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

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.