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- research-articleNovember 2023
HiSTGNN: Hierarchical spatio-temporal graph neural network for weather forecasting
Information Sciences: an International Journal (ISCI), Volume 648, Issue Chttps://doi.org/10.1016/j.ins.2023.119580AbstractWeather forecasting is an attractive yet challenging task due to its significant impacts on human life and the intricate nature of atmospheric motion. Deep learning-based techniques, utilizing abundant observations, have gained popularity in ...
Highlights- A hierarchical spatio-temporal graph neural network enables weather forecasting.
- The spatio-temporal dependencies between variables across regions are captured.
- The hierarchical graph is learned from the meteorological ...
- research-articleOctober 2023
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 10Pages 9973–9984https://doi.org/10.1109/TKDE.2023.3269771Urban metro flow prediction is of great value for metro operation scheduling, passenger flow management and personal travel planning. However, the problem is challenging. First, different metro stations, e.g. transfer stations and non-transfer stations ...
- research-articleSeptember 2023
SLAFusion: Attention fusion based on SAX and LSTM for dangerous driving behavior detection
Information Sciences: an International Journal (ISCI), Volume 640, Issue Chttps://doi.org/10.1016/j.ins.2023.119063AbstractDangerous driving behaviors are the main cause of most traffic accidents, and the detection of these behaviors is one of the extremely important researches in Intelligent Transportation System (ITS). Although some recent approaches ...
- short-paperJuly 2023
Friend Ranking in Online Games via Pre-training Edge Transformers
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 2016–2020https://doi.org/10.1145/3539618.3591990Friend recall is an important way to improve Daily Active Users (DAU) in online games. The problem is to generate a proper inactive (lost) friend ranking list essentially. Traditional friend recall methods focus on rules like friend intimacy or training ...
- research-articleMay 2023
Housing rental suggestion based on e-commerce data
AbstractBecause renting a house is a low-frequency behavior of people, the housing rental suggestion issue suffers from extremely sparse data. In this paper, we propose to investigate the issue based on e-commerce data, as the two scenarios share the ...
- research-articleMay 2023
Shortening Passengers’ Travel Time: A Dynamic Metro Train Scheduling Approach Using Deep Reinforcement Learning
- Zhaoyuan Wang,
- Zheyi Pan,
- Shun Chen,
- Shenggong Ji,
- Xiuwen Yi,
- Junbo Zhang,
- Jingyuan Wang,
- Zhiguo Gong,
- Tianrui Li,
- Yu Zheng
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 5Pages 5282–5295https://doi.org/10.1109/TKDE.2022.3153385Urban metros have become the foremost public transit to modern cities, carrying millions of daily rides. As travel efficiency matters to the work productivity of the city, shortening passengers’ travel time for metros is therefore a pressing need, ...
- research-articleMay 2023
Cross-Domain Knowledge Graph Chiasmal Embedding for Multi-Domain Item-Item Recommendation
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 5Pages 4621–4633https://doi.org/10.1109/TKDE.2022.3151986Recommender system can provide users with the required information accurately and efficiently, playing a very important role in improving users’ life experience. Although knowledge graph-based recommender system can solve the sparsity and cold ...
- research-articleApril 2023
FedDSR: Daily Schedule Recommendation in a Federated Deep Reinforcement Learning Framework
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 4Pages 3912–3924https://doi.org/10.1109/TKDE.2021.3130265Daily schedule recommendation is an intelligent approach to recommend multiple suitable activity locations and activity sequences for users based on their needs in a day. In such a scenario, training the model using traditional methods requires ...
- research-articleMay 2019
Alleviating Users' Pain of Waiting: Effective Task Grouping for Online-to-Offline Food Delivery Services
Ordering take-out food (a.k.a. takeaway food) on online-to-offline (O2O) food ordering and delivery platforms is becoming a new lifestyle for people living in big cities, thanks to its great convenience. Web users and mobile device users can order take-...
- research-articleMarch 2019
A Deep Reinforcement Learning-Enabled Dynamic Redeployment System for Mobile Ambulances
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Volume 3, Issue 1Article No.: 15, Pages 1–20https://doi.org/10.1145/3314402Protecting citizens' lives from emergent accidents (e.g. traffic accidents) and diseases (e.g. heart attack) is of vital importance in urban computing. Every day many people are caught in emergent accidents or diseases and thus need ambulances to ...
- research-articleSeptember 2016
Urban sensing based on human mobility
UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous ComputingPages 1040–1051https://doi.org/10.1145/2971648.2971735Urban sensing is a foundation of urban computing, collecting data in cities through ubiquitous computing techniques, e.g. using humans as sensors. In this paper, we propose a crowd-based urban sensing framework that maximizes the coverage of collected ...
- short-paperNovember 2015
Location selection for ambulance stations: a data-driven approach
SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information SystemsArticle No.: 85, Pages 1–4https://doi.org/10.1145/2820783.2820876Emergency medical service provides a variety of services for those in need of emergency care. One of the major challenges encountered by emergency service providers is selecting the appropriate locations for ambulance stations. Prior works measure ...