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.
K. Liu et al., "Modeling the Interaction Coupling of Multi-View Spatiotemporal Contexts for Destination Prediction," Proceedings of the 2018 SIAM International Conference on Data Mining (2018, San Diego, CA), pp. 171-179, Society for Industrial and Applied Mathematics Publications, May 2018.
The definitive version is available at https://doi.org/10.1137/1.9781611975321.20
2018 SIAM International Conference on Data Mining, SDM 2018 (2018: May 3-5, San Diego, CA)
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)
Article - Conference proceedings
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