SeqST-ResNet: A Sequential Spatial Temporal ResNet for Task Prediction in Spatial Crowdsourcing
Type
Conference PaperKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2019-04-24Online Publication Date
2019-04-24Print Publication Date
2019Permanent link to this record
http://hdl.handle.net/10754/652903
Metadata
Show full item recordAbstract
Task appearance prediction has great potential to improve task assignment in spatial crowdsourcing platforms. The main challenge of this prediction problem is to model the spatial dependency among neighboring regions and the temporal dependency at different time scales (e.g., hourly, daily, and weekly). A recent model ST-ResNet predicts traffic flow by capturing the spatial and temporal dependencies in historical data. However, the data fragments are concatenated as one tensor fed to the deep neural networks, rather than learning the temporal dependencies in a sequential manner. We propose a novel deep learning model, called SeqST-ResNet, which well captures the temporal dependencies of historical task appearance in sequences at several time scales. We validate the effectiveness of our model via experiments on a real-world dataset. The experimental results show that our SeqST-ResNet model significantly outperforms ST-ResNet when predicting tasks at hourly intervals and also during weekday and weekends, more importantly, in regions with intensive task requests.Citation
Zhai D, Liu A, Chen S, Li Z, Zhang X (2019) SeqST-ResNet: A Sequential Spatial Temporal ResNet for Task Prediction in Spatial Crowdsourcing. Lecture Notes in Computer Science: 260–275. Available: http://dx.doi.org/10.1007/978-3-030-18576-3_16.Sponsors
This research was supported in part by National Natural Science Foundation of China (NSFC) (Grant No. 61572336, 61632016, 61572335, 61772356), and Natural Science Research Project of Jiangsu Higher Education Institution (No. 18KJA520010, 17KJA520003), and the King Abdullah University of Science and Technology (KAUST), and the Blockshine Technology corp. Data sour Didi Chuxing.Publisher
Springer NatureConference/Event name
24th International Conference on Database Systems for Advanced Applications, DASFAA 2019Additional Links
https://link.springer.com/chapter/10.1007%2F978-3-030-18576-3_16ae974a485f413a2113503eed53cd6c53
10.1007/978-3-030-18576-3_16