A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
A survey on service route and time prediction in instant delivery: Taxonomy, progress, and prospects
Instant delivery services, such as food delivery and package delivery, have achieved
explosive growth in recent years by providing customers with daily-life convenience. An …
explosive growth in recent years by providing customers with daily-life convenience. An …
A predict-then-optimize couriers allocation framework for emergency last-mile logistics
In recent years, emergency last-mile logistics (ELML) have played an essential role in urban
emergencies. The efficient allocation of couriers in ELML is of practical significance to …
emergencies. The efficient allocation of couriers in ELML is of practical significance to …
Deep learning for trajectory data management and mining: A survey and beyond
Trajectory computing is a pivotal domain encompassing trajectory data management and
mining, garnering widespread attention due to its crucial role in various practical …
mining, garnering widespread attention due to its crucial role in various practical …
ILRoute: A Graph-based Imitation Learning Method to Unveil Riders' Routing Strategies in Food Delivery Service
Pick-up and delivery (PD) services such as online food ordering are playing an increasingly
important role in serving people's daily demands. Accurate PD route prediction (PDRP) is …
important role in serving people's daily demands. Accurate PD route prediction (PDRP) is …
A survey of route recommendations: Methods, applications, and opportunities
Nowadays, with advanced information technologies deployed citywide, large data volumes
and powerful computational resources are intelligentizing modern city development. As an …
and powerful computational resources are intelligentizing modern city development. As an …
Micro-Macro Spatial-Temporal Graph-Based Encoder-Decoder for Map-Constrained Trajectory Recovery
Recovering intermediate missing GPS points in a sparse trajectory, while adhering to the
constraints of the road network, could offer deep insights into users' moving behaviors in …
constraints of the road network, could offer deep insights into users' moving behaviors in …
Drl4route: A deep reinforcement learning framework for pick-up and delivery route prediction
Pick-up and Delivery Route Prediction (PDRP), which aims to estimate the future service
route of a worker given his current task pool, has received rising attention in recent years …
route of a worker given his current task pool, has received rising attention in recent years …
[PDF][PDF] A Prediction-and-Scheduling Framework for Efficient Order Transfer in Logistics.
Order Transfer from the transfer center to delivery stations is an essential and expensive part
of the logistics service chain. In practice, one vehicle sends transferred orders to multiple …
of the logistics service chain. In practice, one vehicle sends transferred orders to multiple …
A deep reinforcement learning with dynamic spatio-temporal graph model for solving urban logistics delivery planning problems
Y Li, Q Guan, J Gu, X Jiang - International Journal of Digital Earth, 2024 - Taylor & Francis
The urban logistics delivery planning problems are a crucial component of urban spatial
decision analysis. Most studies typically focus on traditional urban logistics delivery planning …
decision analysis. Most studies typically focus on traditional urban logistics delivery planning …