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Qitan Shao

Publications
2
Citations
2
Publications by Year
20242025
Co-Authors:
Show All Co-Authors (7)
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Qitan Shao

Affiliation

Beijing Key Laboratory of Multimedia and Intelligent Software Technology
Beijing Institute of Artificial Intelligence
School of Information Science and Technology, Beijing University of Technology, Beijing, China

Publication Topics

Biography

Qitan Shao received the bachelor’s degree in computer science and technology from Beijing University of Technology. He is currently a Graduate Student with the Department of Informatics, Beijing University of Technology. His research interest is intelligent transportation systems.(Based on document published on 26 August 2024).
Publications
2
Citations
2
Publications by Year
20242025
Co-Authors:
Show All Co-Authors (7)
Author's Published Works

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ChatTraffic: Text-to-Traffic Generation via Diffusion Model

Chengyang Zhang;Yong Zhang;Qitan Shao;Bo Li;Yisheng Lv;Xinglin Piao;Baocai Yin

IEEE Transactions on Intelligent Transportation Systems
Year: 2025 | Volume: 26, Issue: 2 | Journal Article |
The analysis of traffic situations under abnormal conditions is one of the bottleneck issues in Intelligent Transportation Systems (ITS). Influenced by the suddenness, randomness, and uncertainty, this issue is challenging to achieve through existing deep learning methods. It needs to be assisted by traffic simulation models for analysis. However, simulation models always require extensive scene m...Show More

ChatTraffic: Text-to-Traffic Generation via Diffusion Model

Chengyang Zhang;Yong Zhang;Qitan Shao;Bo Li;Yisheng Lv;Xinglin Piao;Baocai Yin

Year: 2025 | Volume: 26, Issue: 2 | Journal Article |

BjTT: A Large-Scale Multimodal Dataset for Traffic Prediction

Chengyang Zhang;Yong Zhang;Qitan Shao;Jiangtao Feng;Bo Li;Yisheng Lv;Xinglin Piao;Baocai Yin

IEEE Transactions on Intelligent Transportation Systems
Year: 2024 | Volume: 25, Issue: 11 | Journal Article |
Cited by: Papers (2)
Traffic prediction plays a significant role in Intelligent Transportation Systems (ITS). Although many datasets have been introduced to support the study of traffic prediction, most of them only provide time-series traffic data. However, urban transportation systems are always susceptible to various factors, including unusual weather and traffic accidents. Therefore, relying solely on historical d...Show More

BjTT: A Large-Scale Multimodal Dataset for Traffic Prediction

Chengyang Zhang;Yong Zhang;Qitan Shao;Jiangtao Feng;Bo Li;Yisheng Lv;Xinglin Piao;Baocai Yin

Year: 2024 | Volume: 25, Issue: 11 | Journal Article |

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