Modeling the Interaction Coupling of Multi-View Spatiotemporal Contexts for Destination Prediction


Bike-Sharing Systems (BSSs) are being introduced to more and more cities recently, and therefore they have generated huge amounts of data. Mobike is a station-less BSS which is suffering from the chaotic parking problem. To solve this problem, it is necessary to predict where the bikes are going. Traditional works dealing with destination prediction mainly focus on station-based BSSs, and they merely leverages context-aware information technically. Thus it is naturally promising to investigate how to improve the destination prediction of station-less bikes by context information. To that end, in this paper, we develop a multi-view machine (MVM) method, by incorporating the context information from Point of Interest (POI) data and human mobility data into destination prediction. Specifically, we first describe three different views, namely start position, start time and destination by features extracted from POI data and human mobility data. Then, we capture the relationship between these three views' interactions and the trip's possibility by a multi-view machine. Finally, since multi-view machine contains too many parameters to be optimized, we leverage tensor factorization (TF) to reduce the computation costs. The experimental results show that the model can effectively capture the potential relationship of three views with trip's possibility and the approach is thus much more effective than traditional prediction methods for destination.

Meeting Name

2018 SIAM International Conference on Data Mining, SDM 2018 (2018: May 3-5, San Diego, CA)


Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for High Performance Computing Research


This research was partially supported by University of Missouri Research Board (UMRB) via the proposal number: 4991. This research was partially supported by the Natural Science Foundation of China (NSFC) via the grant numbers: 71701007 and 61773199.

Keywords and Phrases

Bicycles; Couplings; Forecasting; Semantics; Computation costs; Context information; Human mobility; Interaction coupling; Point of interest; Prediction methods; Sharing systems; Tensor factorization; Data mining

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2018 Society for Industrial and Applied Mathematics (SIAM) Publications, All rights reserved.

Publication Date

01 May 2018