The Use of Artificial Intelligence in the Assessment of User Routes in Shared Mobility Systems in Smart Cities

IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

Smart Cities Pub Date : 2023-08-01 DOI:10.3390/smartcities6040086

A. Kubik

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引用次数: 1

Abstract

The use of artificial intelligence in solutions used in smart cities is becoming more and more popular. An example of the use of machine learning is the improvement of the management of shared mobility systems in terms of assessing the accuracy of user journeys. Due to the fact that vehicle-sharing systems are appearing in increasing numbers in city centers and outskirts, and the way vehicles are used is not controlled by operators in real mode, there is a need to fill this research gap. The article presents a built machine learning model, which is a supplement to existing research and is updated with new data from the existing system. The developed model is used to determine and assess the accuracy of trips made by users of shared mobility systems. In addition, an application was also created showing an example of using the model in practice. The aim of the article is therefore to indicate the possibility of correct identification of journeys with vehicles from shared mobility systems. Studies have shown that the prediction efficiency of the data generated by the model reached the level of 95% agreement. In addition, the research results indicate that it is possible to automate the process of evaluating journeys made in shared mobility systems. The application of the model in practice will facilitate management and, above all, it is open to further updates. The use of many machine learning models will allow solving many problems that will occur in an increasing number of smart cities.

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人工智能在智慧城市共享出行系统中用户路径评估中的应用

在智慧城市中使用的解决方案中使用人工智能正变得越来越流行。使用机器学习的一个例子是在评估用户旅程的准确性方面改进共享移动系统的管理。由于车辆共享系统在城市中心和郊区越来越多地出现，而车辆在真实模式下的使用方式并不是由操作员控制的，因此需要填补这一研究空白。本文提出了一个构建的机器学习模型，该模型是对现有研究的补充，并使用来自现有系统的新数据进行更新。所开发的模型用于确定和评估共享出行系统用户出行的准确性。此外，还创建了一个应用程序，展示了在实践中使用该模型的示例。因此，本文的目的是指出正确识别来自共享移动系统的车辆的旅程的可能性。研究表明，该模型生成的数据预测效率达到95%的一致性水平。此外，研究结果表明，在共享移动系统中评估旅程的过程是可能实现自动化的。该模型在实践中的应用将有助于管理，最重要的是，它可以进一步更新。许多机器学习模型的使用将允许解决越来越多的智慧城市中出现的许多问题。

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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来源期刊

Smart Cities Multiple-

CiteScore

11.20

自引率

6.20%

发文量

审稿时长

11 weeks

期刊介绍： Smart Cities (ISSN 2624-6511) provides an advanced forum for the dissemination of information on the science and technology of smart cities, publishing reviews, regular research papers (articles) and communications in all areas of research concerning smart cities. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible, with no restriction on the maximum length of the papers published so that all experimental results can be reproduced.