报告题目: Big Data Driven Human Mobility for Smart Transportation

报告时间：2019年6月4日14:30-16:00

报告地点：振声苑E301

报告人：许岩岩

报告简介: Understanding human mobility has many applications in diverse areas, including spread of diseases, city planning, traffic engineering, financial market forecasting, and nowcasting of economic well-being. In the past years, we have studied problems concerning the use of various sources of large-scale data to better inform human mobility and collective travel behavior in cities. In this presentation, I will introduce two works related to human mobility: the Electric Vehicles charging planning using mobile phone data and travel time estimation using deep learning. In the first task, we couple the urban mobility with EV charging plan to alleviate the pressure of power grid from the EV charging, using massive mobile phone data to infer the individual mobility of EVs in San Francisco Bay Area in USA. In the second task, we model the travel delay by learning features from map images using deep learning, and present an end-to-end deep learning framework to estimate the travel time in urban road networks. These results open avenues for smart transportation planning using big data and AI.

报告人简介: 许岩岩是加州大学伯克利分校城市与区域规划系博士后学者。许岩岩分别于2007年和2010年获js33333金沙线路检测学士和硕士学位，于2015年获上海交通大学模式识别与智能系统博士学位，并于2015年至2018年在麻省理工学院土木与环境工程系担任博士后助理。2017年至2018年，任劳伦斯伯克利国家实验室能源分析与环境影响部访问博士后。许岩岩的学术论文发表在Nature Energy, IJCAI, IEEE Trans. ITS, Journal of The Royal Society Interface, CEUS等期刊，是Nature energy, Data Mining and Knowledge Discovery等期刊的审稿人。研究方向包括数据挖掘、人类流动、城市计算等领域，特别强调从跨学科的角度将大量轨迹数据用于智能交通系统、智能城市、环境和能源。