标题：An Optimization Model for Staggered Bottleneck Congestion with Heterogeneous Commuters
主讲人：Dr. Liu Yang, Department of Industrial and Systems Engineering, National University of Singapore, Singapore
The traffic congestion in the morning rush hours is mainly caused by home-to-work trips which are constrained by the working hour schemes imposed by employers. This study extends Vickrey’s bottleneck model and examines the temporal evaluation of the staggered congestion under the flexible working hour scheme, i.e., employees can choose their work start time according to their needs. Along the line of the semi-analytical approach in Liu et al. (2015), an equivalent optimization model with capacity constraints is proposed here for solving the dynamic user equilibrium of heterogeneous commuters. The proposed formula admits symmetric Hessian matrix which allows us to prove the uniqueness and the stability of long-run equilibrium. The proposed approach is used to numerically test the redistribution work trips at user equilibrium. It can be served as a useful tool for policy makers to estimate the congestion reduction after implementing the flexible work arrangement, and to guide the employers to properly stagger working hours.
Dr. Liu Yang is currently an Assistant Professor of Department of Industrial and Systems Engineering at National University of Singapore. She received her B.S. from Tsinghua University, her M.S. from Hong Kong University of Science and Technology and her Ph.D. from Northwestern University. Previously, Dr. Liu worked as a consultant at Cambridge Systematics and provided modeling expertise to public agencies such as Chicago Department of Transportation. Dr. Liu’s research covers various topics in the areas of transportation system modeling and analysis, and transportation economics. Her research interests focus in congestion pricing, travel demand modeling and management, and shared mobility service network design and operations. Her recent research is supported by Singapore Ministry of Education AcRF Tier 1.