Open Access

ARTICLE

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

by Ning Cao¹, Shengfang Li¹, Keyong Shen¹, Sheng Bin³, Gengxin Sun³, Dongjie Zhu⁴, Xiuli Han⁵, Guangsheng Cao⁵, Abraham Campbell⁶

1 College of Computer Information and Engineering, Nanchang Institute of Technology, Nanchang, China.
2 College of Information Engineering, Sanming University, Sanming, China.
3 School of Data Science and Software Engineering, Qingdao University, Qingdao, China.
4 School of Computer Science and Technology, Harbin Institute of Technology, Weihai, China.
5 Public Teaching Department, Qingdao Technical College, Qingdao, China.
6 School of Computer Science, University College Dublin, Dublin, Ireland.

* Corresponding Author: Gengxin Sun. Email: .

Computers, Materials & Continua 2019, 61(1), 227-241. https://doi.org/10.32604/cmc.2019.06125

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.

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Citations