Automated spatio-temporal synchronous modeling with multiple graphs for traffic prediction

F Li, H Yan, G Jin, Y Liu, Y Li, D Jin - Proceedings of the 31st ACM …, 2022 - dl.acm.org
Traffic prediction plays an important role in many intelligent transportation systems. Many
existing works design static neural network architecture to capture complex spatio-temporal …

A dynamic convolutional neural network based shared-bike demand forecasting model

S Qiao, N Han, J Huang, K Yue, R Mao, H Shu… - ACM Transactions on …, 2021 - dl.acm.org
Bike-sharing systems are becoming popular and generate a large volume of trajectory data.
In a bike-sharing system, users can borrow and return bikes at different stations. In …

Real‐World Wireless Network Modeling and Optimization: From Model/Data‐Driven Perspective

Y Li, S Zhang, X Ren, J Zhu, J Huang… - Chinese Journal of …, 2022 - Wiley Online Library
With the rapid development of the fifthgeneration wireless communication systems, a
profound revolution in terms of transmission capacity, energy efficiency, reliability, latency …

A data-driven spatial-temporal graph neural network for docked bike prediction

G Li, X Wang, GS Njoo, S Zhong… - 2022 IEEE 38th …, 2022 - ieeexplore.ieee.org
Docked bike systems have been widely deployed in many cities around the world. To the
service provider, predicting the demand and supply of bikes at any station is crucial to …

Learning structured components: Towards modular and interpretable multivariate time series forecasting

J Deng, X Chen, R Jiang, D Yin, Y Yang… - arXiv preprint arXiv …, 2023 - arxiv.org
Multivariate time-series (MTS) forecasting is a paramount and fundamental problem in many
real-world applications. The core issue in MTS forecasting is how to effectively model …

A multi-step forecasting model of online car-hailing demand

F Teng, J Teng, L Qiao, S Du, T Li - Information Sciences, 2022 - Elsevier
Multi-step forecasting of online car-hailing demand is necessary for the long-term planning
of traffic resources. However, accurate forecasting is very challenging, because it is difficult …

A lightweight and accurate spatial-temporal transformer for traffic forecasting

G Li, S Zhong, X Deng, L Xiang… - … on Knowledge and …, 2022 - ieeexplore.ieee.org
We study the forecasting problem for traffic with dynamic, possibly periodical, and joint
spatial-temporal dependency between regions. Given the aggregated inflow and outflow …

Efficient automated deep learning for time series forecasting

D Deng, F Karl, F Hutter, B Bischl… - Joint European Conference …, 2022 - Springer
Recent years have witnessed tremendously improved efficiency of Automated Machine
Learning (AutoML), especially Automated Deep Learning (AutoDL) systems, but recent work …

Context-aware spatial-temporal neural network for citywide crowd flow prediction via modeling long-range spatial dependency

J Feng, Y Li, Z Lin, C Rong, F Sun, D Guo… - ACM Transactions on …, 2021 - dl.acm.org
Crowd flow prediction is of great importance in a wide range of applications from urban
planning, traffic control to public safety. It aims at predicting the inflow (the traffic of crowds …

Cooperative multi-agent reinforcement learning in express system

Y Li, Y Zheng, Q Yang - Proceedings of the 29th ACM International …, 2020 - dl.acm.org
Express systems are widely deployed in many major cities. One type of important tasks in
the system is to pick up packages from customers in time. As pick-up requests come in real …