Public transportation is essential in people’s daily life and it is crucial to understand how people move around the city. Some prior works have exploited GPS, Wi-Fi or bluetooth to collect data, in which extra sensors or devices were needed. Other works uti- lized data from smart card systems. However, some public trans- portation systems have their own smart card system and the smart card data cannot include all kinds of transportation modes, which makes it unsuitable for our study. Nowadays, each user has his/her own mobile phones and from the cellular data of mobile phone service providers, it is possible to know the uses’ transportation mode and fine-grained crowd flows. As such, given a set of cel- lular data, in this talk, I will introduce our system for public transportation mode detection, crowd density estimation, and crowd flow estimation. Note that we only have cellular data, no extra sensor data collected from users’ mobile phones.
Wen-Chih Peng received the BS and MS degrees from the National Chiao Tung University, Taiwan, in 1995 and 1997, respectively, and the Ph.D. degree in Electrical Engineering from the National Taiwan University, Taiwan, R.O.C in 2001. Currently, he is a professor at the department of Computer Science, National Chiao Tung University, Taiwan. Prior to joining the department of Computer Science and Information Engineering, National Chiao Tung University, he was mainly involved in the projects related to mobile computing, data broadcasting and network data management. Dr. Peng published some papers in several prestigious conferences, such as IEEE International Conference on Data Engineering (ICDE), IEEE International Conference on Data Mining (ICDM) and ACM Conference on Information and Knowledge Management (ACM CIKM) and prestigious journals (e.g., IEEE TKDE, IEEE TMC, IEEE TPDS). Dr. Peng has the best paper award in ACM Workshop on location-based social network 2009 and the best student paper award in IEEE International Conference on Mobile Data Management 2011. His research interests include mobile computing, network data management and data mining. He is a member of IEEE.