Data-driven Bus Crowding Prediction Models Using Context-specific Features
Data-driven Bus Crowding Prediction Models Using Context-specific Features
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DOI：
10.1145/3406962
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发表时间：
2020-09
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影响因子：
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通讯作者：
Tahereh Arabghalizi;Alexandros Labrinidis
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文献类型：
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作者：
Tahereh Arabghalizi;Alexandros Labrinidis
公共交通是人们谈论“智慧城市”时首先想到的事情之一。因此，许多技术、应用程序和基础设施已经部署，将智慧城市的承诺带到公共交通中。其中大多数都集中在回答这个问题上，“我的公共汽车什么时候到达？”;几乎没有人回答这个问题，“我的下一辆公共汽车会有多满？”这也极大地影响了通勤者的生活质量。在这篇文章中，我们考虑了公交车满载问题。特别是，我们提出了两种不同的配方的问题，开发多个预测模型，并评估其准确性使用匹兹堡地区的数据。我们的预测模型始终优于基线（高达8倍）。
Public transit is one of the first things that come to mind when someone talks about “smart cities.” As a result, many technologies, applications, and infrastructure have already been deployed to bring the promise of the smart city to public transportation. Most of these have focused on answering the question, “When will my bus arrive?”; little has been done to answer the question, “How full will my next bus be?” which also dramatically affects commuters’ quality of life. In this article, we consider the bus fullness problem. In particular, we propose two different formulations of the problem, develop multiple predictive models, and evaluate their accuracy using data from the Pittsburgh region. Our predictive models consistently outperform the baselines (by up to 8 times).