Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big Data

作     者：Ning Cao Shengfang Li Keyong Shen Sheng Bin Gengxin Sun Dongjie Zhu Xiuli Han Guangsheng Cao Abraham Campbell 

作者机构：College of Computer Information and EngineeringNanchang Institute of TechnologyNanchangChina College of Information EngineeringSanming UniversitySanmingChina School of Data Science and Software EngineeringQingdao UniversityQingdaoChina School of Computer Science and TechnologyHarbin Institute of TechnologyWeihaiChina Public Teaching DepartmentQingdao Technical CollegeQingdaoChina School of Computer ScienceUniversity College DublinDublinIreland 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2019年第61卷第7期

页      面：227-241页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

基　　金：This work is supported by Shandong Provincial Natural Science Foundation,China under Grant No.ZR2017MG011 This work is also supported by Key Research and Development Program in Shandong Provincial(2017GGX90103) 

主　　题：Origin-destination(OD)flows semantics analytics complex network big data analysis 

摘      要：Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human ***-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD *** this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in *** spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD *** 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 *** propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.