面向有效再平衡策略的微交通小时需求预测的准确性

IF 1.4 Q4 ENGINEERING, INDUSTRIAL
Kanokporn Boonjubut, H. Hasegawa
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引用次数: 1

摘要

共享单车的供需失衡问题十分突出。因此,这些系统需要重新安置自行车以满足客户的需求。本研究的目的是提高自行车共享系统在再平衡问题上的效率。通过对预测共享单车网络中自行车需求的算法进行评估,预测共享单车需求可以提高共享单车系统在规划中使用的信息的再平衡操作过程中的效率。数据集中使用了来自三个不同数据库的历史、天气和假日数据,并采用了三种基本预测模型进行了比较。此外,统计方法包括选择变量,提高模型的准确性。本研究提出了不同人工智能技术模型预测共享单车需求的准确性。本研究的结果将有助于共享单车公司的经营者确定有关共享单车需求的数据,以规划未来。因此,这些数据有助于为管理再平衡过程创建适当的计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accuracy of Hourly Demand Forecasting of Micro Mobility for Effective Rebalancing Strategies
Abstract The imbalance in bike-sharing systems between supply and demand is significant. Therefore, these systems need to relocate bikes to meet customer needs. The objective of this research is to increase the efficiency of bike-sharing systems regarding rebalancing problems. The prediction of the demand for bike sharing can enhance the efficiency of a bike-sharing system for the operation process of rebalancing in terms of the information used in planning by proposing an evaluation of algorithms for forecasting the demand for bikes in a bike-sharing network. The historical, weather and holiday data from three distinct databases are used in the dataset and three fundamental prediction models are adopted and compared. In addition, statistical approaches are included for selecting variables that improve the accuracy of the model. This work proposes the accuracy of different models of artificial intelligence techniques to predict the demand for bike sharing. The results of this research will assist the operators of bike-sharing companies in determining data concerning the demand for bike sharing to plan for the future. Thus, these data can contribute to creating appropriate plans for managing the rebalancing process.
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来源期刊
CiteScore
4.30
自引率
13.30%
发文量
48
审稿时长
10 weeks
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