CMT-Net: A Mutual Transition Aware Framework for Taxicab Pick-ups and Drop-offs Co-Prediction

Abstract

With increasing population of modern cities, accurate estimation of regional passenger demands is critical to online taxicab services as such platforms aim at a reformation of taxicab scheduling for a more efficient order dispatching. Though great efforts have been made on passenger demand predictions, existing works still have the following shortcomings: i) they mostly performed based on uniform grid partition, which results in the imbalance of demand volumes among regions and even non-vehicle regions in such partition, ii) none of previous demand forecasting efforts have highlighted the important mutual influences between pick-ups and drop-offs, which are of great significance for taxicab scheduling. To this end, we first devise a multi-kernel based clustering to achieve a taxicab-behavior and geographic-aware sub-region partition, hence a more balanced and compact regional division is obtained. Subsequently, we emphasize the essential factors with regard to mutual transition quantification in taxicab predictions, then propose a Transfer-LSTM and an Origin-Destination-based transition matrix to respectively capture the drop-to-pick and pick-to-drop spatiotemporal transition patterns. Hence, a novel mutual-transition-aware co-prediction framework is devised by capturing complex spatiotemporal interactions between pick-ups and drop-offs. Extensive experiments on two real-world taxicab datasets demonstrate our co-prediction framework is superior to state-of-the-art methods, thus providing novel perspectives to urban human mobility understanding and transition-based taxicab scheduling.