@Article{cmc.2019.06125,
AUTHOR = {Ning  Cao, Shengfang  Li, Keyong  Shen, Sheng  Bin, Gengxin  Sun, Dongjie  Zhu, Xiuli  Han, Guangsheng  Cao, Abraham  Campbell},
TITLE = {Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big Data},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {61},
YEAR = {2019},
NUMBER = {1},
PAGES = {227--241},
URL = {http://www.techscience.com/cmc/v61n1/23110},
ISSN = {1546-2226},
ABSTRACT = {Monitoring, understanding and predicting Origin-destination (OD) flows in a city is an important problem for city planning and human activity. Taxi-GPS traces, acted as one kind of typical crowd sensed data, it can be used to mine the semantics of OD flows. In this paper, we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China. The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows. Then based on a novel complex network model, a semantics mining method of OD flows is proposed through compounding Points of Interests (POI) network and public transport network to the OD flows network. The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.},
DOI = {10.32604/cmc.2019.06125}
}