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学术海报

2017年12月28日

上午
10:00

车路协同环境下的绿色智能交通

学术海报1:车路协同环境下的绿色智能交通

主讲人:郝鹏博士

讲座时间:20171228日上午10:00-10:45

讲座地点:犀浦校区X2525

主讲人简介:

郝鹏博士是美国加州大学河滨分校环境研究与技术中心助理研究教授,项目负责人。他于2008年获得清华大学土木工程系学士学位。于2013年获得美国伦斯勒理工学院土木与环境工程系的博士学位,并先后在伦斯勒理工学院与加州大学河滨分校从事博士后研究工作。

郝博士的研究领域包括基于移动感知的交通建模、自动驾驶与车路协同、车辆能耗排放、交通仿真、交通控制以及智能交通。他的研究获得了美国交通部,美国能源部,加州能源厅,加州交通厅的资助。他发表了四十余篇国际期刊论文和国际会议论文,并担任多个交通学术期刊的审稿专家。郝博士是电气电子工程师协会(IEEE)IEEE智能交通分会、美国交通运输研究会(TRB)的会员。


学术海报2: Modeling multimodal transportation network emergency evacuation considering evacuees’ cooperative behavior

主讲人:杨霞博士

讲座时间:20171228日上午10:50-11:30

讲座地点:犀浦校区X2525

主讲人简介:

Dr. Xia (Sarah) Yang is currently an Assistant Professor of Civil Engineering at SUNY Polytechnic Institute. She received her B.S. in Railway Transportation Engineering and M.S. in Traffic and Transportation Planning and Management from Central South University in China, and her Ph.D. in Transportation Engineering from Rensselaer Polytechnic Institute in US.

Dr. Xia (Sarah) Yang’s primary research interests include transportation network modeling and simulation, evacuation modeling and planning, machine learning and statistical modeling, freight demand modeling and economics, and railway timetable optimization. She worked around 10 research projects funded by NSF, USDOT, UTRC2, and World Bank during her doctoral and postdoctoral research at Rensselaer Polytechnic Institute (RPI). She was the recipient for the 2017 Franz Edelman Finalist Award for her efforts on GPS data analysis and urban freight performance evaluation in the “Off-Hours Delivery (OHD) Project in New York City”. Her PhD dissertation on evacuation modeling and planning was presented at the most distinguished conference in traffic engineering ISTTT22. She is also a reviewer for around 10 international research journals.

 

讲座简介:

Modeling emergency evacuation could help reduce losses and damages from disasters. In this paper, based on the system optimum principle, we develop a multimodal evacuation model that considers multiple transportation modes and their interactions, and captures the proper traffic dynamics including the congestion effects, the cooperative behavior of evacuees, and the capacities of the transportation system and the shelters. We further develop a Method of Successive Average (MSA)-based sequential optimization algorithm for large-scale evacuations. Both the proposed model and the solution algorithm are tested and validated through a set of numerical tests on a small network, and a detailed case study on the Lower Manhattan network. The results of the paper can provide insight on modeling flow interactions of different transportation modes and useful guidance on developing evacuation strategies to reduce the system evacuation time and losses from disasters.    

郝鹏

犀浦校区X2525