DESTPRE: a data-driven approach to destination prediction for taxi rides
DESTPRE: a data-driven approach to destination prediction for taxi rides
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DOI：
10.1145/2971648.2971664
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发表时间：
2016-09
期刊：
影响因子：
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通讯作者：
Mengwen Xu;Dong Wang-;Jian Li
中科院分区：
文献类型：
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作者：
Mengwen Xu;Dong Wang-;Jian Li
随着移动的设备的广泛使用，预测移动车辆的目的地已经成为基于位置的推荐系统和基于目的地的广告的日益重要的问题。大多数现有的方法是基于各种马尔可夫链模型，其中历史轨迹用于训练模型，并返回前k个最可能的目的地。我们确定以前的方法的某些限制。相反，我们提出了一个新的数据驱动框架，称为DestPre，它不是基于概率模型，而是直接对轨迹进行操作并进行预测。我们只使用历史轨迹，而不使用个人身份信息。我们设计的DestPre，虽然简单，是从真实的轨迹数据的几个有用的意见的结果。DestPre涉及基于Bucket PR四叉树和Minwise哈希的索引，用于有效地检索相似的轨迹，以及对目的地进行聚类以进行预测。通过结合一些额外的想法，我们表明，预测精度可以进一步提高。我们在真实的北京出租车数据集上进行了大量的实验。实验结果验证了DestPre的有效性。
With the wide use of mobile devices, predicting the destination of moving vehicles has become an increasingly important problem for location based recommendation systems and destination-based advertising. Most existing approaches are based on various Markov chain models, in which the historical trajectories are used to train the model and the top-k most probable destinations are returned. We identify certain limitations of the previous approaches. Instead, we propose a new data-driven framework, called DestPre, which is not based on a probabilistic model, but directly operates on the trajectories and makes the prediction. We make use of only historic trajectories, without individual identity information. Our design of DestPre, although simple, is a result of several useful observations from the real trajectory data. DestPre involves an index based on Bucket PR Quadtree and Minwise hashing, for efficiently retrieving similar trajectories, and a clustering on destinations for predictions. By incorporating some additional ideas, we show that the prediction accuracy can be further improved. We have conducted extensive experiments on real Beijing Taxi dataset. The experimental results demonstrate the effectiveness of DestPre.