Towards a Better Understanding of Public Transportation Traffic: A Case Study of the Washington, DC Metro
Towards a Better Understanding of Public Transportation Traffic: A Case Study of the Washington, DC Metro
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DOI：
10.3390/urbansci2030065
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发表时间：
2018-08
期刊：
影响因子：
2
通讯作者：
Robert Truong;Olga Gkountouna;D. Pfoser;Andreas Züfle
中科院分区：
文献类型：
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作者：
Robert Truong;Olga Gkountouna;D. Pfoser;Andreas Züfle
交通预测问题在从个人出行规划到城市规划的大量应用中至关重要。现有的工作主要集中在道路网络上的交通预测。然而，公共交通对整体人类流动性和乘客量做出了重要贡献。例如，华盛顿地铁在工作日平均有60万乘客。在这项工作中，我们解决的问题，建模，分类和预测这样的客运量在公共交通系统。我们研究的情况下，华盛顿，DC地铁探索票价卡数据，特别是乘客进出车站。为了降低数据的维数，我们应用主成分分析来提取不同站点和不同日历日的潜在特征。我们的无监督聚类结果表明，这些潜在的功能是高度区分。它们使我们能够得出不同的车站类型（住宅，商业和混合），并有效地分类和识别“未知”车站的客流。最后，我们还表明，这种分类可以应用于预测客运量在车站。通过学习一段时间内车站的潜在特征，我们能够预测接下来几个小时的流量。使用基线神经网络和两种朴素周期性方法进行的大量实验表明，使用基于潜在特征的方法时，准确性有了相当大的提高。
The problem of traffic prediction is paramount in a plethora of applications, ranging from individual trip planning to urban planning. Existing work mainly focuses on traffic prediction on road networks. Yet, public transportation contributes a significant portion to overall human mobility and passenger volume. For example, the Washington, DC metro has on average 600,000 passengers on a weekday. In this work, we address the problem of modeling, classifying and predicting such passenger volume in public transportation systems. We study the case of the Washington, DC metro exploring fare card data, and specifically passenger in- and outflow at stations. To reduce dimensionality of the data, we apply principal component analysis to extract latent features for different stations and for different calendar days. Our unsupervised clustering results demonstrate that these latent features are highly discriminative. They allow us to derive different station types (residential, commercial, and mixed) and to effectively classify and identify the passenger flow of “unknown” stations. Finally, we also show that this classification can be applied to predict the passenger volume at stations. By learning latent features of stations for some time, we are able to predict the flow for the following hours. Extensive experimentation using a baseline neural network and two naïve periodicity approaches shows the considerable accuracy improvement when using the latent feature based approach.