no code implementations • 31 Dec 2020 • M. H. Rahman, S. M. Rifaat, S. N. Sadeek, M. Abrar, D. Wang
Designing spatio-temporal forecasting models separately in a task-wise and city-wise manner poses a burden for the expanding transportation network companies.
no code implementations • 16 Dec 2020 • M. H. Rahman, S. M. Rifaat
To that end, a novel spatio-temporal deep learning architecture is proposed in this paper for forecasting demand and supply-demand gap in a ride-hailing system with anonymized spatial adjacency information, which integrates feature importance layer with a spatio-temporal deep learning architecture containing one-dimensional convolutional neural network (CNN) and zone-distributed independently recurrent neural network (IndRNN).