Spatial and socioeconomic structures of geographical units produce various inter-regional
relations, which impose a direct effect on origin–destination flows. Currently, most flow …

Accurate forecasting of traffic flows remains a significant challenge owing to its complex
spatiotemporal dependencies. Although existing methods capture some spatiotemporal …

Urban online ride-hailing demand forecasting is an important component of smart city
transportation systems. An accurate online ride-hailing demand prediction model can help …

Online prediction of real-time taxi-hailing demand generally provides better real-time
decision support for passengers and taxi drivers compared with offline prediction. Current …

The randomness of passengers' travel and the blindness of empty drivers seeking
passengers can lead to a serious imbalance in the spatio-temporal distribution of taxi supply …

Accurate prediction of demand for traditional taxi and ride-hailing services is crucial for
addressing supply-demand imbalances. However, recent studies based on global adaptive …

Accurate taxi demand prediction across urban road networks is critical for optimizing taxi
operations and improving urban traffic management. Traditional approaches to this problem …

Due to the periodic and dynamic changes of traffic flow and the spatial–temporal coupling
interaction of complex road networks, traffic flow forecasting is highly challenging and rarely …

Online ride-hailing order forecasting is a very important part of the intelligent traffic dispatch
system. Accurate order forecasting can reduce the flow of invalid vehicles and improve the …

Ride-hailing demand prediction is essential in fundamental research areas such as
optimizing vehicle scheduling, improving service quality, and reducing urban traffic …