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Mining Big Data for Big Transportation Decisions and Discoveries: E-Science of Transportation Approach and Its Supporting Platform
供稿:   日期:2017年02月24日 18:27  浏览量:[]

报 告 人:Yinhai Wang美国华盛顿大学终身教授




Transportation involves human, infrastructure, vehicle, and environmental interactions and is therefore a very complicated system. Transportation activities are found affecting public health, air quality, sustainability, etc., and thus tie to everyone’s daily life and are critical for achieving goals of Smart Cities. Traditionally, transportation has been studied through classical methods, typically with ideal assumptions, limited data support, and poor computing resources. While the theories developed through these efforts provide valuable insights in understanding transportation-related issues, they are often ineffective in large-scale transportation system analysis with massive amount of data from various sources.

With recent advances in sensing, networking, and computing technologies, more and more cities have launched their smart cities plans to improve quality of life, sustainability, efficiency, and productivity. Sensor networks are essential for the intelligence needed by smart cities and we expect many new transportation-related data and computational resources will become available in the Smart Cities environment. These new assets are likely to bring in new opportunities to understand transportation systems better and address those critical transportation issues in a faster, more accountable, and more cost-effective way. To take advantage of these big data, a new theoretical framework and its supporting platform are clearly needed to integrate the quickly growing massive amount of data, typically from numerous sources of varying spatial and temporal characteristics, into the large-scale transportation problem solving and decision making processes. Efforts along this line are likely to form up a new subject area, namely e-science of transportation, in the years to come. The speaker will share his vision and pilot research in mining big data for smart cities transportation applications and linking big data to big discoveries and smart transportation decisions through his talk.


美国华盛顿大学(西雅图)土木与环境工程系终身教授、美国联邦交通部第10区(由西北地区的华盛顿州、俄勒冈州、爱荷华州、及阿拉斯加州等四州构成)大学交通研究中心主任、华盛顿大学智能交通研究与应用实验室(STAR Lab)主任及清华大学长江学者特聘教授。主要研究方向为交通检测、e交通学与大数据应用、交通控制、交通建模、智能交通系统、交通安全、及交通仿真等。主持或参与主持了75多项研究课题,资助经费总额超过5100万美元。发表专业论文120多篇,其中67篇被SCI收录。获2003年ASCE交通工程期刊最佳论文奖及1996年日本土木工程师协会第51届年会最佳讲演奖等殊荣。在国际交通领域具有非常大的影响力。

现任电气与电子工程师协会(IEEE)智能交通系统分会理事(Board of Governors),美国土木工程师协会(ASCE)交通与发展部(Transportation and Development Institute)理事,美国科学院交通研究会(TRB)高速公路运营委员会(AHB20)委员、交通信息系统与科技委员会(ABJ50)委员、道路通行能力与服务水平委员会(AHB40)委员兼科技分会主席。王教授同时还担任交通工程(ASCE Journal of Transportation Engineering)、土木工程计算(Journal of Computing in Civil Engineering)、及智能交通系统(Journal of Intelligent Transportation Systems: Technology, Planning, and Operations)等三个SCI收录国际期刊的副主编。曾于2010-2012年任海外华人交通协会(COTA)会长。

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