自行车上的环境数据挖掘

Lorand Dali, D. Mladenić
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引用次数: 3

摘要

本文介绍了一种基于公共自行车系统的环境数据挖掘方法。环境信息包括天气信息和自行车站点位置,以及一天中的时间和每个站点每小时的自行车数量。应用数据挖掘方法预测给定站点在特定时间的可用自行车数量,描述空站和满站的情况,估计最常见的路径和使用模式。该方法在真实世界数据上的实验评估给出了有希望的结果,与基线方法相比,机器学习方法在预测自行车数量方面的误差显着降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BICIKELJ: Environmental data mining on the bicycle
The paper describes an approach to environmental data mining on a problem of public bicycle system. Environmental infromation including weather information and the bicycle station location is considered, as well as the time of the day and the number of bicycles at each hour at each station. Data mining methods are applied to predict the number of available bicycles at a certain time at a given station, to describe situations of empty and full stations and, to estimate the most common paths and usage patterns. The experiemntal evaluation of the proposed approach on real-world data gives promissing results, with machine learning mehtods achieving significantely lower error on predicting the number of bicycles compared to a baseline method.
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