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Sun Yuxuan received the Best Paper Award at the 103rd Transportation Research Board (TRB) Annual Meeting.

Date:2024-03-21 15:14:41 | Visits:

From January 7th to January 11th, 2024, the 103rd Annual Meeting of the Transportation Research Board (TRB) was held in Washington, D.C., USA. The TRB Annual Meeting covers a wide range of topics related to the five major modes of transportation, including highway, railway, maritime, and aviation. It encompasses various aspects such as transportation policy, traffic management, practical operations, government research, academic studies, and the latest advancements in the industry. The conference serves not only as a platform for academic exchange but also as a significant venue for innovation and talent interaction.

Global experts, researchers, policymakers, and industry representatives participate in the TRB Annual Meeting, sharing their research findings and industry trends through paper presentations, poster displays, and seminars. The proceedings published for the conference are an important reference for tracking the latest developments in the field of transportation.

At the 103rd TRB Annual Meeting, the paper "Cycle-level and Movement-level Detection of Nonrecurrent Congestion in an Urban Road Network under Spatially Sparse Trajectory Data Environment," co-authored by Professor Ma Wanjing, Associate Professor Yu Chunhui, and doctoral student Sun Yuxuan from the Magic Research Group, was awarded the Best Paper Award for 2024 by the TRB Travel Time Speed and Reliability (TTSR) Committee.

The paper focuses on the detection of nonrecurrent congestion in urban road networks, addressing the challenge of complex urban traffic flows and low penetration rates of trajectory data. It establishes an algorithm for detecting nonrecurrent congestion at the cycle level in time and the movement level in space. The algorithm takes into account the impact of signal-controlled intersections on trajectory features and the spatiotemporal correlation of congestion, ensuring high accuracy and timeliness of detection results even in environments with spatially sparse trajectory data.

Sun Yuxuan was invited to attend the TRB conference and presented their research in the form of a poster display.


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