<ul><li>Accurate spatial-temporal prediction of network-based travelers' requests is crucial for the effective policy design of ridesharing platforms.</li><li>This paper introduces a novel prediction model, OD-CED, for fine-grained Origin-Destination (OD) demand prediction in ridesharing platforms.</li><li>OD-CED combines an unsupervised space coarsening technique and an encoder-decoder architecture to capture both semantic and geographic dependencies.</li><li>Experimental results show that OD-CED outperforms traditional statistical methods, achieving significant reductions in root-mean-square error and weighted mean absolute percentage error.</li></ul>

Spatio-temporal Prediction of Fine-Grained Origin-Destination Matrices with Applications in Ridesharing

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