Laboratoire Ville Mobilité Transport

Biographie, parcours

Dr. Biao Yin is Research Associate at LVMT, Ecole des Ponts ParisTech (ENPC). Prior to that, he was a post-doctorate associate in Engineering Division, at New York University Abu Dhabi (NYUAD) since March  2018. He received his B.S. in Transportation from Central South University, China, in 2010, and the M.S. in System Engineering from Beijing Jiaotong University, China, in 2012. He received Ph.D. in Automation from Université de Technologie de Belfort-Montbéliard, France, in 2015. After his Ph.D., he was recruited to participate in ANR project – VITE at LVMT.

Thématiques de Recherche

Research interests include adaptive traffic signal control, connected and automated vehicles, Big Data for mobility analysis, agent-based traffic simulation, (deep-) reinforcement learning, land-use and transport integration modeling.  He has rich experience in programming for microscopic traffic simulation (MATSim, SUMO) and transport modeling (TransCAD). His research extends to the topics of traffic network optimization and urban mobility analysis.

Mots clés : Multi-agent simulation; Land-use and transport modeling; Reinforcement learning; Big-data on mobility analytics; Adaptive traffic signal control; CAVs

Thèse de doctorat

Contrôle adaptatif des feux de signalisation dans les carrefours: modélisation du système de trafic dynamique et approches de résolution (English: Adaptive traffic signal control at intersections: dynamic traffic system modeling and algorithms), à l’Université de technologie de Belfort Montbéliard (UTBM), 12. 2015, Directeur de thèse: Prof.  Abdellah EL MOUDNI.

Projets de recherche

November 2020 – present: Modeling and simulation of sustainable mobility in Île-de-France with multi-agent transport simulations (MATSim) (Chaire VINCI-ParisTech)

March 2019 – October 2020:  Mobility behavior analysis from geolocalised data and the HTS in the Paris region (Chaire Île-de-France Mobilités)

March 2018-March 2019: Joint optimization of traffic signal control and trajectory planning under a CAV environment (NYUAD)

June 2017 – February  2018:  Mobility diagnostics on territory based on mobile phone data (FUI-Géolytics)

June 2016 – May 2017: Integrated transport, land-use, environment modeling, and prospective analysis in the Paris region (ANR-VITE!)

Responsabilités éditoriales et reviewing

Paper reviewing: IEEE Transactions on Intelligent Transportation Systems, IET Intelligent Transport Systems, Transportation Research Record, European Journal of Operational Research, International Journal of Sustainable Transportation, Future Transportation, etc.

 

Liste choisie de publications et travaux

Articles dans des revues répertoriées dans les bases de données internationales

Yin B., AO Diallo, T Seregina, N Coulombel, L Liu, 2023, Evaluation of Low-Traffic Neighborhoods and Scale Effects: The Paris Case Study. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981231170130

Yin B., Leurent F., 2022, What are the multimodal patterns of individual mobility at the day level in the Paris region? A two-stage data-driven approach based on the 2018 Household Travel Survey. Transportation (2022). https://doi.org/10.1007/s11116-022-10285-w

Yin B, Menendez M, Yang K., 2021, Joint Optimization of Intersection Control and Trajectory Planning Accounting for Pedestrians in a Connected and Automated Vehicle Environment, Sustainability 2021, 13, 1135. https://doi.org/10.3390/su13031135

Yin B., Leurent F., 2021, Exploring Individual Activity-Travel Patterns Based on Geolocation Data from Mobile Phones, Transportation Research Record: Journal of the Transportation Research Board, 2021, https://doi.org/10.1177/03611981211031234

B. Yin, L. Liu, N. Coulombel, and V. Viguié, 2018, Appraising the environmental benefits of ride-sharing: the Paris region case study. Journal of Cleaner Production. 2018, doi.org/10.1016/j.jclepro.2017.12.186.

B. Yin, M. Dridi, and A. El Moudni, 2017, Recursive least-squares temporal difference learning for adaptive traffic signal control at intersection. Neural Computing and Applications. pp. 1-16, 2017. doi:10.1007/s00521-017-3066-9

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