window. In order to extract meaningful travel flows we use K-means clustering in four-dimensional space with maximum cluster size limitation for origin and destination. Because of large number of clusters, we use non-negative matrix factorization to decrease the number of travel clusters. We also use a stacked recurrent neural network model to predict travels count in each cluster. Comparing our results with other existing models show that our proposed model has 5-7 and 14
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