A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection

M **, HY Koh, Q Wen, D Zambon… - … on Pattern Analysis …, 2024 - ieeexplore.ieee.org
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) …

A survey on service route and time prediction in instant delivery: Taxonomy, progress, and prospects

H Wen, Y Lin, L Wu, X Mao, T Cai, Y Hou… - … on Knowledge and …, 2024 - ieeexplore.ieee.org
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 …

A predict-then-optimize couriers allocation framework for emergency last-mile logistics

K **a, L Lin, S Wang, H Wang, D Zhang… - Proceedings of the 29th …, 2023 - dl.acm.org
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 …

Deep learning for trajectory data management and mining: A survey and beyond

W Chen, Y Liang, Y Zhu, Y Chang, K Luo… - arxiv preprint arxiv …, 2024 - arxiv.org
Trajectory computing is a pivotal domain encompassing trajectory data management and
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

T Feng, H Yan, H Wang, W Huang, Y Han… - Proceedings of the 29th …, 2023 - dl.acm.org
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 …

A survey of route recommendations: Methods, applications, and opportunities

S Zhang, Z Luo, L Yang, F Teng, T Li - Information Fusion, 2024 - Elsevier
Nowadays, with advanced information technologies deployed citywide, large data volumes
and powerful computational resources are intelligentizing modern city development. As an …

Micro-Macro Spatial-Temporal Graph-Based Encoder-Decoder for Map-Constrained Trajectory Recovery

T Wei, Y Lin, Y Lin, S Guo, L Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
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 …

Drl4route: A deep reinforcement learning framework for pick-up and delivery route prediction

X Mao, H Wen, H Zhang, H Wan, L Wu… - Proceedings of the 29th …, 2023 - dl.acm.org
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 …

[PDF][PDF] A Prediction-and-Scheduling Framework for Efficient Order Transfer in Logistics.

W Lyu, H Wang, Y Song, Y Liu, T He, D Zhang - IJCAI, 2023 - ijcai.org
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 …

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 …