Stochastic behavior of road networks: a percolation-based approach

【主讲】王俊伟，助理教授，香港大学工业及制造系统工程系

【主题】道路网络的随机行为：一种基于渗透的方法

【时间】2019年3月4日（周一）下午3:30

【地点】清华经管学院 伟伦楼453

【语言】英语

【主办】管理科学与工程系

【简历】王俊伟老师的简历

Junwei Wang received the Ph.D. degree in mechanical engineering from the University of Saskatchewan, Canada, in 2013 and the Ph.D. degree in systems engineering from the Northeastern University, China, in 2006. He is currently an Assistant Professor with the Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong. Before joining HKU, He was a JSPS Postdoctoral Fellow with the Department of systems innovation, The University of Tokyo and a full professor with the college of mechanical engineering at East Chine University of Science and Technology. He has authored 50 refereed journal papers and a book on optimization algorithms. His current research interests include modeling and optimization, service systems engineering, and resilience engineering.

【Speaker】Junwei Wang, Assistant Professor，the Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong.

【Topic】Stochastic behavior of road networks: a percolation-based approach

【Time】Monday, Mar 4, 2019, 3:30 pm

【Venue】Room 453, Weilun Building, Tsinghua SEM

【Language】English

【Organizer】Department of Management Science and Engineering

【Abstract】The road network serves as one of the most fundamental infrastructure systems for the society, while is at risk from different types of disruptions. Due to the variety and unpredictability of disruptions, the road network has stochastic behavior, which refers to the change of the state of the network. In this paper, we propose an analytical method to study the stochastic behavior of road networks impacted by disasters. The behavior of road networks is quantified by the change of network connectivity and efficiency. Two distinctive features of road networks are considered, i.e. (1) continuously degradable link capacities, and (2) various link lengths. Validation by a real-world case shows that this new approach provides detailed and accurate evaluation of the stochastic behavior of disaster-impacted road networks.