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Segmented Trajectory Clustering-Based Destination Prediction in IoVs
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DOI：10.1109/ACCESS.2020.2998063.png)
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Abstract
En 中文
Location-based services have important applications in IoVs, and especially the destination-related applications have attracted more and more attention. Due to privacy consideration or operation convenience, people hesitate to share destinations to the public. Thus, these applications need to predict the destinations of moving vehicles in order to provide better services. Some existing works on destination prediction suffer from the dataset sparsity problem or the model inaccuracy problem. To overcome these problems, a Segmented Trajectory Clustering-Based Destination Prediction mechanism is proposed in this paper. First, each original trajectory is segmented to several key sub-trajectories, with the DP-based trajectory segmentation algorithm. Then, all the sub-trajectories are clustered based on the average nearest point pair distance to reveal the common characteristics or similar tracks. Finally, a deep neural network-based model is utilized to predict destinations, according to the history trajectories. Extensive simulations are conducted for destination predictions. Simulation results show that our proposed method can predict destinations with acceptable average errors and outperform other methods in most of the cases.
Keywords:
Trajectory
Predictive models
Hidden Markov models
Prediction algorithms
Clustering algorithms
Machine learning
Markov processes
Location-based service
destination prediction
trajectory segmentation
trajectory clustering
deep learning
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