Chenglong Liu is an associate professor at Tongji University, and a member of the Maintenance and Management Committee of the Shanghai Highway Society. His primary research focuses on the intersection of road engineering and information engineering. He has developed a lightweight detection technology for assessing riding quality, as well as a fine-grained evaluation method for high-frequency and multi-dimensional pavement performance. He has proposed a theory for road infrastructure management and maintenance optimization based on fine-grained spatio-temporal data. His research has been published in more than 50 high-level journals, including Transport Res. C, IEEE Trans. ITS, CACAIE, and China Highway Journal. Furthermore, his work has resulted in more than 20 China, the United States, the United Kingdom, and international patents, with core patents winning the China Patent Excellence Award and Shanghai Top 100 High-Value Patents. Several of his achievements have been industrialized and applied, covering more than 150,000 kilometers in over 20 provinces across the country. As the primary contributor, he has been awarded the first prize of Shanghai Science and Technology Progress Award, the first prize of Science and Technology Award of China Highway Society, the first prize of Invention and Innovation Award of China Invention Association.
Educational experience
2010.09-2014.06 Tongji University, Transportation Engineering, Bachelor
2017.09-2018.09 University of Washington, Civil and Environmental Engineering, Joint Ph.D.
2014.09-2019.06 Tongji University, Transportation Engineering, Ph.D
Work experience
2019.12~2022.12 Postdoctoral fellow, School of Transportation Engineering, Tongji University
2023.02~present Associate Professor, School of Transportation Engineering, Tongji University
Liu, C., Wu, D., Li, Y., & Du, Y. (2021). Large-scalepavement roughness measurements with vehicle crowdsourced data usingsemi-supervised learning. Transportation Research Part C: Emerging Technologies,125, 103048. (Q1, IF=9.022)，ESI (1%), ESI (0.1%)，COTA Best Presentation Award.
Liu, C., Nie, T., Du, Y., Cao, J., Wu, D., & Li, F.(2022). A Response-Type Road Anomaly Detection and Evaluation Method for SteadyDriving of Automated Vehicles. IEEE Trans. on Intelligent TransportationSystems. (Q1, IF=9.551)
Liu, C., Xu, N., Weng, Z., Li, Y., Du, Y., & Cao, J.(2022). Effective pavement skid resistance measurement using multi‐scaletextures and deep fusion network. Computer‐Aided Civil and InfrastructureEngineering. (Q1, IF=10.066)
Liu, C., Du, Y., Wong, S. C., Chang, G., & Jiang, S.(2020). Eco-based pavement lifecycle maintenance scheduling optimization forequilibrated networks. Transportation Research Part D, 86, 102471. (Q1, IF=7.041)
Li, Y., Liu, C. (Corresponding Author), Yue, G., Gao, Q., &Du, Y. (2022). Deep learning-based pavement subsurface distress detection viaground penetrating radar data. Automation in Construction, 142, 104516. (Q1, IF=10.517)
Du, Y., Liu, C. ( Corresponding Author), Song, Y., Li, Y., &Shen, Y. (2019). Rapid estimation of road friction for anti-skid autonomousdriving. IEEE transactions on intelligent transportation systems, 21(6),2461-2470. (Q1, IF=9.551)
Weng, Z., Ablat, G., Wu, D., Liu, C. (Corresponding Author), Li, F., Du, Y., & Cao, J. (2022). Rapidpavement aggregate gradation estimation based on 3D data using a multi-featurefusion network. Automation in Construction, 134, 104050. (Q1, IF=10.517)
Liu, C., Wu, D., Li, Y., Jiang, S., & Du, Y. (2022). Mathematical insights into the relationship between pavement roughness andvehicle vibration. International Journal of Pavement Engineering, 23(6),1935-1947. (Q1, IF=4.178)