Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network

17 October 2019

Papers citing "Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network"

5 / 5 papers shown

Title
UMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Method Peng Xie Minbo Ma Bin Wang Junbo Zhang Tianrui Li 24 0 0 08 Sep 2024
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction Xinke Jiang Dingyi Zhuang Xianghui Zhang Hao Chen Jiayuan Luo Xiaowei Gao 11 20 0 16 Jun 2023
Fairness-enhancing deep learning for ride-hailing demand prediction Yunhan Zheng Qingyi Wang Dingyi Zhuang Shenhao Wang Jinhua Zhao 31 11 0 10 Mar 2023
A Baselined Gated Attention Recurrent Network for Request Prediction in Ridesharing Jingran Shen Nikos Tziritas Georgios Theodoropoulos AI4TS 15 7 0 11 Jul 2022
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 234 1,801 0 25 Nov 2016