Travel purpose inference with GPS trajectories, POIs, and geo-tagged social media data
Travel purpose inference with GPS trajectories, POIs, and geo-tagged social media data
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DOI：
10.1109/bigdata.2017.8258062
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Chuishi Meng;Yu Cui;Qi He;Lu Su;Jing Gao
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文献类型：
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作者：
Chuishi Meng;Yu Cui;Qi He;Lu Su;Jing Gao
在我们的日常生活中，旅行占据了重要的一部分，每天都会产生许多旅行，例如上学或购物。随着GPS集成设备的广泛采用，大量的行程可以用GPS轨迹记录。这些轨迹由地理坐标序列表示，可以帮助我们回答简单的问题，例如“你去了哪里”。然而，还有另一个重要的问题有待回答，那就是“你做了/将要做什么”，即，旅行目的推断。在实践中，人们的出行目的对于理解出行行为和估计出行需求具有重要意义。显然，仅根据轨迹推断旅行目的是非常具有挑战性的，因为GPS设备不够准确，无法精确定位所访问的地点。在本文中，我们推断个人的旅行目的相结合的知识，从异构数据源，包括轨迹，POI和社交媒体数据。所提出的动态贝叶斯网络模型捕捉了三个重要因素：行程活动的顺序属性，功能和POI流行的行程结束地区。在真实世界的数据集上进行了广泛的实验，这些数据集包含海湾地区8，361名居民的轨迹和690万条带有地理标签的推文。实验结果表明，该方法在正确推断出行目的方面具有优势。
In our daily lives, travel takes up an important part, and many trips are generated everyday, such as going to school or shopping. With the widely adoption of GPS-integrated devices, a large amount of trips can be recorded with GPS trajectories. These trajectories are represented by sequences of geo-coordinates and can help us answer simple questions such as “where did you go”. However, there is another important question awaiting to be answered, that is “what did/will you do”, i.e., the trip purpose inference. In practice, people's trip purposes are very important in understanding travel behaviors and estimating travel demands. Obviously, it is very challenging to infer trip purposes solely based on the trajectories, because the GPS devices are not accurate enough to pinpoint the venues visited. In this paper, we infer individual's trip purposes by combining the knowledge from heterogeneous data sources including trajectories, POIs and social media data. The proposed dynamic Bayesian network model captures three important factors: the sequential properties of trip activities, the functionality and POI popularity of trip end areas. Extensive experiments are conducted on real-world data sets with trajectories of 8,361 residents and the 6.9 million geo-tagged tweets in the Bay area. Experimental results demonstrate the advantages of the proposed method on correctly inferring the trip purposes.