Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests
Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests
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DOI：
10.1007/s11067-017-9366-x
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发表时间：
2017-12-01
影响因子：
2.4
通讯作者：
Wang, Wei
中科院分区：
文献类型：
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作者：
Bao, Jie;Xu, Chengcheng;Wang, Wei
本研究的主要目的是通过结合智能卡数据和在线兴趣点（POI）来调查自行车共享出行模式和出行目的。从纽约市的自行车共享系统中收集了大规模的智能卡出行数据。从Google Places API获得每个站点周围的POI。首先应用K-means聚类分析将自行车共享站点根据其周围的POI分为五种类型。然后进行潜在狄利克雷分配（LDA）分析，以发现隐藏的自行车共享出行模式和出行目的，使用确定的站点类型和智能卡数据。比较了具有和不具有POI数据的LDA模型的性能，以确定是否应该使用POI数据。最后，讨论了所提出的方法在自行车共享规划和运营中的实际应用。对比分析的结果验证了POI数据在探索自行车共享出行模式和出行目的方面的重要性。LDA模型的结果显示，纽约市居民出行的主要目的是乘坐公共自行车就餐，其次是购物和换乘其他公共交通系统。此外，结果还表明，居住在共享自行车站点周围的人们更有可能在早高峰换乘其他通勤工具，并在下班后骑车回家。所提出的方法可以用来提供有用的指导和建议，交通机构制定战略和法规，旨在改善自行车共享系统的操作。
The primary objective of this study was to investigate the bikesharing travel patterns and trip purposes by combining smart card data and online point of interests (POIs). A large-scale smart card trip data was collected from the bikesharing system in New York City. The POIs surrounding each station were obtained from Google Places API. K-means clustering analysis was first applied to divide bikesharing stations into five types based on their surrounding POIs. The Latent Dirichlet Allocation (LDA) analysis was then conducted to discover the hidden bikesharing travel patterns and trip purposes using the identified station types and smart card data. The performance of the LDA models with and without POI data was compared to identify whether the POI data should be used. Finally, a practical application of the proposed methods in bikesharing planning and operation was discussed. The result of comparative analyses verified the importance of POI data in exploring bikesharing travel patterns and trip purposes. The results of LDA model showed that the most prevalent travel purpose in New York City is taking public bike for eating, followed by shopping and transferring to other public transit systems. In addition, the result also suggested that people living around the bikesharing stations are more likely to transfer to other commuting tools on the morning peak and ride for home after work. The proposed methods can be used to provide useful guidance and suggestions for transportation agency to develop strategies and regulation that aim at improving the operations of bikesharing systems.