主讲人：Yan Huang
时间：2014 年 7 月 10 日（周四）下午 2:00-3:00
地点：软件楼 105 IBM 会议室
联系人：王晓阳（xywangCS@fudan.edu.cn）
Abstract:
Urban traffic gridlock is a familiar scene. At the same time, the mean occupancy rate of personal vehicle trips in the United States is only 1.6 persons per vehicle mile. Ridesharing has the potential to solve many environmental, congestion, pollution, and energy problems. In this paper, we introduce the problem of large scale real-time ridesharing with service guarantee on road networks. Trip requests are dynamically matched to vehicles while trip waiting time and service time constraints are satisfied. We first propose two scheduling algorithms: a branch-and-bound algorithm and an integer programing algorithm. However, these algorithms do not adapt well to the dynamic nature of the ridesharing problem. Thus, we propose kinetic tree algorithms which are better suited to efficient scheduling of dynamic requests and adjust routes on-the-fly. We perform experiments on a real large Shanghai taxi dataset. Results show that the kinetic tree algorithms outperform other algorithms significantly.
We will also briefly introduce a few other projects at our lab.
Bio:
Yan Huang received her B.S. degree in Computer Science from Peking University, Beijing, China, 1997 and Ph.D. degree in, Computer Science from University of Minnesota, USA, 2003. She is currently a professor at the Computer Science and Engineering department of University of North Texas, Denton, TX, USA. Her research interests include spatio-temporal databases and mining, smart transportation, geo-stream data processing, and location-based social networks. She has been a visiting scholar of May – August 2011. During Fall 2011, she visited Fudan University, China. Currently, she is on the Board of Directors of The SSTD Endowment (2014-2019), is the General Chair of ACM SIGSPATIAL 2014, and Conference Finances Coordinator of ACM SIGSPATIAL (2010-2014). She received Distinguished Service Award from ACM SIGSpatial in 2010. Her research has been/is supported by Texas Advanced Research Program (ARP), Oak Ridge National Lab, National Science Foundation, Texas Department of Transportation, and U.S. Department of Defense.
