城市共享单车使用需求预测及数量建议

Yifeng Cui, Weifeng Lv, Qing Wang, Bowen Du
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引用次数: 7

摘要

城市共享单车的兴起和广泛使用给居民的日常出行带来了极大的便利。然而,随着城市中共享单车数量的不断增加,越来越多的共享单车被不合理地分配,这对公共交通和城市面貌产生了很大的影响。为了解决这一问题,本文以地铁站附近的共享单车数量推荐为研究重点,以地铁站出站客流量作为潜在需求进行研究。在此基础上,首先对自动收费站(AFC)采集的地铁交通数据及相关特征数据进行收集和研究,作为需求预测的基础数据源。其次,在这些数据的基础上,我们基于先进的Xgboost方法和滑动窗口的思想建立了一种新的客流预测模型。结合预测结果,设计了一种方法,将客流结果转化为使用需求指数,并以此为特定地铁站推荐合适的共享单车数量。最后,我们用各种真实的数据进行了一系列的实验,结果表明我们的模型可以达到更好的性能,并给出了合理的数量推荐。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Usage Demand Forecast and Quantity Recommendation for Urban Shared Bicycles
The rise and wide use of urban shared bicycles brings great convenience to residents' daily trips. However, as the number of shared bicycles continues to increase in the city, more and more shared bicycles are being allocated unreasonably, which has a great impact on the public transportation and city appearance. To solve this problem, in this paper, we focus on the recommendation of the number of shared bicycles near the subway station and take the volume of the station's outbound passenger flow as the potential demand to study. Along this line, we first collect and investigate the subway traffic data which are gathered by automated fare collection(AFC) systems and some related feature data as the basic data source for the demand prediction. Second, upon these data, we develop a novel passenger flow forecast model based on an advanced Xgboost method and the idea of sliding window. Combined with the predicted results, we then design a method to transform the passenger flow results into an usage demand index and recommend a suitable number of shared bicycles for a specific subway station by it. Finally, we conduct a series of experiments with a variety of real-world data, and the results demonstrate our model can achieve a better performance and give us a reasonable quantity recommendation.
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