T-DesP: Destination Prediction Based on Big Trajectory Data
T-DesP: Destination Prediction Based on Big Trajectory Data
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T-DesP：基于轨迹大数据的目的地预测
DOI：
10.1109/tits.2016.2518685
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发表时间：
2016
影响因子：
8.5
通讯作者：
Yin Jian
中科院分区：
文献类型：
--
作者：
Li Xiang;Li Mengting;Gong Yue-Jiao;Zhang Xing-Lin;Yin Jian
目的地预测在基于位置的服务中非常重要，例如目标广告位置推荐。目前的方法大多是根据历史轨迹，根据已有的行程预测目的地。然而，现有的工作都没有考虑经过的位置和历史轨迹的目的地之间的影响的差异，这严重影响了预测结果的准确性，因为目的地可以指示旅行的目的。同时，历史轨迹的时间信息在目的地预测中起着重要的作用。一方面，不同时期的历史轨迹影响也不同，上周的历史轨迹比两年前的历史轨迹更能准确地反映现状。另一方面，不同时间段的历史轨迹反映了不同的交通事实和人们的移动习惯，例如，白天去餐馆，晚上去酒吧。虽然在大数据时代可以实现大量的历史轨迹，但由于道路网络分布广泛，轨迹数据稀疏，因此它仍然远远不能覆盖所有的查询轨迹。历史轨迹的时间敏感性更突出了稀疏性问题。因此，我们提出了一种新的模型T-DesP来解决上述问题。该模型由两个模块组成：轨迹学习和目的地预测。在轨迹学习模块中，提出了一种镜像吸收马尔可夫链模型来对目标进行轨迹建模。我们建立了一个转移张量来推导在特定时隙中每个位置对之间的转移概率。为了解决数据稀疏性问题，我们通过上下文感知的张量分解方法填充转移张量中的缺失值。在目的地预测模块中，由填充的转移张量推导出吸收张量，建立了目的地预测的理论模型。实验证明了T-DesP的有效性和效率。
Destination prediction is very important in location-based services such as recommendation of targeted advertising location. Most current approaches always predict destination according to existing trip based on history trajectories. However, no existing work has considered the difference between the effects of passing-by locations and the destination in history trajectories, which seriously impacts the accuracy of predicted results as the destination can indicate the purpose of traveling. Meanwhile, the temporal information of history trajectories in destination prediction plays an important role. On one hand, the history trajectories in different periods also differ in the influence, e.g., the history trajectories from last week can reflect the status quo more accurately than the history trajectories two years ago. On the other hand, the history trajectories in different time slots reflect different facts of traffic and moving habits of people, e.g., visiting a restaurant in the daytime and visiting a bar at night. Although a huge amount of history trajectories can be achieved in the era of big data, it is still far from covering all the query trajectories since a road network is widely distributed and trajectory data is sparse. The temporal sensitivity of history trajectories highlights the sparsity problem even more. Therefore, we propose a novel model T-DesP to solve the aforementioned problems. The model is comprised of two modules: trajectory learning and destination prediction. In the module of trajectory learning, a novel method called the mirror absorbing Markov chain model is proposed for modeling the trajectories for isolating the destination. We build a transition tensor to deduce the transition probability between each location pair in a particular time slot. To address the data sparsity problem, we fill the missing values in transition tensor through a context-aware tensor decomposition approach. In the module of destination prediction, an absorbing tensor is derived from the filled transition tensor, and the theoretical model is established for destination prediction. The experiments prove the effectiveness and efficiency of T-DesP.