题 目：Using Big Data Analytics for Public Transportation Planning in Urban Economic Development（大数据与交通规划问题）
报告人：王海波教授（美国德克萨斯农工国际大学，Taxes A&M International University）
时 间：12月23（周二）下午13:00-14:30
地 点：欢迎来到公海欢迎来到赌船335会议室
欢迎相关专业教师和研究生参加！
报告人简介：
王海波，2004年密西西比大学运营管理博士毕业。目前是美国德克萨斯农工国际大学桑切斯商学院Killam基金会（冠名）杰出教授，博士生导师，兼任美国德州罗宾逊物流公司学术顾问，广州增信技术公司联合创始人及技术总监，郑州长通物流有限公司首席顾问，北京中能汇智软件技术公司首席顾问。美国高级科学技术学会，国际电子电气工程师学会，美国运筹管理学会，决策科学研究院成员。多所中国大学的访问和客座教授，多家国际刊物的客座主编与编委。获得过最佳理论论文奖，年度最佳学者，具有全球理念的研究者等等荣誉，学术论文在多个国际知名SCI/SSCI收录的刊物发表，包括European Journal of Operational Research, Journal of Intelligent and Robotic Systems, Computers and Operation Research, IEEE transactions on Control System Technology, IEEE transactions on Automation Science and Engineering, Journal of Operational Research Society, Computers and Industrial Engineering, Journal of Applied Mathematical Modeling, International Journal of Flexible Manufacturing Systems, International Journal of Production Research, Journal of Human and Ecological Risk Assessment, Journal of Heuristics, Communications in Statistics, International Journal of Information Technology and Decision Making, Journal of Combinatorial Optimization等等。
报告内容简介：
This project develops a comprehensive data aggregation and analysis system to provide the decision support for public transportation planning and collects data from different sources such as Census data in the Economic Enhancement Zones, GIS data on public transportation lines and sensor or GPS data from the operators of public transportation. The data will be aggregated on both space and time dimensions and analyzed by using “big data” models and tools. A new difference-in-difference regression and estimation model is implemented for analyzing the “big data”. The data collected from the project can be used to develop mobile application for public transportation as location based service. The improvement on public transportation planning can have benefits on creating the employment opportunities for lower income population, on helping local small business on attracting customers, on improving the accessibility to public service facilities for lower income population in the economic enhancement zones.
