Forecasting Public Transit Use by Crowdsensing and Semantic Trajectory Mining: Case Studies
Forecasting Public Transit Use by Crowdsensing and Semantic Trajectory Mining: Case Studies
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DOI：
10.3390/ijgi5100180
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发表时间：
2016-09
期刊：
影响因子：
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通讯作者：
Ningyu Zhang;Huajun Chen;Xi Chen;Jiaoyan Chen
中科院分区：
文献类型：
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作者：
Ningyu Zhang;Huajun Chen;Xi Chen;Jiaoyan Chen
随着智慧城市的不断发展，公共交通预测开始引起人们的高度重视。在本文中，我们提出了一种方法来预测乘客的登机选择和公共交通客流。我们的预测模型基于挖掘语义轨迹的常见用户行为，并使用地理和天气数据中的知识丰富功能。所有的实验数据均来自南通岭通客车有限公司和阿里巴巴平台，该平台也向公众开放。我们评估我们的方法使用各种数据源，包括兴趣点（POI），天气状况，并在广州的公共巴士信息，以证明其有效性。实验结果表明，我们的建议表现出更好的乘客上车选择和公共交通客流预测基线。
With the growing development of smart cities, public transit forecasting has begun to attract significant attention. In this paper, we propose an approach for forecasting passenger boarding choices and public transit passenger flow. Our prediction model is based on mining common user behaviors for semantic trajectories and enriching features using knowledge from geographic and weather data. All the experimental data comes from the Ridge Nantong Limited bus company and Alibaba platform which is also open to the public. We evaluate our approach using various data sources, including point of interest (POI), weather condition, and public bus information in Guangzhou to demonstrate its effectiveness. Experimental results show that our proposal performs better than baselines in the prediction of passenger boarding choices and public transit passenger flow.