Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling
Ride-hailing applications are becoming more and more popular for providing drivers and
passengers with convenient ride services, especially in metropolises like Beijing or New …
passengers with convenient ride services, especially in metropolises like Beijing or New …
A multi-task matrix factorized graph neural network for co-prediction of zone-based and OD-based ride-hailing demand
Ride-hailing service has witnessed a dramatic growth over the past decade but meanwhile
raised various challenging issues, one of which is how to provide a timely and accurate …
raised various challenging issues, one of which is how to provide a timely and accurate …
Spatio-temporal graph convolutional and recurrent networks for citywide passenger demand prediction
Online ride-sharing platforms have become a critical part of the urban transportation system.
Accurately recommending hotspots to drivers in such platforms is essential to help drivers …
Accurately recommending hotspots to drivers in such platforms is essential to help drivers …
A GAN framework-based dynamic multi-graph convolutional network for origin–destination-based ride-hailing demand prediction
Z Huang, W Zhang, D Wang, Y Yin - Information Sciences, 2022 - Elsevier
Ride-hailing demand prediction plays an important role in ride-hailing vehicle scheduling,
traffic condition control and intelligent transportation system construction. Accurate and real …
traffic condition control and intelligent transportation system construction. Accurate and real …
Predicting multi-step citywide passenger demands using attention-based neural networks
Predicting passenger pickup/dropoff demands based on historical mobility trips has been of
great importance towards better vehicle distribution for the emerging mobility-on-demand …
great importance towards better vehicle distribution for the emerging mobility-on-demand …
Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network
With the rapid development of mobile-internet technologies, on-demand ride-sourcing
services have become increasingly popular and largely reshaped the way people travel …
services have become increasingly popular and largely reshaped the way people travel …
DNEAT: A novel dynamic node-edge attention network for origin-destination demand prediction
The ride-hailing service platforms have grown tremendously around the world and attracted
a wide range of research interests. A key to ride-hailing service platforms is how to realize …
a wide range of research interests. A key to ride-hailing service platforms is how to realize …
Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing
services. However, predicting passenger demand over multiple time horizons is generally …
services. However, predicting passenger demand over multiple time horizons is generally …
Coupled layer-wise graph convolution for transportation demand prediction
Abstract Graph Convolutional Network (GCN) has been widely applied in transportation
demand prediction due to its excellent ability to capture non-Euclidean spatial dependence …
demand prediction due to its excellent ability to capture non-Euclidean spatial dependence …
Passenger demand forecasting with multi-task convolutional recurrent neural networks
Accurate prediction of passenger demands for taxis is vital for reducing the waiting time of
passengers and drivers in large cities as we move towards smart transportation systems …
passengers and drivers in large cities as we move towards smart transportation systems …