In this paper, we propose a novel dynamic spatio-temporal multi-scale representation method DSTMR to predict bus ridership.
In this paper, we propose a novel dynamic spatio-temporal multi- scale representation method DSTMR to predict bus ridership. Specifically, DSTMR consists of ...
Multi-Scale. Conference Paper. Dynamic Spatio-Temporal Multi-Scale Representation for Bus Ridership Prediction. June 2023. DOI:10.1109/IJCNN54540.2023.10191107.
Apr 28, 2024 · Results In response to this gap, this paper proposes the MEG-PPIS approach, a PPIS prediction method based on multi-scale graph information and ...
Missing: Bus | Show results with:Bus
This compilation focuses on spatio-temporal prediction papers. Currently, we've collected papers from venues such as KDD, ICML, NeurIPS, ICLR, AAAI, WWW, ...
In order to assess their spatio–temporal influences, a temporal resolution of 30 min complements the spatial link level. Ridership data for trams and buses is ...
Missing: Scale Representation
We identify non-Euclidean correlations among intersections in ridesplitting demand forecasting and encode them using multiple geographical and semantic graphs.
Missing: Ridership | Show results with:Ridership
Dynamic Spatial-Temporal Representation Learning for Traffic ...
dl.acm.org › doi › TITS.2020.3002718
To address this problem, we propose a unified neural network called Attentive Traffic Flow Machine (ATFM), which can effectively learn the spatial-temporal ...
We introduce an innovative Graph Neural Network frame- work for the task of forecasting bus ridership in scenarios where data distribution is highly imbalanced.
This is the repository for the collection of Graph Neural Network for Traffic Forecasting. If you find this repository helpful, you may consider cite our ...