Development of an IoT based real-time traffic monitoring system for city governance

Q1 Social Sciences
Mohammed Sarrab , Supriya Pulparambil , Medhat Awadalla
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引用次数: 48

Abstract

A significant amount of research work carried out on traffic management systems, but intelligent traffic monitoring is still an active research topic due to the emerging technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). The integration of these technologies will facilitate the techniques for better decision making and achieve urban growth. However, the existing traffic prediction methods mostly dedicated to highway and urban traffic management, and limited studies focused on collector roads and closed campuses. Besides, reaching out to the public, and establishing active connections to assist them in decision-making is challenging when the users are not equipped with any smart devices. This research proposes an IoT based system model to collect, process, and store real-time traffic data for such a scenario. The objective is to provide real-time traffic updates on traffic congestion and unusual traffic incidents through roadside message units and thereby improve mobility. These early-warning messages will help citizens to save their time, especially during peak hours. Also, the system broadcasts the traffic updates from the administrative authorities. A prototype is implemented to evaluate the feasibility of the model, and the results of the experiments show good accuracy in vehicle detection and a low relative error in road occupancy estimation. The study is part of the Omani-funded research project, investigating Real-Time Feedback for Adaptive Traffic Signals.

开发基于物联网的城市交通实时监控系统
在交通管理系统方面开展了大量的研究工作,但由于物联网(IoT)和人工智能(AI)等新兴技术的发展,智能交通监控仍然是一个活跃的研究课题。这些技术的整合将促进更好的决策和实现城市增长的技术。然而,现有的交通预测方法主要针对高速公路和城市交通管理,对集散道路和封闭校园的研究较少。此外,在用户没有任何智能设备的情况下,接触公众并建立积极的联系来帮助他们决策是具有挑战性的。本研究提出了一种基于物联网的系统模型,用于采集、处理和存储实时交通数据。目的是透过路边资讯装置,提供有关交通挤塞及异常交通事故的实时最新情况,从而改善交通流动。这些预警信息将帮助市民节省时间,尤其是在高峰时段。此外,系统还广播来自管理当局的流量更新。实验结果表明,该模型具有较好的车辆检测精度和较低的道路占用估计相对误差。这项研究是阿曼资助的研究项目的一部分,研究自适应交通信号的实时反馈。
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
Global Transitions
Global Transitions Social Sciences-Development
CiteScore
18.90
自引率
0.00%
发文量
1
审稿时长
20 weeks
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