DST-TransitNet: A Dynamic Spatio-Temporal Deep Learning Model for
Scalable and Efficient Network-Wide Prediction of Station-Level Transit
Ridership

DST-TransitNet: A Dynamic Spatio-Temporal Deep Learning Model for Scalable and Efficient Network-Wide Prediction of Station-Level Transit Ridership

19 October 2024

Jiahao Wang

Amer Shalaby

ArXiv (abs)PDF HTML

Papers citing "DST-TransitNet: A Dynamic Spatio-Temporal Deep Learning Model for Scalable and Efficient Network-Wide Prediction of Station-Level Transit Ridership"

5 / 5 papers shown

Title
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting Yong Liu Tengge Hu Haoran Zhang Haixu Wu Shiyu Wang Lintao Ma Mingsheng Long AI4TS 102 547 0 10 Oct 2023
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christopher Morris Martin Ritzert Matthias Fey William L. Hamilton J. E. Lenssen Gaurav Rattan Martin Grohe GNN 194 1,643 0 04 Oct 2018
Graph Attention Networks Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio GNN 481 20,233 0 30 Oct 2017
Semi-Supervised Classification with Graph Convolutional Networks Thomas Kipf Max Welling GNN SSL 659 29,154 0 09 Sep 2016
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling Junyoung Chung Çağlar Gülçehre Kyunghyun Cho Yoshua Bengio 601 12,741 0 11 Dec 2014