Cluster-Based Destination Prediction in Bike Sharing System

Pengcheng Dai, Changxiong Song, Huiping Lin, Pei Jia, Zhipeng Xu
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引用次数: 9

Abstract

Destination prediction not only helps to understand users' behavior, but also provides basic information for destination-related customized service. This paper studies the destination prediction in the public bike sharing system, which is now blooming in many cities as an environment friendly short-distance transportation solution. Due to the large number of bike stations (e.g. more than 800 stations of Citi Bike in New York City), the accuracy and effectiveness of destination prediction becomes a problem, where clustering algorithm is often used to reduce the number of destinations. However, grouping bike stations according to their location is not effective enough. The contribution of the paper lies in two aspects: 1) Proposes a Compound Stations Clustering method that considers not only the geographic location but also the usage pattern; 2) Provide a framework that uses feature models and corresponding labels for machine learning algorithms to predict destination for on-going trips. Experiments are conducted on real-world data sets of Citi Bike in New York City through the year of 2017 and results show that our method outperforms baselines in accuracy.
基于集群的共享单车目的地预测
目的地预测不仅可以帮助了解用户的行为,还可以为与目的地相关的定制服务提供基础信息。作为一种环境友好型的短途交通解决方案,公共自行车共享系统目前在许多城市蓬勃发展,本文对其目的地预测进行了研究。由于自行车站点数量众多(例如纽约市的Citi bike有800多个站点),目的地预测的准确性和有效性成为一个问题,通常使用聚类算法来减少目的地的数量。然而,根据位置对自行车站进行分组是不够有效的。本文的贡献主要体现在两个方面:1)提出了一种既考虑地理位置又考虑使用模式的复合站点聚类方法;2)为机器学习算法提供一个使用特征模型和相应标签来预测正在进行的旅行目的地的框架。在2017年纽约市Citi Bike的真实数据集上进行了实验,结果表明我们的方法在准确性上优于基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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