Paper Review10 Spatio-temporal knowledge transfer for urban crowd flow prediction via deep attentive adaptation networks. Wang, Senzhang, et al. "Spatio-temporal knowledge transfer for urban crowd flow prediction via deep attentive adaptation networks." IEEE Transactions on Intelligent Transportation Systems 23.5 (2021): 4695-4705. https://ieeexplore.ieee.org/abstract/document/9352560/ [1] J. Zhang, Y. Zheng, and D. Qi, “Deep spatio-temporal residual networks for citywide crowd flows prediction,” in Proc. AAAI Conf.. 2022. 10. 31. [KDD] City Metro Network Expansion with Reinforcement Learning https://dl.acm.org/doi/10.1145/3394486.3403315 City Metro Network Expansion with Reinforcement Learning | Proceedings of the 26th ACM SIGKDD International Conference on Knowle ABSTRACT City metro network expansion, included in the transportation network design, aims to design new lines based on the existing metro network. Existing methods in the field of transportation network design either (i) .. 2022. 9. 5. [SIGSPATIAL] OSM Ski Resort Routing https://dl.acm.org/doi/10.1145/3474717.3483628 OSM Ski Resort Routing | Proceedings of the 29th International Conference on Advances in Geographic Information Systems Overall Acceptance Rate 113 of 570 submissions, 20% dl.acm.org 3D로 스키 트랙을 라우팅 해주는것 같은데 매우 흥미롭다. 2022. 9. 5. From Twitter to traffic predictor: Next-day morning traffic prediction using social media data Yao, Weiran, and Sean Qian. "From Twitter to traffic predictor: Next-day morning traffic prediction using social media data." Transportation research part C: emerging technologies 124 (2021): 102938. 본 연구는 이른아침 시간의 트위터 포스트가 아침시간의 교통 예측을 하는데 쓸모가 있음을 보이는 연구이다. 2022. 6. 2. Hierarchical Graph Convolution Network for Traffic Forecasting (AAAI-21) https://ojs.aaai.org/index.php/AAAI/article/view/16088 Hierarchical Graph Convolution Network for Traffic Forecasting | Proceedings of the AAAI Conference on Artificial Intelli ojs.aaai.org 먼저 Hierarchical graph는 threshold 이하의 거리의 노드들을 연결한 그래프에서 spectral clustering을 해서 만들었다. 2022. 1. 17. Fine-grained Urban Flow Prediction (WWW'21) https://zhangjunbo.org/pdf/2021_WWW_UrbanFlow.pdf Urban flow prediction benefits smart cities in many aspects, such as traffic management and risk assessment. However, a critical prerequisite for these benefits is having fine-grained knowledge of the city. Thus, unlike previous works that are limited to coarse-grained data, we extend the horizon of urban flow prediction to fine granularity which.. 2022. 1. 11. Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network (CIKM-20) Li, Can, et al. "Knowledge adaption for demand prediction based on multi-task memory neural network." Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020. URL: https://doi.org/10.1145/3340531.3411965 그와중에 DCRNN 이전에 GCRN이라는 논문이 있었음을 알게되었다. https://arxiv.org/abs/1707.01926 Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.. 2021. 12. 16. Discrete Graph Structure Learning for Forecasting Multiple Time Series Shang, Chao, Jie Chen, and Jinbo Bi. "Discrete Graph Structure Learning for Forecasting Multiple Time Series." arXiv preprint arXiv:2101.06861 (2021). Paper: Discrete Graph Structure Learning for Forecasting Multiple Time Series Code: https://github.com/chaoshangcs/GTS Gumbel Distribution 참고: https://data-newbie.tistory.com/263 https://blog.evjang.com/2016/11/tutorial-categorical-variational.htm.. 2021. 12. 14. DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis (AAAI-19) Lin, Ziqian, et al. "Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis." Proceedings of the AAAI conference on artificial intelligence. Vol. 33. No. 01. 2019. https://ojs.aaai.org/index.php/AAAI/article/view/3892 DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis | Proceedings of the AAAI ojs.aaai.org Regi.. 2021. 12. 9. Traffic Prediction 관련 논문/데이터셋 정리 github.com/aprbw/traffic_prediction aprbw/traffic_prediction Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries). - aprbw/traffic_prediction github.com You can find the bibtex in traffic_prediction.bib (not complete yet) modelcitationsvenuepublished datepapercodes 3D-TGCN 12 arXiv 3 Mar .. 2021. 1. 15. 이전 1 다음
