An urban commuters' OD hybrid prediction method based on big GPS data.

Abstract

With the quick development of mobile Internet and communication technology, the use of Global Position System (GPS)-enabled devices is rapidly increasing, which facilitates the collection of huge volumes of movement data in the form of trajectories. Trajectory data contain a lot of commuters' travel information, which offer convenience for researchers to study traffic problems and to mine urban commuters' travel information. In this paper, we represent an urban commuters' origin-destination (OD) hybrid prediction method based on big GPS data, which considers the temporal and spatial dependencies of OD volume data simultaneously. The regional division was performed based on a simple grid map, and the data for each grid can be obtained. Based on the grids, the OD pairs can be constructed and the network topology of OD pairs can be established. A graph convolutional network and a long short-term memory deep learning method were introduced to capture the temporal and spatial dependencies, respectively. In addition, an attention mechanism was used to learn the weights of input data. The numerical experiment was performed based on the GPS data in Chengdu, China, and some comparisons were made. The results demonstrated that the proposed hybrid OD prediction method was significant and the accuracy was reasonable.