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A link-based flow model with turn-level queue transmission and time-varying free-flow speed for urban road networks

Wei, L., Waller, S. T., Mei, Y., Chen, P., Wang, Y. & Wang, M., Jan 2025, In: Transportation Research Part C: Emerging Technologies. 170, 104937.

Research output: Contribution to journal › Research article › Contributed › peer-review

High-precision vehicle trajectory data from an intersection in Shanghai: A unique dataset for microscopic traffic flow studies collected by drone and GNSS receiver

Zhao, J., Ma, R. & Wang, M., Dec 2024, In: Data in brief. 57, 8 p., 110864.

Research output: Contribution to journal › Research article › Contributed › peer-review

An Imputation-Enhanced Hybrid Deep Learning Approach for Traffic Volume Prediction in Urban Networks: A Case Study in Dresden

Yan, P., Li, Z., Ijaradar, J., Pape, S., Körner, M. & Wang, M., 13 Sept 2024, In: Data Science for Transportation. 6, 3, 22.

Research output: Contribution to journal › Research article › Contributed › peer-review

基于自然驾驶数据的交互轨迹基元表征与提取

Li, Z., Wang, H., Gong, J., Lü, C., Zhao, X. & Wang, M., 25 Aug 2024, In: Qi che gong cheng. 46, 8, p. 1382-1393 12 p.

Research output: Contribution to journal › Research article › Contributed › peer-review

A new computational perceived risk model for automated vehicles based on potential collision avoidance difficulty (PCAD)

He, X., Happee, R. & Wang, M., 30 Jul 2024, In: Transportation Research Part C: Emerging Technologies. 166 (2024), 31 p., 104751.

Research output: Contribution to journal › Research article › Contributed › peer-review

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