使用数据分析优化大学校园的公共交通

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI:10.1109/DSAA.2018.00059

K. Zimmer, H. Kurban, Mark Jenne, Logan Keating, P. Maull, Mehmet M. Dalkilic

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引用次数: 7

摘要

利用GPS坐标形式的大量公交车数据(超过1亿个数据点)和自动乘客计数数据(超过100万个数据点)，我们开发了(1)一个分析和预测未来公共交通需求的系统(2)一个使用大学校园特定概念的新模型，以最大限度地提高乘客满意度。利用这些概念，我们改进了一个典型的大学公共交通服务，更具体地说，印第安纳大学校园巴士服务(IUCBS)。

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Using Data Analytics to Optimize Public Transportation on a College Campus

Using a large volume of bus data in the form of GPS coordinates (over 100 million data points) and automated passenger count data (over 1 million data points) we have developed (1) a system of analysis and prediction of future public transportation demand (2) a new model that uses concepts specific to college campuses that maximizes passenger satisfaction. Using these concepts we improve service of a model college public transportation service and more specifically the Indiana University Campus Bus Service (IUCBS).

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2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)

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