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利用模式控制算法,在具有不同点线路连通性的智慧城市中进行定向运输。

Directive transportation in smart cities with line connectivity at distinctive points using mode control algorithm.

作者信息

Selvarajan Shitharth, Manoharan Hariprasath, Khadidos Alaa O, Khadidos Adil O, Hasanin Tawfiq

机构信息

School of Built Environment, Engineering and Computing, Leeds Beckett University, LS1 3HE, Leeds, UK.

Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai, India.

出版信息

Sci Rep. 2024 Aug 2;14(1):17938. doi: 10.1038/s41598-024-68121-3.

Abstract

This article examines the operational functionality of intelligent transport systems to enhance smart cities by reducing traffic congestion. Given the increasing populations of smart cities, there is a growing demand for public transit systems to address the issue of traffic congestion. Therefore, the suggested system is developed using a few parametric design models, which combine point-to-point protocol and mode control optimization. The multi-objective parametric design for a smart transportation system is conducted using min-max functions to minimize the waiting time period for end users. Furthermore, customers are given the option to utilize a line following mechanism that offers suitable connectivity, along with independent identification and revitalize functions. The predicted model effectively eliminates the delay produced by transportation devices when positioning units are involved, ensuring that individual messages are delivered without any interruptions. In order to evaluate the results of the proposed system model, four different scenarios were examined. A comparison analysis revealed that the suggested method achieves a suitable directional flow for 96% of smart transport units. Additionally, it reduces delays and waiting periods by 2% and 6% respectively, while increasing energy consumption by 29%.

摘要

本文探讨智能交通系统的运行功能,通过减少交通拥堵来提升智慧城市。鉴于智慧城市人口不断增加,对公共交通系统解决交通拥堵问题的需求也日益增长。因此,所建议的系统是使用一些参数设计模型开发的,这些模型结合了点对点协议和模式控制优化。智能交通系统的多目标参数设计使用最小-最大函数进行,以尽量减少终端用户的等待时间。此外,客户可以选择使用提供合适连接以及独立识别和恢复功能的循线机制。当涉及定位单元时,预测模型有效地消除了运输设备产生的延迟,确保单个消息能够无中断地传递。为了评估所提出的系统模型的结果,研究了四种不同的场景。比较分析表明,所建议的方法为96%的智能运输单元实现了合适的定向流。此外,它分别将延迟和等待时间减少了2%和6%,同时能源消耗增加了29%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6289/11297257/6c46395ef398/41598_2024_68121_Fig1_HTML.jpg

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