Trip2Vec: a deep embedding approach for clustering and profiling taxi trip purposes
Trip2Vec: a deep embedding approach for clustering and profiling taxi trip purposes
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Trip2Vec：一种用于聚类和分析出租车行程目的的深度嵌入方法
DOI：
10.1007/s00779-018-1175-9
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发表时间：
2018-07
影响因子：
--
通讯作者：
Zhao Junfeng
中科院分区：
文献类型：
--
作者：
Chen Chao;Liao Chengwu;Xie Xuefeng;Wang Yasha;Zhao Junfeng
With the wide availability of GPS trajectory data, sustainable development on understanding travel behaviors has been achieved in recent years. But relatively less attention has been paid to uncovering the trip purposes, i.e.,whypeople make the trips. Unlike to the GPS trajectory data, the trip purposes cannot be easily and directly collected on a large scale, which necessitates the inference of trip purposes automatically. To this end, in this paper, we propose adevice-free and novelmodel called Trip2Vec, which consists of three components. In the first component, it augments the context on trip origins and destinations, respectively, by extracting the information about the nearby point of interest configurations and human activity popularity at particular time periods (i.e., activity period popularity) from two crowdsourced datasets. Such context is well-recognized as the clear clue of trip purposes. In the second component, on the top of the augmented context, a deep embedding approach is developed to get a moresemantical and discriminativecontext representation in the latent space. In the third component, we simply adopt the common clustering algorithm (i.e., K-means) to aggregate trips with similar latent representation, then conduct trip purpose interpretation based on the clustering results, followed by understanding the time-evolving tendency of trip purpose patterns (i.e., profiling) in the city-wide level. Finally, we present extensive experiment results with real-world taxi trajectory and Foursquare check-in data generated in New York City (NYC) to demonstrate the effectiveness of the proposed model, and moreover, the obtained city-wide trip purpose patterns are quite consistent with real situations.
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影响因子：
3.6
作者：
Weishan Dong;Ting Yuan;Kai Yang;Changsheng Li;Shilei Zhang
通讯作者：
Weishan Dong;Ting Yuan;Kai Yang;Changsheng Li;Shilei Zhang
CROWDDELIVER：利用出租车人群规划全市包裹递送路径
DOI：
10.1109/tits.2016.2607458
发表时间：
2017-06
影响因子：
8.5
作者：
Chen Chao;Zhang Daqing;Ma Xiaojuan;Guo Bin;Wang Leye;Wang Yasha;Sha Edwin
通讯作者：
Sha Edwin
DOI：
10.4108/icst.urb-iot.2014.257173
发表时间：
2014-10
期刊：
Proceedings of the First International Conference on IoT in Urban Space
影响因子：
--
作者：
Zack Z. Zhu;Ulf Blanke;G. Tröster
通讯作者：
Zack Z. Zhu;Ulf Blanke;G. Tröster
DOI：
10.1609/aaai.v31i1.10500
发表时间：
2017-02
期刊：
--
影响因子：
--
作者：
Shanshan Feng;G. Cong;Bo An;Yeow Meng Chee
通讯作者：
Shanshan Feng;G. Cong;Bo An;Yeow Meng Chee
DOI：
10.1109/tits.2014.2328231
发表时间：
2015-02-01
影响因子：
8.5
作者：
Zhang, Daqing;Sun, Lin;Wu, Zhaohui
通讯作者：
Wu, Zhaohui