[HTML][HTML] How machine learning informs ride-hailing services: A survey
In recent years, online ride-hailing services have emerged as an important component of
urban transportation system, which not only provide significant ease for residents' travel …
urban transportation system, which not only provide significant ease for residents' travel …
[HTML][HTML] Demand management for smart transportation: A review
The current revolutions of automation, electrification, and sharing are reshaping the way we
travel, with broad implications for future mobility management. While much uncertainty …
travel, with broad implications for future mobility management. While much uncertainty …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
Predicting electric vehicle charging demand using a heterogeneous spatio-temporal graph convolutional network
Abstract Short-term Electric Vehicle (EV) charging demand prediction is an essential task in
the fields of smart grid and intelligent transportation systems, as understanding the …
the fields of smart grid and intelligent transportation systems, as understanding the …
Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach
Dynamic demand prediction is crucial for the efficient operation and management of urban
transportation systems. Extensive research has been conducted on single-mode demand …
transportation systems. Extensive research has been conducted on single-mode demand …
Coordinating ride-sourcing and public transport services with a reinforcement learning approach
Combining ride-sourcing and public transit services (with ride-sourcing service to address
the first/last-mile issues) can bring many benefits, such as saving passengers' trip fares …
the first/last-mile issues) can bring many benefits, such as saving passengers' trip fares …
Estimating intercity heavy truck mobility flows using the deep gravity framework
Accurate estimation of intercity heavy truck mobility flows is of vital importance to urban
planning, transportation management and logistics operations. The inaccessibility of big …
planning, transportation management and logistics operations. The inaccessibility of big …
Prediction of corn variety yield with attribute-missing data via graph neural network
F Yang, D Zhang, Y Zhang, Y Zhang, Y Han… - … and Electronics in …, 2023 - Elsevier
The crop variety yield prediction is widely used to select new varieties and select suitable
planting areas for them, but it still suffers from multiple grand challenges, including sparse …
planting areas for them, but it still suffers from multiple grand challenges, including sparse …
A macro–micro spatio-temporal neural network for traffic prediction
Accurate traffic prediction is crucial for planning, management and control of intelligent
transportation systems. Most state-of-the-art methods for traffic prediction effectively capture …
transportation systems. Most state-of-the-art methods for traffic prediction effectively capture …
Autostl: Automated spatio-temporal multi-task learning
Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling
multiple spatio-temporal tasks can further promote an intelligent city life by integrating their …
multiple spatio-temporal tasks can further promote an intelligent city life by integrating their …