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 …
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 …
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 …
Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
Short-term passenger demand forecasting is of great importance to the on-demand ride
service platform, which can incentivize vacant cars moving from over-supply regions to over …
service platform, which can incentivize vacant cars moving from over-supply regions to over …
Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting
Region-level demand forecasting is an essential task in ridehailing services. Accurate ride-
hailing demand forecasting can guide vehicle dispatching, improve vehicle utilization …
hailing demand forecasting can guide vehicle dispatching, improve vehicle utilization …
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 …
A spatiotemporal attention mechanism-based model for multi-step citywide passenger demand prediction
Y Zhou, J Li, H Chen, Y Wu, J Wu, L Chen - Information Sciences, 2020 - Elsevier
In taxi dispatch systems, predicting citywide passenger pickup/dropoff demand is
indispensable for developing effective taxi distribution and scheduling strategies to resolve …
indispensable for developing effective taxi distribution and scheduling strategies to resolve …
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 …
Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network
Region-level passenger demand prediction plays an important role in the coordination of
travel demand and supply in the urban public transportation system. The complex urban …
travel demand and supply in the urban public transportation system. The complex urban …
Deep multi-view graph-based network for citywide ride-hailing demand prediction
Urban ride-hailing demand prediction is a crucial but challenging task for intelligent
transportation system construction. Predictable ride-hailing demand can facilitate more …
transportation system construction. Predictable ride-hailing demand can facilitate more …